2026 AGENDA
Boston, 29 - 30 September 2026
Schedule
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Sep 299:10Conference pass
Introductory remarks
Keynotes
Anna Abiola, Conference Director, Terrapinn Holdings Ltd
Sep 299:15Conference pass
Chair's remarks
Keynotes
Emily Gillen, Head of Global Data Partnerships, UCB
Sep 299:20Conference pass
Advancing the development of individualised genetic therapies
Keynotes
Sep 299:40Conference pass
Investment trends in AI
Keynotes
Moderator: Rana Lonnen, General Partner, Science Capital Ventures
Issa Kildani, Managing Partner, Founder, Ambrosia Ventures
Pooja Majmudar, Investment Partner, KELES Digital Health
Sarah Johnson, Partner, Advantary Capital Partners
Sep 2910:40Conference pass
Roundtables
RoundtablesAgentic AI in Drug Discovery
Stephanie Oestreich, Managing Director, Myeloma Investment FundAI adoption in small-scale cell & gene therapy biomanufacturing: opportunities, challenges, and practical pathways
Danyel Evseev, Process Development Lead, Riddell Centre for Cancer Immunotherapy, University of CalgaryAI-Driven Futures: Roadmap to Digital Transformation
Shabana Motlani, Director, USO PS QA & GxP Automation & Analytics, Novo NordiskAI‑Ready Drug Discovery: What It Actually Takes
Building the Intelligent Health Ecosystem: Where Pharma Meets AI Powered by Real-World Data
Qingchu Jin, Faculty Scientist I, MaineHealth Institute for ResearchFrom Bench to Bedside: Smarter Pre-Clinical Strategies to De-Risk Early-stage Technologies
From Complexity to Clarity: Single-Cell, Spatial, and Multi-Omics
Isha Parikh, Bioinformatician, Mount SinaiInvestment Trends in 2026: AI is everywhere, where Capital Is Actually Going (and Why)? Real investor perspectives + live founder feedback
Invite Only: Building the Future of AI & Data Strategy in Life Sciences
Shabana Motlani, Director, USO PS QA & GxP Automation & Analytics, Novo NordiskMichael Liebman, Managing Director, IPQ Analytics, LLCLei Xie, Professor, Northeastern UniversityShihan He, Machine Learning Engineer, Novo Nordisk Inc.Hong Truong, Principal, Define VenturesNeil Pfister, Assistant Professor; Head of AI in Precision Medicine Research Group, University of Alabama at BirminghamYi-Hsiang Hsu, Director and associate Professor, Broad Institute of MIT and HarvardJake Chen, Endowed Professor and Director, University of Alabama at BirminghamSamir Hanash, Director, Red And Charline Mccombs Institute For The Early Detection And Treatment Of Cancer, MD Anderson Cancer CenterHuda Eldosougi, Chief Technical Advisor, Saudi Food and Drug AuthorityKaruna Kantor, Director, Novo Nordisk
Sep 2910:45Conference pass
Judges Remarks
Startup Pitches
Christina Waters, Chief Executive Officer, Archer Precision Medicine Advisory
Preetha Ram, Managing Partner, Pier 70 Ventures
Sep 2910:50Conference pass
Dirty Data, Broken Deals: The Hidden Legal Risks When AI Meets Drug Development
Startup Pitches
Adaku Nwachukwu, Managing Partner, A N Law Firm, PC
Sep 2911:10Conference pass
Would You Put This Biomarker in the Protocol? A Multi-scale Stress Test for Mechanism-driven Phase Ib/II Enrichment
Startup PitchesIngenix's Biological Reasoning Engine brings auditable, mechanism-grounded reasoning to the high-stakes decisions in drug development. We demonstrate one: whether a biomarker is ready to enrich or stratify a trial, with the evidence chain behind the call.
Through Modality Fusion, we connect modalities across biological scales: target biology, pathways, omics, model systems, and clinical data – integrated at the representation level into one architecture that reasons over the whole.
We run the stress test on a real biomarker: what makes it enrichment-grade, where the evidence breaks, and how our recommendation held up against the trial's actual readout.
Krzysztof Kolmus, Principal Scientist, Ingenix.AI
Sep 2911:20Conference pass
Using Multi-Modal patient data for Reinforcement Learning and Digital Twins for Biology Workflows
Startup Pitches
Sumit Sinha, Founder & CEO, OmicsBank
Sep 2911:40Conference pass
Chair's remarks
FAIR Data: Management, Storage and Architecture
Sree Chitoor, Chief Technology Officer, IAVI
Sep 2911:40Conference pass
Chair's remarks
AI in Clinical Trials
Sep 2911:40Conference pass
Chair's remarks
Real World Evidence
Catherine Brownstein, Assistant Professor, Harvard Medical School
Sep 2911:40Conference pass
Chair's remarks
Large Language Models
Dr. Nick (Nemanja) Kovacev, Surgeon/Engineer, OrtoMD Polyclinic
Sep 2911:40Conference pass
Chair's remarks
Digital Transformation
Sep 2911:40Conference pass
Chair's remarks
AI in Drug Discovery and Development
Sep 2911:45Conference pass
Digital Transformation Is Not a Line, It's a Circle
Digital Transformation
Sep 2911:45Conference pass
Engineering Certainty and Clinical Safety: From Probabilistic to Deterministic AI
Large Language Models
Dr. Nick (Nemanja) Kovacev, Surgeon/Engineer, OrtoMD Polyclinic
Sep 2911:45Conference pass
From Foundation to Mechanistic Models—and In Between: Leveraging AI to maximize the value of every patient's data across clinical development
AI in Drug Discovery and Development
Sep 2911:45Conference pass
From Silos to FAIR: Driving Data Strategy in Research and Development
FAIR Data: Management, Storage and Architecture
Sep 2911:45Conference pass
Integration of real world evidence in genomics discovery
Real World Evidence
Catherine Brownstein, Assistant Professor, Harvard Medical School
Sep 2911:45Conference pass
Robust AI
AI in Clinical Trials
Faisal Khan, Corporate Vice President AI and Analytics, Novo Nordisk
Sep 2912:05Conference pass
Beyond the Chatbot: Building Trusted Agentic AI with Scientific Services and Connectors for Drug R&D
Large Language ModelsPharmaceutical R&D organizations are investing heavily in agentic AI while also developing their own research platforms, copilots, and orchestration environments. However, many implementations still depend on fragmented data sources, inconsistent retrieval methods, and outputs that scientists must manually reconcile before they can support research decisions. This limits reproducibility, makes provenance difficult to preserve, and creates uncertainty about what evidence an AI system has used.
We describe Elsevier’s Life Sciences evolution from a cross-portfolio, multi-agent alpha to a modular architecture based on reusable scientific connectors and task-level agents. Connectors provide controlled access to trusted scientific data and capabilities including retrieve, resolve, normalize structured and unstructured evidence. Agents combine these components to address defined research tasks within broader workflows.
Examples from current work in chemistry, pharmacokinetics, and drug safety include compound identity resolution, chemical structure search, cross-source evidence retrieval, data harmonization, and structured evidence packaging. Separating these scientific capabilities from higher-level agent behavior makes them reusable across different products, models, and customer environments, while allowing provenance, completeness, and individual components to be evaluated explicitly.
Taken together, these examples provide a practical framework for deciding what should be implemented as a connector and task-level agent, or user-facing workflow, and for designing scientific AI that complements rather than replaces the enterprise research platforms pharmaceutical organizations are already building.
Sep 2912:05Conference pass
From signal to evidence: How AI is transforming GLP-1 research
Real World EvidenceBy the time many studies are designed, executed, and published, treatment patterns have shifted, prescribing behaviors have changed, and new clinical questions have already emerged. This lag between what is happening in practice and what is captured in evidence is becoming increasingly difficult to ignore, particularly in rapidly evolving therapeutic areas such as GLP-1 receptor agonists.
Using GLP-1 RA therapies as a case study, this session explores how AI-enabled real-world intelligence can rapidly identify emerging trends, answer research questions, generate hypotheses, and prioritize areas for deeper investigation. Attendees will see how early signals from continuously updated healthcare data can be translated into validated evidence, creating a seamless path from insight to decision-ready research.
The future of evidence generation is not simply faster studies. It is a continuous cycle of intelligence, validation, and learning. Learn how to identify meaningful signals, verify findings with rigor, and generate regulatory-grade evidence that keeps pace with real-world change.
Learning objectives
By the end of this session, attendees will be able to identify new ways to keep pace with rapidly evolving clinical practice and therapeutic markets, evaluate how AI can help generate real-world intelligence and accelerate evidence generation, and identify emerging patterns and generate research hypotheses from continuously updated healthcare data.
Spencer Andrei, PhD, Life Science Director, Truveta
Sep 2912:05Conference pass
From Silos to Synergy: How an Ecosystem-Driven ELN Accelerates Scientific Discovery
Digital Transformation
Rob Brown, Global VP and Head of the Scientific Office, Sapio Sciences
Sep 2912:05Conference pass
Generative AI for the Lab: Streamlining Scientific Workflows Through Intelligent Integration
AI in Clinical Trials
Akhil Rachamadugu, Director, Life Sciences, ServiceNow
Sep 2912:05Conference pass
RenSuper Workstation: An AI-Driven Antibody Discovery Platform From Target to Antibody Leads in Click
AI in Drug Discovery and DevelopmentWhat if you could move from target to candidate in a single click?
In this session, discover how Biocytogen’s RenSuper Workstation seamlessly integrates large-scale in vivo RenMice immunization, NGS, AI-powered high-throughput screening, and experimental validation to deliver high-quality, development-ready antibody sequences.
Learn how this platform unlocks access to a rich library of experimentally validated, fully human antibodies—complete with data packages to support faster, more confident decision-making. Explore how it enables rapid candidate identification, flexible antibody design, and data-driven workflows, reducing early discovery risk and compressing timelines from years to months.
Icy Niu, Executive Director, Global Business & Marketing Head, Biocytogen
Sep 2912:25Conference pass
AI-driven trial design and optimization
AI in Clinical Trials
Sep 2912:25Conference pass
Excelra
AI in Drug Discovery and Development
Norman Azoulay, Vice President, Platforms & Data, Excelra
Sep 2912:25Conference pass
Implementation of new age AI solutions and how to keep up with them
Digital Transformation
Sep 2912:25Conference pass
Multimodal Multi-Agent Solution for Sales Representatives
Large Language Models
Shihan He, Machine Learning Engineer, Novo Nordisk Inc.
Sep 2912:25Conference pass
The Data Dilemma: Storage, Management, and the Privacy Puzzle
FAIR Data: Management, Storage and Architecture
Joel Schwarz, Adjunct Law Professor, Cybersecurity and Privacy, Albany Law School
Sep 2912:45Conference pass
AI Built for Research: Accelerating Defensible Evidence from Real-World Data
Real World EvidenceMost AI isn't built for research. It's built for chat, for dashboards, for demos. In HEOR, that gap shows up fast. Fragmented data. Opaque models. Manual workflows that break the moment a regulator or peer reviewer asks how an answer was produced.
This session outlines a different approach: healthcare-native AI designed specifically for rigorous, defensible evidence. Three pieces, working together. A Healthcare Map of 60+ curated sources with validated representativeness across payers, regions, and care settings. A research-specific architecture with unified visit consolidation, 95%+ cost fill, and continuous enrollment, built so methodology holds up under scrutiny. And an AI engine with visible code generation, stepwise validation, and explicit articulation of every limitation.
The result is RWE that's faster, scalable, and trusted. Cohort identification compressed from hours to minutes. Descriptive statistics that used to take months. Industry standard R-package integration for inferential methods like propensity score matching, survival modeling, and weighted analyses, all version-controlled and replicable. Ready for the moments that matter most: market access discussions, internal strategic decision making, peer reviewed publications, and guideline directed medical therapy for real patients.
Key Learning Objectives
By the end of this session, attendees will be able to:
- Recognize why most AI approaches fail to meet the standards of HEOR research, including issues of data quality, transparency, and scientific rigor.
- Understand the three integrated components of healthcare-native AI: a comprehensive data foundation, a research-specific architecture, and an AI engine built for auditability.
- Map each safeguard to its purpose: visible code for auditable methods, cost imputation for unbiased economics, representativeness for generalizability.
- Identify HEOR use cases (feasibility, cohort construction, treatment patterns, cost and outcomes analyses) where AI built for research can compress timelines from months to minutes while preserving scientific integrity.
Ashis Das, Senior Director, Research Consulting - Research & Analytics, Komodo
Sep 2912:45Conference pass
Digital Transformation in Pharma Is Missing a Layer: Validation, Trust, and Submission Readiness
Digital TransformationThe pharmaceutical industryis rapidly adoptingdigital transformation andAI to accelerate clinical development, butthese technologiesalone do not reduce business risk. As AI-generated analyses become part of workflows, sponsors face a new challenge: how to establish confidence that outputs are accurate, traceable, and suitable for regulatory submission. Without an independent mechanism to verify AI-generated results, organizations risk undermining the very efficiencies digital transformation is intended to deliver.
When evaluating AI platforms and strategic technology investments, the question is no longer whether AI can generate clinical outputs; it is whether those outputs can be trusted. Building that trust requires an independent validation layer that provides objective evidence of quality, reproducibility, and regulatory readiness without relying ontraditional quality control methods that rely heavily on independent double programming, making them resource-intensive and difficult to scale in an era of AI-assisted development.
Beaconcure Verify, an AI platform designed specifically for regulated clinical development. Rather than automating statistical programming itself, Verify independently validates AI- and programmer-generated TLFs by reconstructing expected outputs directly from clinical datasets, SAPs, and specifications. Powered by a domain-specific AI model trained to reason like an experienced statistical programmer, the platform interprets clinical metadata, analysis logic, and programming intent to generate independent results that are compared against production outputs.
This independent verification approach enables rapid identification of discrepancies, strengthens risk-based quality strategies, and provides transparent evidence supporting submission readiness. By serving as a validation layer, Verify complements existing programming environments while helping organizations scale AI adoption with greater confidence and governance.
Attendees will gain insight into how trust, validation, and independent verification are emerging as essential components of AI strategies in life sciences. The session will explore how specialized validation platforms can reduce implementation risk, strengthen regulatory confidence, and enable organizations to adopt AI more broadly across clinical development while maintaining the quality standards required for submission-ready deliverables.
Sep 2912:45Conference pass
Regulatory-grade real-world evidence from unstructured clinical data
Large Language ModelsRoughly 40% of the clinical facts research needs never reach a structured data field: diagnoses, medication adherence, biomarkers, staging, social determinants, family history. Frontier LLMs can read that text, but at population scale they're expensive, non-deterministic, and hard to audit. This session shows how specialized medical language models extract and de-identify clinical facts at regulatory-grade accuracy: 98% F1 on PHI detection, and primary site, histology, and tumor staging extracted from unstructured pathology text at regulatory-grade accuracy (over 95%) – all at over 80% lower cost than current frontier models, with deterministic, reproducible output. Those facts become a governed, OMOP-standard real-world-evidence asset, with every value traced to its source note and every extraction carrying a confidence score. With that foundation in place, and a shared MCP boundary on top of it, use cases like cohort building, real-world evidence, clinical trial matching, and protocol design become far easier to build and to audit.
Sep 2912:45Conference pass
Same Experts, Smarter Agents: The Next Leap in Clinical Development
AI in Clinical TrialsAI has already reshaped preclinical development — now it's transforming Phase I-III trials. Study start-up is being compressed through AI-driven protocol design, site selection, and feasibility work. Real-world data is enabling synthetic control arms that can reduce the need to enroll patients into placebo groups, cutting study timelines and costs at a point where every day of a Phase III trial can cost upwards of a million dollars. And patient recruitment itself has flipped from a passive, wait-for-patients-to-come model to a proactive one, with AI matching protocol criteria against real-world data to identify eligible patients and where they're being treated — cutting recruitment workload by roughly 80%The next leap is agentic AI: systems that don't just surface insights but actively execute the work. But speed only matters if you can trust the output.In this session, Suzanne will share what "AI-native" really means in practice — where subject matter experts stay firmly in control of agentic workflows, why sourcing and human oversight are the foundation of trustworthy AI rather than an afterthought, and how real-time monitoring is already compressing feedback cycles from days and weeks down to minutes.Attendees will walk away with:
- Where AI is already delivering measurable time and cost savings versus where it's still maturing
- A practical framework for what "AI-native" means for a clinical organization, and how to keep subject matter experts in control as agents take on more of the operational workload
- Questions to ask any AI vendor or internal team about sourcing, hallucination risk, and oversight before trusting agentic output in a regulated trial environment
- A candid perspective on why "trust takes time" with AI — and concrete first steps for piloting agentic tools now rather than waiting for perfect reliability
Sep 2912:45Conference pass
Unleashing Innovation: From Data to Discovery with AMD + HPE
AI in Drug Discovery and Development
Sep 2912:45Conference pass
When Scientists Can't Find Their Data: FAIR Foundations for Real-World AI
FAIR Data: Management, Storage and ArchitectureHow Zifo helped a global biopharma turn disconnected data silos across different platforms into a single FAIR, AI-ready data ecosystem by automating ingestion, data models and metadata management . A practical playbook for building governed, reusable data that scientists trust and AI can actually use.
Deepak Rajendran, Senior Manager, Zifo Technologies Inc
Sep 2913:05Conference pass
Digital transformation in biotherapeutics discovery
Digital Transformation
Alexander Horspool, Associate Director of Data and Lab Automation, Boehringer Ingelheim
Sep 2913:05Conference pass
DNA Methylation-Mediated Epigenetic Adaptation Promotes Cellular Resilience In AML
Real World Evidence
Manna Ahmed, Assistant Research Professor - Manager of The Genomics Resource Facility, Fox Chase Cancer Center
Sep 2913:05Conference pass
Navigating launch challenges in rare disease markets
AI in Drug Discovery and Development
Soumavo Sarkar, Director, Novartis
Sep 2913:05Conference pass
Physics-Informed Large Language Models for Biologics: Applying Nuclear Engineering Rigor to AI Safety and Reliability
Large Language Models
Daya Shankar, Dean, School of Sciences and Founder SuktiAI, Woxsen University
Sep 2913:05Conference pass
Virtual patient generation leveraging RWD to expedite clinical development
AI in Clinical Trials
Yilin Xu, Head of clinical data analytics, Abbvie
Sep 2914:25Conference pass
Chair's remarks
AI in Drug Discovery and Development
Sep 2914:25Conference pass
Sep 2914:25Conference pass
Chair's remarks
FAIR Data: Management, Storage and Architecture
Lori Hoepner, Asst Professor, SUNY DOWNSTATE
Sep 2914:25Conference pass
Chair's remarks
AI in Clinical Trials
Soumavo Sarkar, Director, Novartis
Sep 2914:25Conference pass
Chair's remarks
AI in Clinical Trials
Soumavo Sarkar, Director, Novartis
Sep 2914:25Conference pass
Chair's remarks
Real World Evidence
Michael Liebman, Managing Director, IPQ Analytics, LLC
Sep 2914:30Conference pass
AI Applications: From Clinical Research To Operational Feasibility
AI in Clinical Trials
Paquita Chang, Global Clinical Operations AI & Data Scientist, Roche
Sep 2914:30Conference pass
Digital Transformation in Biomedical Research: Challenges and Opportunities
Digital Transformation
Jason Beckwith, SVP Talent Science BioTalent, University of Leeds
Sep 2914:30Conference pass
From High-Throughput Pipelines to FAIR Data: A Storage Strategy
FAIR Data: Management, Storage and Architecture
Sep 2914:30Conference pass
From Literature to Computable Cohorts: Disease Phenotyping via Multi-Agent Orchestration
AI in Drug Discovery and Development
Chris Willis, Director, Enterprise Business Insights & Technology, Bristol Myers Squibb
Sep 2914:30Conference pass
Large Language Models in Practice: From Patient Support to Precision Cancer Care
Large Language Models
Omer Alis, Director of Artificial Intelligence, Northeastern University
Sep 2914:30Conference pass
Multi-cancer Early Detection (MCED)
Real World Evidence
Samir Hanash, Director, Red And Charline Mccombs Institute For The Early Detection And Treatment Of Cancer, MD Anderson Cancer Center
Sep 2914:50Conference pass
Is your Lab in the Loop?
FAIR Data: Management, Storage and ArchitectureEdge AI in Production: Real-Time Decision-Making in Sanofi’s Cryo-EM CoreModern cryoEM and other high-throughput scientific instruments generate vast amounts of data, yet mismatched infrastructure often limits throughput, delays scientific insights, and allows poor-quality data to propagate. This session presents a deployment of on-premises edge AI that enables real-time data processing and decision-making directly at the instrument. By detecting anomalies and data quality issues from minute zero, the system ensures only high-value data advances through the pipeline. We will show how near-real-time analytics can also trigger automated device actuation, allowing instruments to adjust and optimize performance dynamically - without manual intervention. This approach reduces data movement and infrastructure costs while accelerating time to insight. Drawing on testing at Sanofi’s cryoEM core, attendees will gain practical insights into architecture, deployment considerations, and measurable impact on throughput, data quality, and operational efficiency.
Bill Lynch, Strategic Business Development Manager, Everpure1
Andrew Brown, Founder, Osmosys
Sep 2914:50Conference pass
Operationalizing AI for GxP: What It Takes to Reach Production at Scale
Digital TransformationMost pharma organizations can build an AI model. Far fewer can get that model into a validated GxP environment and sustain that validation through inspection, model change control, and ongoing oversight as agentic systems evolve. This session is about the infrastructure, governance, and validation architecture that senior R&D and IT leaders need to operationalize AI across GxP-regulated workflows at enterprise scale. We'll cover what it takes to move an entire portfolio of models and agents into production, not just one successful pilot, and what changes when that foundation is built in from the start rather than added after a model has already been deployed.
Matt Tendler, VP of Strategy, Life Sciences, Domino Data Lab
Sep 2914:50Conference pass
The Agentic Trial
AI in Clinical TrialsSession illustrates how intelligent coordination across planning, monitoring, data review, and study management can improve visibility, predictability, and execution quality.
Ashley Brenton, Global RWE Lead, Syneos Health
Sep 2914:50Conference pass
Unlocked & Validated: How Human-in-the-Loop LLMs Transform Unstructured EHR into High Quality RWD
AI in Drug Discovery and DevelopmentReal-world data (RWD) is essential to biopharma R&D, but critical signals often remain locked in unstructured data types such as clinical notes, out of reach from standard analytics. Automation alone cannot reliably extract these features; success requires context, and context only emerges when clinical expertise is embedded throughout such workflows.
To capitalize on the phenotypic depth of its EHR-derived RWD, NashBio built a multi-layer LLM extraction system designed around clinical experts who informed extraction criteria, guided prompt and workflow refinements, and evaluated model output against source records to improve capture of clinically meaningful events. Applied to a 2,800 patient inflammatory bowel disease (IBD) cohort, the pipeline surfaced treatment response outcomes from each patient’s IBD clinic notes, spanning a predefined list of 25 medications. The result was more than 58,000 structured medication-response assessments, each substantiated by a verbatim quote from the attending healthcare provider and reviewable in context. This human-in-the-loop architecture achieved >90% accuracy on sampled review and 96% reproducibility – a level of rigor typically reserved for manual chart review – delivered at scale to advance more personalized medicine. Automation alone also missed important documentation patterns unique to specialty care and institutional practice.
NashBio's experience challenges the perception that AI eliminates the need for humans. This work reinforced the fact that, as extraction systems scale, human-in-the-loop matters more, not less; it is what keeps accuracy and context intact. We will also demonstrate how this framework extends to other applications relevant for biopharma R&D, including hepatology feature extraction and biomarker curation.
Leeland Ekstrom, CEO & Co-Founder, NashBio
Sep 2914:50Conference pass
Using real world data in IBD research
Real World Evidence
Tara Fehlmann, Senior Manager, Data Science & Analytics, Crohn's and Colitis Foundation
Sep 2914:50Conference pass
What Makes AI Regulatory Grade? Why LLMs Alone Are Not Enough for Regulated Workflows
Large Language ModelsLarge language models have transformed what is possible in regulatory content creation, but generating fluent text is only the beginning. As the industry moves toward agentic workflows and increasingly automated submission processes, the defining challenge is no longer whether AI can write, but whether its outputs can be trusted.
This session explores what separates regulatory grade AI from general purpose AI, and why successful implementations in regulated environments require more than a language model. Attendees will learn how organizations are combining LLMs with deterministic logic, structured domain knowledge, validation frameworks, and governed human oversight to produce outputs that support the expectations of regulated content development.
Drawing on emerging industry practices and evolving regulatory expectations, the session provides a practical framework for evaluating AI solutions against the capabilities that matter most in regulated environments, including transparency, repeatability, auditability, source traceability, and accountability. Attendees will leave with a concrete framework for assessing whether an AI system is truly designed for regulatory workflows.
Camille Sauder, Director of Solutions Engineering, Yseop
Sep 2915:10Conference pass
Agentic AI for biotech competitive intelligence
Large Language Models
Daniel de Moraes Branco, Senior Director Corporate Development, Latin America Medical Affairs Lead, Artiva Biotherapeutics
Sep 2915:10Conference pass
AI-Powered Predictive Analytics for Surgical and ICU Patients
Real World Evidence
Qingchu Jin, Faculty Scientist I, MaineHealth Institute for Research
Sep 2915:10Conference pass
AI-powered programmable virtual humans for physiologically based drug discovery
AI in Drug Discovery and Development
Lei Xie, Professor, Northeastern University
Sep 2915:10Conference pass
Digital innovations driving clinic trials efficiencies
AI in Clinical Trials
Naomi Fried, CEO, Pharmstars
Sep 2915:10Conference pass
Pitfalls and Best Practices: Notes from the (research) field
FAIR Data: Management, Storage and Architecture
Lori Hoepner, Asst Professor, SUNY DOWNSTATE
Sep 2915:10Conference pass
Securing the Agentic Future: How Privacy and Trust Drive Real Digital Transformation
Digital Transformation
Preetha Ram, Managing Partner, Pier 70 Ventures
Sep 2915:30Conference pass
Building Next-Generation Engagement Using the Power of AI - From Concept to Action
Large Language Modelsny AI discussions emphasize the size of the dataset and the cleverness of the model. In practice, those factors are rarely where the real difference is made.What actually moves things forward is quieter, and far harder to get right: clean, carefully chosen data, and one problem that genuinely matters in practice.
This session is a behind-the-scenes case study of how Innomagine moved from concept to action. We didn’t start with the technology — we started with the people behind the work, and built a connected suite of engagement solutions on one foundation: the Evidence Eagle platform.
We’ll walk through three AI applications already used every day:
- Medical Simulator helps MSLs practice realistic KOL conversations with AI-powered feedback.
- Institutional Engagement Eagle shows who to engage, why they matter, and how to reach them.
- AI-generated Engagement Plans turn existing research into clear, actionable strategies.
Because all three use the same trusted data, insights flow seamlessly from research to engagementtoaction.
We then turn to the question every team faces: build, or buy? Building in-house is tempting, but the hard parts surface later — curating data you can trust, keeping scarce AI talent, and maintaining infrastructure long after the pilot ends. We make the case for the “smart buy”: a focused partner whose platform, curated data, and proven use cases are ready today — so teams reach value in weeks, not years.
The takeaway: look past the big-data hype, get the build-versus-buy call right, and start with clean data and one problem worth solving. That’s where meaningful AI adoption begins — and where real impact, for your teams and the patients at the end of it, follows.
Mayank Bhanderi, Director, Innomagine Consulting Private Limited
Julio Jose Fernandez, Director Field Medical Affairs, Regeneron
Sep 2915:30Conference pass
FAIR Data Foundations: Automating Governance and AI-Readiness for Accelerated Drug Discovery
FAIR Data: Management, Storage and ArchitectureIn this session, we'll cover:
- The Modern Data Dilemma in Biotech - massive volumes of unstructured, often fragmented research data, that hinder innovation, impose regulatory risk and missed milestones
- Building a Scalable "Data Foundation" - implementing a unified data governance framework that balances speed, risk and prepares for intelligent content management
- Governance as an AI Enabler - highlighting metadata management to ensure datasets are discoverable, high-quality and protected
- Future-Proofing the Pipeline - the roadmap for AI-driven regulatory intelligence and its role in streamlining future clinical trials
Sep 2915:30Conference pass
From AI Experiments to Enterprise Impact: What Does It Really Take to Operationalize AI in Pharma
AI in Drug Discovery and Development
Justin Scheer, Vice President, Global Head - In Silico Discovery, Johnson & Johnson
Robert Fenwick, Innovation and Technology Consultant, EPAM Systems, Inc.
Sep 2915:30Conference pass
The Evolution of Real-World Evidence: From Individual Data Assets to Connected Healthcare Intelligence
Real World EvidenceReal-world evidence is evolving from isolated datasets to integrated healthcare intelligence that provides context, not just observations. Learn how Norstella combines multimodal real-world data with proprietary commercial, access, and healthcare intelligence to answer increasingly complex questions that no single nor traditional real-world data source can address, speeding up time to insights.
Sep 2915:30Conference pass
The Skills Behind the Strategy: Preparing Today's Workforce for Digital Transformation
Digital Transformation
Uwe Hohgrawe, Associate Dean Global Learner Access, Strategic Partnerships and AI, Northeastern University
Sep 2915:50Conference pass
AI ML for drug discovery
AI in Drug Discovery and Development
Sep 2915:50Conference pass
Applications of biological foundation models to accelerate clinical trials
AI in Clinical Trials
Neil Pfister, Assistant Professor; Head of AI in Precision Medicine Research Group, University of Alabama at Birmingham
Sep 2915:50Conference pass
Digital transformation within medical and scientific communications within pharma and biotech
Digital Transformation
Sep 2915:50Conference pass
Diving deeper into indications
FAIR Data: Management, Storage and Architecture
Maureen Stark, Biospecimen Senior Specialist - Data Mgt, Roche
Sep 2915:50Conference pass
Medical information response agent
Large Language Models
Chandni Patel, Director, Medical Information, Novo Nordisk
Sep 2915:50Conference pass
The Human Heart of High-Tech Care: Reimagining the Nurse Navigator in the Age of Precision Medicine
Real World Evidence the field of oncology continues to explorecutting-edge breakthroughs in AI, genomic sequencing, and digital transformation, it’s easy to lose sight of the human being at the center—the patient. While industry leaders focus on the data and the drug, the Oncology Nurse Navigator (ONN) never loses sight of the person behind the diagnosis. This presentation brings the ONN’s story to life as the “human translator” of biotechnology—turning intimidating data into understandable choices, calming fears, and ensuring that the promise of precision medicine is matched by genuine, personalized support.Objectives:By the end of this presentation, participants will be able to:
- Identify the three primary psychological and logistical barriers patients face when transitioning from standard care to high-complexity interventions.
- Analyze how the navigator role mitigates disparities in access to precision medicine by addressing social determinants of health (SDOH) that digital platforms often overlook.
- Formulate a model for integrating biotech stakeholders (pharma, diagnostics, and tech) with the nursing navigation team to ensure innovations enhance, rather than disrupt, the patient experience
Sep 2916:05Conference pass
Judges Remarks
Startup Pitches
Christina Waters, Chief Executive Officer, Archer Precision Medicine Advisory
Preetha Ram, Managing Partner, Pier 70 Ventures
Sep 2916:10Conference pass
The Agent Did It. Now Can You Prove It? Cryptographic Traceability for AI in Clinical Workflows
Startup Pitches
Alistair Dootson, Head Life Sciences, EQTY Life Sciences
Tina Morrison, Head of Life Sciences, EQTY Lab
Sep 2916:20Conference pass
Responsible AI by Design: How to Launch Agentic AI in Regulated Biotech
Startup Pitches
Atika Kumar, Founder, Wiztree Consulting
Sep 2916:30Conference pass
AI-Driven Insights from Every single Patient
Startup Pitches
Mattia Marco Caruson, Managing Director, mama health
Sep 2916:40Conference pass
Chair's remarks
AI in Drug Discovery and Development
Pooja Majmudar, Investment Partner, KELES Digital Health
Sep 2916:40Conference pass
Chair's remarks
Large Language Models
Jake Chen, Endowed Professor and Director, University of Alabama at Birmingham
Sep 2916:40Conference pass
Chair's remarks
Digital Transformation
Shabana Motlani, Director, USO PS QA & GxP Automation & Analytics, Novo Nordisk
Sep 2916:40Conference pass
Chair's remarks
FAIR Data: Management, Storage and Architecture
Sep 2916:40Conference pass
Chair's remarks
Real World Evidence
Michael Liebman, Managing Director, IPQ Analytics, LLC
Sep 2916:45Conference pass
Bridging the gap between FEMtech and FEMhealth
Real World Evidence
Michael Liebman, Managing Director, IPQ Analytics, LLC
Sep 2916:45Conference pass
Building Bridges: Developing a Common Omics Data Platform at AbbVie
FAIR Data: Management, Storage and Architecture
Sep 2916:45Conference pass
Leveraging Data and AI to transform Research, Care, and Operations at Sylvester Comprehensive Cancer Center
Digital Transformation
Vasileios Stathias, Assistant Director, Data Science, Sylvester Comprehensive Cancer Center
Sep 2916:45Conference pass
Protecting Our Cognitive Edge: The Paradox of AI Offloading vs. Skills Atrophy
AI in Clinical Trials
Sep 2916:45Conference pass
ROI on AI implantation in drug discovery
AI in Drug Discovery and Development
Pooja Majmudar, Investment Partner, KELES Digital Health
Sep 2917:05Conference pass
Enabling LLM-assisted biomedical discovery in secure, trusted agentic environments
AI in Drug Discovery and DevelopmentBiomedical researchers face a critical productivity gap when working within Trusted Research Environments, where strict security rules ban the use of Large Language Models (LLMs). To solve this, we present the Trusted Agentic Environment, designed to embed AI tools and make them available to researchers by default. By attaching flexible, automated policies directly to datasets, researchers can seamlessly leverage LLMs for code generation, data exploration, and statistical modeling. This integration unlocks complex co-analysis, empowering researchers with advanced AI capabilities to accelerate their scientific output.
Sep 2917:05Conference pass
LLMs for Medical Affairs: Content, Engagement, and Meaningful Scientific Impact for HCPs
Large Language Models
John Wright, Technology Director, Global Medical Affairs, Amgen
Sep 2917:25Conference pass
AI-Powered Trials: Unlocking Leadership in Clinical Innovation
Real World Evidence
Yilin Xu, Head of clinical data analytics, Abbvie
Hong Truong, Principal, Define Ventures
Daniel de Moraes Branco, Senior Director Corporate Development, Latin America Medical Affairs Lead, Artiva Biotherapeutics
Sep 2917:25Conference pass
Better ways to recruit and retain patients
AI in Clinical Trials
Faisal Khan, Corporate Vice President AI and Analytics, Novo Nordisk
Shahanaz Rahman, Senior Clinical Trials Manager, ITM
Sep 2917:25Conference pass
Digital health and AI, what does the future hold?
Digital Transformation
Moderator: Naomi Fried, CEO, Pharmstars
Omer Alis, Director of Artificial Intelligence, Northeastern University
Sep 2917:25Conference pass
From AI Experiments to Enterprise Impact: What Does It Really Take to Operationalize AI in Pharma
Large Language Models
Chandni Patel, Director, Medical Information, Novo Nordisk
Puneeth kumar Ammapalli, Principle Cloud Architect, CVS Ltd
Sep 2917:25Conference pass
The Health Data Trilemma: Privacy, National Security, and Borderless dataflows
FAIR Data: Management, Storage and Architecture
Moderator: Joel Schwarz, Adjunct Law Professor, Cybersecurity and Privacy, Albany Law School
Huda Eldosougi, Chief Technical Advisor, Saudi Food and Drug Authority
Alexander Sherman, Director, Center for Innovation and Bioinformatics, Mass General Hospital, Harvard Medical School
Preetha Ram, Managing Partner, Pier 70 Ventures
Shabana Motlani, Director, USO PS QA & GxP Automation & Analytics, Novo Nordisk
Sep 2917:25Conference pass
Using AI in the drug development and discovery process
AI in Drug Discovery and Development
David Sherris, ceo, Attivare Therapeutics
Santrupti Nerli, Scientist, Genentech
Create your personal agenda –check the favourite icon
Sep 309:10Conference pass
Introductory remarks
Keynotes
Anna Abiola, Conference Director, Terrapinn Holdings Ltd
Sep 309:15Conference pass
Chair's remarks
Keynotes
Christina Waters, Chief Executive Officer, Archer Precision Medicine Advisory
Sep 309:20Conference pass
Biotech in the balance
Keynotes
Jeremy Levin, Executive Chairman, Ovid Therapeutics
Sep 309:40Conference pass
Evolution of women’s health
Keynotes
Preetha Ram, Managing Partner, Pier 70 Ventures
Catherine Brownstein, Assistant Professor, Harvard Medical School
Preetha Ram, Managing Partner, Pier 70 Ventures
Sep 3010:40Conference pass
Judges Remarks
Startup Pitches
Christina Waters, Chief Executive Officer, Archer Precision Medicine Advisory
Preetha Ram, Managing Partner, Pier 70 Ventures
Sep 3010:45Conference pass
From Synopsis to Shortlist in Seconds: AI-Powered Site Selection & Feasibility
Startup Pitches
Sep 3010:55Conference pass
From Innovation to Revenue: Fixing the Commercial Gap in Science-Led
Startup PitchesStrong innovation doesn’t automatically lead to commercial success. Many science-led companies struggle to translate capability into revenue. This session introduces a platform that identifies commercial gaps and turnsinnovation into a clear, execution-ready growth strategy.
Nandy Thaver, Director, Thaver Consulting
Sep 3011:05Conference pass
A Smarter Path to Regulatory-Grade Evidence Synthesis Using Agentic AI
Startup Pitches
Gaugarin Oliver, CEO & Founder, MadeAi
Sep 3011:15Conference pass
The AI-Native Platform for Drug Development
Startup Pitches
Xiaomai Zhang, Chief Marketing Officer, HopeAI, Inc.
Sep 3011:25Conference pass
WittGen: GenAI Drug Response Prediction, Cell by Cell
Startup Pitches
Minwoo Jung, CTO, WittGen Inc.
Sep 3011:40Conference pass
Chair's remarks
AI in Drug Discovery and Development
Sep 3011:40Conference pass
Chair's remarks
Bioinformatics + InSilico R&D
Qingchu Jin, Faculty Scientist I, MaineHealth Institute for Research
Sep 3011:40Conference pass
Chair's remarks
Technology & Innovation
Daniel de Moraes Branco, Senior Director Corporate Development, Latin America Medical Affairs Lead, Artiva Biotherapeutics
Sep 3011:40Conference pass
Chair's remarks
Real World Evidence
Michael Liebman, Managing Director, IPQ Analytics, LLC
Sep 3011:40Conference pass
Chair's remarks
AI in Clinical Trials
Sep 3011:45Conference pass
AI in medical authoring and regulatory intelligence
AI in Clinical Trials
Sree Chitoor, Chief Technology Officer, IAVI
Sep 3011:45Conference pass
Beyond SBIRs: NIH as your technology development and commercialisation partner
Technology & Innovation
Sep 3011:45Conference pass
Building Contextual Layer for Agentic Framework for Clinical Data Science
Large Language Models
Mukul Virmani, Director, Clinical Data Science, AI Research Center, Gilead Sciences, Inc.
Sep 3011:45Conference pass
Harnessing the Power of Generative AI: Unlocking Insights from Real-World Data for Data-Driven Decisions in Pharma R&D
Real World Evidence
Sep 3011:45Conference pass
How AI can help early stage companies in drug development
AI in Drug Discovery and Development
Lou Kassa, CEO, Pennsylvania Biotechnology Center
Sep 3011:45Conference pass
When Quantum Meets Pharma: Inside the Race to Design Drugs Atom by Atom
Bioinformatics + InSilico R&D
Sep 3012:05Conference pass
From Hours to Minutes: Reimagining Site Operations with AI
AI in Clinical Trials
Vanessa Diaz, Clinical Trials Research Manager, Illinois Bone & Joint Institute
Sep 3012:05Conference pass
Impact of insilico innovations on drug discovery and development
Bioinformatics + InSilico R&D
Hanqing Li, investment director, Merck
Sep 3012:05Conference pass
Multi-modal real world data foundation models: Challenges & opportunities
Real World Evidence
Janie Shelton, Director, Translational Epidemiology, Bristol Myers Squibb
Sep 3012:05Conference pass
Structuring high-consequence decisions from discovery to the clinic
AI in Drug Discovery and Development
Juan Beltran, Senior Director of AI, Machine Learning & Innovation, BullFrog AI
Sep 3012:25Conference pass
AI to select biomarker candidates
AI in Drug Discovery and Development
Fernanda Cerqueira, Senior Associate Director, Michael J Fox Foundation for Parkinson's Research
Sep 3012:25Conference pass
Driving Innovation in Oncological care with Radiopharmaceuticals
Technology & Innovation
Tracy Casas, Director of Clinical Operations/ Nurse Practitioner, Comprehensive Hematology Oncology
Sep 3012:25Conference pass
Maximizing the Value of Real-World Evidence for Patient Safety
Real World EvidenceAs the pharmaceutical industry becomes increasingly data-driven, organizations are generating more real-world data (RWD) and real-world evidence (RWE) than ever before. While RWE presents significant opportunities to enhance patient safety, realizing its full value requires navigating operational, methodological, and regulatory complexities.
Realizing this value requires strong collaboration between patient safety and RWE functions to ensure evidence is appropriately contextualized and translated into meaningful action.
Drawing on practical examples and lessons learned, this session explores the opportunities and challenges at the intersection of patient safety and RWE.
Attendees will gain insights into approaches for strengthening cross-functional collaboration, leveraging RWE more effectively, and improving confidence in the safe use of medicines.
Osamagbe Woghiren, Senior Manager, QPPV Office & International Pharmacovigilance Operations, UCB
Sep 3012:25Conference pass
Running Large Language Models on AWS: Bedrock, SageMaker, and Purpose-Built AI Infrastructure
Large Language Models
Puneeth kumar Ammapalli, Principle Cloud Architect, CVS Ltd
Sep 3012:25Conference pass
Virtual cell modelling
Bioinformatics + InSilico R&D
Jake Chen, Endowed Professor and Director, University of Alabama at Birmingham
Sep 3012:45Conference pass
How Novo Nordisk Transformed Customer targeting using AI
Technology & Innovation
Sai Rithvik Kanakamedala, Data Scientist, Novo Nordisk
Sep 3012:45Conference pass
Identifying Tumour Biomarkers Using Real World Data
Real World Evidence
Zaigham Ali Khan, Associate Director, Merck
Sep 3012:45Conference pass
Leveraging Real-World Data and AI for the Evaluation of Clinical Trial Efficacy
AI in Clinical TrialsBackground:Traditional clinical trial evaluations heavily rely on the Average Treatment Effect (ATE), a metric that can obscure substantial heterogeneity in individual patient responses. This limitation is magnified in observational real-world data (RWD), where simple statistical associations often conflate correlation with systemic selection bias. To enhance regulatory confidence and optimize therapeutic positioning, clinical research must move beyond purely associative analyses and shift toward isolating true counterfactual causation by precisely estimating Individual Treatment Effects (ITE) to enable personalized treatment selection.
Methods:This study presents a deep causal learning framework that adjusts for high-dimensional baseline confounding. Utilizing RWD from a diverse clinical cohort in which more than 80% of participants originate from communities historically underrepresented in biomedical research, the pipeline maps longitudinal, time-fixed, and time-varying covariate vectors into a dense latent space via a pre-trained Med-BERT transformer encoder. These phenotypic embeddings parameterize an integrated causal neural network architecture that features an objective-focused propensity score gate and dual counterfactual potential-outcome heads. To evaluate individual treatment benefit under non-proportional hazards, the architecture maps continuous counterfactual survival curves for patients within the balanced common support region where the propensity score is approximately 0.5. Rather than collapsing temporal dynamics into static metrics, the framework evaluates the global geometry of these curves using non-parametric omnibus tests to isolate and rank an optimized sub-cohort of the top ntreatment responders.
Results:Validation audits demonstrated exceptional predictive fidelity, with the outcome survival network achieving a baseline Concordance Index of 0.9439 ± 0.0771. The causal estimation engine yielded a statistically significant Conditional Average Treatment Effect (CATE) point estimate of 0.6205 (95% Confidence Interval: 0.5934 to 0.6476), with structural validity confirmed by a Leave-One-Out (LOO) cohort stability index of 99.71%. The integrated KONP and multi-directional screening framework successfully isolated the optimized high-responder cohort from the balanced common-support distribution.
Conclusion:Shifting from associative correlation to robust counterfactual inference on highly diverse RWD provides a scalable, causal methodology for predicting personalized treatment response, optimizing trial cohort enrichment strategies, and accelerating precision evidence generation without modifying active clinical trajectories.
Tarek Adam, President and CMO, Phalcon, LLC
Sep 3013:05Conference pass
AI-driven phenotyping system that extracts operational definitions of patient cohorts
Large Language Models
Andrew Smith, Senior Principal Scientist, Bristol Myers Squibb
Sep 3013:05Conference pass
How to integrate multi-omics
Bioinformatics + InSilico R&D
Yi-Hsiang Hsu, Director and associate Professor, Broad Institute of MIT and Harvard
Sep 3013:05Conference pass
Leveraging NIH as your commercialization partner
Technology & Innovation
Sep 3013:05Conference pass
Leveraging RWD in the Drug Development Process
Real World Evidence
Thomas Dougherty, Data Science & AI Innovative Partnership Lead, Novo Nordisk
Sep 3013:05Conference pass
Mind the Gap: How Artificial Intelligence Can Strengthen the Clinical Trial Enterprise from Protocol Design to External Controls
AI in Clinical Trials
Alexander Sherman, Director, Center for Innovation and Bioinformatics, Mass General Hospital, Harvard Medical School
Sep 3013:05Conference pass
Revolutionizing immunotherapy through patient-centric therapeutic hardware platform, ATT-AI
AI in Drug Discovery and Development
David Sherris, ceo, Attivare Therapeutics
Sep 3014:25Conference pass
Chair's remarks
AI in Drug Discovery and Development
Michael Liebman, Managing Director, IPQ Analytics, LLC
Sep 3014:25Conference pass
Chair's remarks
AI in Clinical Trials
Sep 3014:30Conference pass
AI influenced nano technology in drug discovery and development
AI in Drug Discovery and Development
Beauty Pandey, Associate Dean & Associate Professor, Woxsen University
Sep 3014:30Conference pass
Detecting Dengue, Chikungunya, and Oropouche Viruses Using Radio Frequency Waves and Machine Learning.
Bioinformatics + InSilico R&D
Omer Alis, Director of Artificial Intelligence, Northeastern University
Sep 3014:30Conference pass
Driving field medical innovation
Real World Evidence
Karuna Kantor, Director, Novo Nordisk
Sep 3014:50Conference pass
How AI is used to support the three R’s in drug discovery
AI in Drug Discovery and Development
Sep 3014:50Conference pass
Sponsored presentation
Real World Evidence
Sep 3015:10Conference pass
Not All Bias Breaks Decisions: Relative Ignorability for Real-World Evidence
Bioinformatics + InSilico R&D
Sep 3015:10Conference pass
Preparing the Biotech Workforce of the Future: The Role of Academic-Industry Partnerships
Real World Evidence
Sep 3015:10Conference pass
Regulatory affairs, The regulations and guidance globally
AI in Drug Discovery and Development
Jia Huang, Associate Director, Merck
Sep 3015:50Conference pass
Pediatric Precision Medicine
Keynotes
Wendy Chung, Chief, Department of Pediatrics, Boston Children's Hospital
last published: 05/Aug/26 10:15 GMT
Boston, 29 - 30 September 2026
Schedule
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Introductory remarks
Chair's remarks
Advancing the development of individualised genetic therapies
Investment trends in AI
Roundtables
Agentic AI in Drug Discovery
AI adoption in small-scale cell & gene therapy biomanufacturing: opportunities, challenges, and practical pathways
AI-Driven Futures: Roadmap to Digital Transformation
AI‑Ready Drug Discovery: What It Actually Takes
Building the Intelligent Health Ecosystem: Where Pharma Meets AI Powered by Real-World Data
From Bench to Bedside: Smarter Pre-Clinical Strategies to De-Risk Early-stage Technologies
From Complexity to Clarity: Single-Cell, Spatial, and Multi-Omics
Investment Trends in 2026: AI is everywhere, where Capital Is Actually Going (and Why)? Real investor perspectives + live founder feedback
Invite Only: Building the Future of AI & Data Strategy in Life Sciences
Judges Remarks
Dirty Data, Broken Deals: The Hidden Legal Risks When AI Meets Drug Development
Would You Put This Biomarker in the Protocol? A Multi-scale Stress Test for Mechanism-driven Phase Ib/II Enrichment
Ingenix's Biological Reasoning Engine brings auditable, mechanism-grounded reasoning to the high-stakes decisions in drug development. We demonstrate one: whether a biomarker is ready to enrich or stratify a trial, with the evidence chain behind the call.
Through Modality Fusion, we connect modalities across biological scales: target biology, pathways, omics, model systems, and clinical data – integrated at the representation level into one architecture that reasons over the whole.
We run the stress test on a real biomarker: what makes it enrichment-grade, where the evidence breaks, and how our recommendation held up against the trial's actual readout.
Using Multi-Modal patient data for Reinforcement Learning and Digital Twins for Biology Workflows
Chair's remarks
Chair's remarks
Chair's remarks
Chair's remarks
Chair's remarks
Chair's remarks
Digital Transformation Is Not a Line, It's a Circle
Engineering Certainty and Clinical Safety: From Probabilistic to Deterministic AI
From Foundation to Mechanistic Models—and In Between: Leveraging AI to maximize the value of every patient's data across clinical development
From Silos to FAIR: Driving Data Strategy in Research and Development
Integration of real world evidence in genomics discovery
Robust AI
Beyond the Chatbot: Building Trusted Agentic AI with Scientific Services and Connectors for Drug R&D
Pharmaceutical R&D organizations are investing heavily in agentic AI while also developing their own research platforms, copilots, and orchestration environments. However, many implementations still depend on fragmented data sources, inconsistent retrieval methods, and outputs that scientists must manually reconcile before they can support research decisions. This limits reproducibility, makes provenance difficult to preserve, and creates uncertainty about what evidence an AI system has used.
We describe Elsevier’s Life Sciences evolution from a cross-portfolio, multi-agent alpha to a modular architecture based on reusable scientific connectors and task-level agents. Connectors provide controlled access to trusted scientific data and capabilities including retrieve, resolve, normalize structured and unstructured evidence. Agents combine these components to address defined research tasks within broader workflows.
Examples from current work in chemistry, pharmacokinetics, and drug safety include compound identity resolution, chemical structure search, cross-source evidence retrieval, data harmonization, and structured evidence packaging. Separating these scientific capabilities from higher-level agent behavior makes them reusable across different products, models, and customer environments, while allowing provenance, completeness, and individual components to be evaluated explicitly.
Taken together, these examples provide a practical framework for deciding what should be implemented as a connector and task-level agent, or user-facing workflow, and for designing scientific AI that complements rather than replaces the enterprise research platforms pharmaceutical organizations are already building.
From signal to evidence: How AI is transforming GLP-1 research
By the time many studies are designed, executed, and published, treatment patterns have shifted, prescribing behaviors have changed, and new clinical questions have already emerged. This lag between what is happening in practice and what is captured in evidence is becoming increasingly difficult to ignore, particularly in rapidly evolving therapeutic areas such as GLP-1 receptor agonists.
Using GLP-1 RA therapies as a case study, this session explores how AI-enabled real-world intelligence can rapidly identify emerging trends, answer research questions, generate hypotheses, and prioritize areas for deeper investigation. Attendees will see how early signals from continuously updated healthcare data can be translated into validated evidence, creating a seamless path from insight to decision-ready research.
The future of evidence generation is not simply faster studies. It is a continuous cycle of intelligence, validation, and learning. Learn how to identify meaningful signals, verify findings with rigor, and generate regulatory-grade evidence that keeps pace with real-world change.
Learning objectives
By the end of this session, attendees will be able to identify new ways to keep pace with rapidly evolving clinical practice and therapeutic markets, evaluate how AI can help generate real-world intelligence and accelerate evidence generation, and identify emerging patterns and generate research hypotheses from continuously updated healthcare data.
From Silos to Synergy: How an Ecosystem-Driven ELN Accelerates Scientific Discovery
Generative AI for the Lab: Streamlining Scientific Workflows Through Intelligent Integration
RenSuper Workstation: An AI-Driven Antibody Discovery Platform From Target to Antibody Leads in Click
What if you could move from target to candidate in a single click?
In this session, discover how Biocytogen’s RenSuper Workstation seamlessly integrates large-scale in vivo RenMice immunization, NGS, AI-powered high-throughput screening, and experimental validation to deliver high-quality, development-ready antibody sequences.
Learn how this platform unlocks access to a rich library of experimentally validated, fully human antibodies—complete with data packages to support faster, more confident decision-making. Explore how it enables rapid candidate identification, flexible antibody design, and data-driven workflows, reducing early discovery risk and compressing timelines from years to months.
AI-driven trial design and optimization
Excelra
Implementation of new age AI solutions and how to keep up with them
Multimodal Multi-Agent Solution for Sales Representatives
The Data Dilemma: Storage, Management, and the Privacy Puzzle
AI Built for Research: Accelerating Defensible Evidence from Real-World Data
Most AI isn't built for research. It's built for chat, for dashboards, for demos. In HEOR, that gap shows up fast. Fragmented data. Opaque models. Manual workflows that break the moment a regulator or peer reviewer asks how an answer was produced.
This session outlines a different approach: healthcare-native AI designed specifically for rigorous, defensible evidence. Three pieces, working together. A Healthcare Map of 60+ curated sources with validated representativeness across payers, regions, and care settings. A research-specific architecture with unified visit consolidation, 95%+ cost fill, and continuous enrollment, built so methodology holds up under scrutiny. And an AI engine with visible code generation, stepwise validation, and explicit articulation of every limitation.
The result is RWE that's faster, scalable, and trusted. Cohort identification compressed from hours to minutes. Descriptive statistics that used to take months. Industry standard R-package integration for inferential methods like propensity score matching, survival modeling, and weighted analyses, all version-controlled and replicable. Ready for the moments that matter most: market access discussions, internal strategic decision making, peer reviewed publications, and guideline directed medical therapy for real patients.
Key Learning Objectives
By the end of this session, attendees will be able to:
- Recognize why most AI approaches fail to meet the standards of HEOR research, including issues of data quality, transparency, and scientific rigor.
- Understand the three integrated components of healthcare-native AI: a comprehensive data foundation, a research-specific architecture, and an AI engine built for auditability.
- Map each safeguard to its purpose: visible code for auditable methods, cost imputation for unbiased economics, representativeness for generalizability.
- Identify HEOR use cases (feasibility, cohort construction, treatment patterns, cost and outcomes analyses) where AI built for research can compress timelines from months to minutes while preserving scientific integrity.
Digital Transformation in Pharma Is Missing a Layer: Validation, Trust, and Submission Readiness
The pharmaceutical industryis rapidly adoptingdigital transformation andAI to accelerate clinical development, butthese technologiesalone do not reduce business risk. As AI-generated analyses become part of workflows, sponsors face a new challenge: how to establish confidence that outputs are accurate, traceable, and suitable for regulatory submission. Without an independent mechanism to verify AI-generated results, organizations risk undermining the very efficiencies digital transformation is intended to deliver.
When evaluating AI platforms and strategic technology investments, the question is no longer whether AI can generate clinical outputs; it is whether those outputs can be trusted. Building that trust requires an independent validation layer that provides objective evidence of quality, reproducibility, and regulatory readiness without relying ontraditional quality control methods that rely heavily on independent double programming, making them resource-intensive and difficult to scale in an era of AI-assisted development.
Beaconcure Verify, an AI platform designed specifically for regulated clinical development. Rather than automating statistical programming itself, Verify independently validates AI- and programmer-generated TLFs by reconstructing expected outputs directly from clinical datasets, SAPs, and specifications. Powered by a domain-specific AI model trained to reason like an experienced statistical programmer, the platform interprets clinical metadata, analysis logic, and programming intent to generate independent results that are compared against production outputs.
This independent verification approach enables rapid identification of discrepancies, strengthens risk-based quality strategies, and provides transparent evidence supporting submission readiness. By serving as a validation layer, Verify complements existing programming environments while helping organizations scale AI adoption with greater confidence and governance.
Attendees will gain insight into how trust, validation, and independent verification are emerging as essential components of AI strategies in life sciences. The session will explore how specialized validation platforms can reduce implementation risk, strengthen regulatory confidence, and enable organizations to adopt AI more broadly across clinical development while maintaining the quality standards required for submission-ready deliverables.
Regulatory-grade real-world evidence from unstructured clinical data
Roughly 40% of the clinical facts research needs never reach a structured data field: diagnoses, medication adherence, biomarkers, staging, social determinants, family history. Frontier LLMs can read that text, but at population scale they're expensive, non-deterministic, and hard to audit. This session shows how specialized medical language models extract and de-identify clinical facts at regulatory-grade accuracy: 98% F1 on PHI detection, and primary site, histology, and tumor staging extracted from unstructured pathology text at regulatory-grade accuracy (over 95%) – all at over 80% lower cost than current frontier models, with deterministic, reproducible output. Those facts become a governed, OMOP-standard real-world-evidence asset, with every value traced to its source note and every extraction carrying a confidence score. With that foundation in place, and a shared MCP boundary on top of it, use cases like cohort building, real-world evidence, clinical trial matching, and protocol design become far easier to build and to audit.
Same Experts, Smarter Agents: The Next Leap in Clinical Development
AI has already reshaped preclinical development — now it's transforming Phase I-III trials. Study start-up is being compressed through AI-driven protocol design, site selection, and feasibility work. Real-world data is enabling synthetic control arms that can reduce the need to enroll patients into placebo groups, cutting study timelines and costs at a point where every day of a Phase III trial can cost upwards of a million dollars. And patient recruitment itself has flipped from a passive, wait-for-patients-to-come model to a proactive one, with AI matching protocol criteria against real-world data to identify eligible patients and where they're being treated — cutting recruitment workload by roughly 80%The next leap is agentic AI: systems that don't just surface insights but actively execute the work. But speed only matters if you can trust the output.In this session, Suzanne will share what "AI-native" really means in practice — where subject matter experts stay firmly in control of agentic workflows, why sourcing and human oversight are the foundation of trustworthy AI rather than an afterthought, and how real-time monitoring is already compressing feedback cycles from days and weeks down to minutes.Attendees will walk away with:
- Where AI is already delivering measurable time and cost savings versus where it's still maturing
- A practical framework for what "AI-native" means for a clinical organization, and how to keep subject matter experts in control as agents take on more of the operational workload
- Questions to ask any AI vendor or internal team about sourcing, hallucination risk, and oversight before trusting agentic output in a regulated trial environment
- A candid perspective on why "trust takes time" with AI — and concrete first steps for piloting agentic tools now rather than waiting for perfect reliability
Unleashing Innovation: From Data to Discovery with AMD + HPE
When Scientists Can't Find Their Data: FAIR Foundations for Real-World AI
How Zifo helped a global biopharma turn disconnected data silos across different platforms into a single FAIR, AI-ready data ecosystem by automating ingestion, data models and metadata management . A practical playbook for building governed, reusable data that scientists trust and AI can actually use.
Digital transformation in biotherapeutics discovery
DNA Methylation-Mediated Epigenetic Adaptation Promotes Cellular Resilience In AML
Navigating launch challenges in rare disease markets
Physics-Informed Large Language Models for Biologics: Applying Nuclear Engineering Rigor to AI Safety and Reliability
Virtual patient generation leveraging RWD to expedite clinical development
Chair's remarks
Chair's remarks
Chair's remarks
Chair's remarks
Chair's remarks
AI Applications: From Clinical Research To Operational Feasibility
Digital Transformation in Biomedical Research: Challenges and Opportunities
From High-Throughput Pipelines to FAIR Data: A Storage Strategy
From Literature to Computable Cohorts: Disease Phenotyping via Multi-Agent Orchestration
Large Language Models in Practice: From Patient Support to Precision Cancer Care
Multi-cancer Early Detection (MCED)
Is your Lab in the Loop?
Edge AI in Production: Real-Time Decision-Making in Sanofi’s Cryo-EM CoreModern cryoEM and other high-throughput scientific instruments generate vast amounts of data, yet mismatched infrastructure often limits throughput, delays scientific insights, and allows poor-quality data to propagate. This session presents a deployment of on-premises edge AI that enables real-time data processing and decision-making directly at the instrument. By detecting anomalies and data quality issues from minute zero, the system ensures only high-value data advances through the pipeline. We will show how near-real-time analytics can also trigger automated device actuation, allowing instruments to adjust and optimize performance dynamically - without manual intervention. This approach reduces data movement and infrastructure costs while accelerating time to insight. Drawing on testing at Sanofi’s cryoEM core, attendees will gain practical insights into architecture, deployment considerations, and measurable impact on throughput, data quality, and operational efficiency.
Operationalizing AI for GxP: What It Takes to Reach Production at Scale
Most pharma organizations can build an AI model. Far fewer can get that model into a validated GxP environment and sustain that validation through inspection, model change control, and ongoing oversight as agentic systems evolve. This session is about the infrastructure, governance, and validation architecture that senior R&D and IT leaders need to operationalize AI across GxP-regulated workflows at enterprise scale. We'll cover what it takes to move an entire portfolio of models and agents into production, not just one successful pilot, and what changes when that foundation is built in from the start rather than added after a model has already been deployed.
The Agentic Trial
Session illustrates how intelligent coordination across planning, monitoring, data review, and study management can improve visibility, predictability, and execution quality.
Unlocked & Validated: How Human-in-the-Loop LLMs Transform Unstructured EHR into High Quality RWD
Real-world data (RWD) is essential to biopharma R&D, but critical signals often remain locked in unstructured data types such as clinical notes, out of reach from standard analytics. Automation alone cannot reliably extract these features; success requires context, and context only emerges when clinical expertise is embedded throughout such workflows.
To capitalize on the phenotypic depth of its EHR-derived RWD, NashBio built a multi-layer LLM extraction system designed around clinical experts who informed extraction criteria, guided prompt and workflow refinements, and evaluated model output against source records to improve capture of clinically meaningful events. Applied to a 2,800 patient inflammatory bowel disease (IBD) cohort, the pipeline surfaced treatment response outcomes from each patient’s IBD clinic notes, spanning a predefined list of 25 medications. The result was more than 58,000 structured medication-response assessments, each substantiated by a verbatim quote from the attending healthcare provider and reviewable in context. This human-in-the-loop architecture achieved >90% accuracy on sampled review and 96% reproducibility – a level of rigor typically reserved for manual chart review – delivered at scale to advance more personalized medicine. Automation alone also missed important documentation patterns unique to specialty care and institutional practice.
NashBio's experience challenges the perception that AI eliminates the need for humans. This work reinforced the fact that, as extraction systems scale, human-in-the-loop matters more, not less; it is what keeps accuracy and context intact. We will also demonstrate how this framework extends to other applications relevant for biopharma R&D, including hepatology feature extraction and biomarker curation.
Using real world data in IBD research
What Makes AI Regulatory Grade? Why LLMs Alone Are Not Enough for Regulated Workflows
Large language models have transformed what is possible in regulatory content creation, but generating fluent text is only the beginning. As the industry moves toward agentic workflows and increasingly automated submission processes, the defining challenge is no longer whether AI can write, but whether its outputs can be trusted.
This session explores what separates regulatory grade AI from general purpose AI, and why successful implementations in regulated environments require more than a language model. Attendees will learn how organizations are combining LLMs with deterministic logic, structured domain knowledge, validation frameworks, and governed human oversight to produce outputs that support the expectations of regulated content development.
Drawing on emerging industry practices and evolving regulatory expectations, the session provides a practical framework for evaluating AI solutions against the capabilities that matter most in regulated environments, including transparency, repeatability, auditability, source traceability, and accountability. Attendees will leave with a concrete framework for assessing whether an AI system is truly designed for regulatory workflows.
Agentic AI for biotech competitive intelligence
AI-Powered Predictive Analytics for Surgical and ICU Patients
AI-powered programmable virtual humans for physiologically based drug discovery
Digital innovations driving clinic trials efficiencies
Pitfalls and Best Practices: Notes from the (research) field
Securing the Agentic Future: How Privacy and Trust Drive Real Digital Transformation
Building Next-Generation Engagement Using the Power of AI - From Concept to Action
- ny AI discussions emphasize the size of the dataset and the cleverness of the model. In practice, those factors are rarely where the real difference is made.
- Medical Simulator helps MSLs practice realistic KOL conversations with AI-powered feedback.
- Institutional Engagement Eagle shows who to engage, why they matter, and how to reach them.
- AI-generated Engagement Plans turn existing research into clear, actionable strategies.
Because all three use the same trusted data, insights flow seamlessly from research to engagementtoaction.
We then turn to the question every team faces: build, or buy? Building in-house is tempting, but the hard parts surface later — curating data you can trust, keeping scarce AI talent, and maintaining infrastructure long after the pilot ends. We make the case for the “smart buy”: a focused partner whose platform, curated data, and proven use cases are ready today — so teams reach value in weeks, not years.
The takeaway: look past the big-data hype, get the build-versus-buy call right, and start with clean data and one problem worth solving. That’s where meaningful AI adoption begins — and where real impact, for your teams and the patients at the end of it, follows.
What actually moves things forward is quieter, and far harder to get right: clean, carefully chosen data, and one problem that genuinely matters in practice.
This session is a behind-the-scenes case study of how Innomagine moved from concept to action. We didn’t start with the technology — we started with the people behind the work, and built a connected suite of engagement solutions on one foundation: the Evidence Eagle platform.
We’ll walk through three AI applications already used every day:
FAIR Data Foundations: Automating Governance and AI-Readiness for Accelerated Drug Discovery
In this session, we'll cover:
- The Modern Data Dilemma in Biotech - massive volumes of unstructured, often fragmented research data, that hinder innovation, impose regulatory risk and missed milestones
- Building a Scalable "Data Foundation" - implementing a unified data governance framework that balances speed, risk and prepares for intelligent content management
- Governance as an AI Enabler - highlighting metadata management to ensure datasets are discoverable, high-quality and protected
- Future-Proofing the Pipeline - the roadmap for AI-driven regulatory intelligence and its role in streamlining future clinical trials
From AI Experiments to Enterprise Impact: What Does It Really Take to Operationalize AI in Pharma
The Evolution of Real-World Evidence: From Individual Data Assets to Connected Healthcare Intelligence
Real-world evidence is evolving from isolated datasets to integrated healthcare intelligence that provides context, not just observations. Learn how Norstella combines multimodal real-world data with proprietary commercial, access, and healthcare intelligence to answer increasingly complex questions that no single nor traditional real-world data source can address, speeding up time to insights.
The Skills Behind the Strategy: Preparing Today's Workforce for Digital Transformation
AI ML for drug discovery
Applications of biological foundation models to accelerate clinical trials
Digital transformation within medical and scientific communications within pharma and biotech
Diving deeper into indications
Medical information response agent
The Human Heart of High-Tech Care: Reimagining the Nurse Navigator in the Age of Precision Medicine
- the field of oncology continues to explorecutting-edge breakthroughs in AI, genomic sequencing, and digital transformation, it’s easy to lose sight of the human being at the center—the patient. While industry leaders focus on the data and the drug, the Oncology Nurse Navigator (ONN) never loses sight of the person behind the diagnosis. This presentation brings the ONN’s story to life as the “human translator” of biotechnology—turning intimidating data into understandable choices, calming fears, and ensuring that the promise of precision medicine is matched by genuine, personalized support.
- Identify the three primary psychological and logistical barriers patients face when transitioning from standard care to high-complexity interventions.
- Analyze how the navigator role mitigates disparities in access to precision medicine by addressing social determinants of health (SDOH) that digital platforms often overlook.
- Formulate a model for integrating biotech stakeholders (pharma, diagnostics, and tech) with the nursing navigation team to ensure innovations enhance, rather than disrupt, the patient experience
Objectives:By the end of this presentation, participants will be able to:
Judges Remarks
The Agent Did It. Now Can You Prove It? Cryptographic Traceability for AI in Clinical Workflows
Responsible AI by Design: How to Launch Agentic AI in Regulated Biotech
AI-Driven Insights from Every single Patient
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Bridging the gap between FEMtech and FEMhealth
Building Bridges: Developing a Common Omics Data Platform at AbbVie
Leveraging Data and AI to transform Research, Care, and Operations at Sylvester Comprehensive Cancer Center
Protecting Our Cognitive Edge: The Paradox of AI Offloading vs. Skills Atrophy
ROI on AI implantation in drug discovery
Enabling LLM-assisted biomedical discovery in secure, trusted agentic environments
Biomedical researchers face a critical productivity gap when working within Trusted Research Environments, where strict security rules ban the use of Large Language Models (LLMs). To solve this, we present the Trusted Agentic Environment, designed to embed AI tools and make them available to researchers by default. By attaching flexible, automated policies directly to datasets, researchers can seamlessly leverage LLMs for code generation, data exploration, and statistical modeling. This integration unlocks complex co-analysis, empowering researchers with advanced AI capabilities to accelerate their scientific output.
LLMs for Medical Affairs: Content, Engagement, and Meaningful Scientific Impact for HCPs
AI-Powered Trials: Unlocking Leadership in Clinical Innovation
Better ways to recruit and retain patients
Digital health and AI, what does the future hold?
From AI Experiments to Enterprise Impact: What Does It Really Take to Operationalize AI in Pharma
The Health Data Trilemma: Privacy, National Security, and Borderless dataflows
Using AI in the drug development and discovery process
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Introductory remarks
Chair's remarks
Biotech in the balance
Evolution of women’s health
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From Synopsis to Shortlist in Seconds: AI-Powered Site Selection & Feasibility
From Innovation to Revenue: Fixing the Commercial Gap in Science-Led
Strong innovation doesn’t automatically lead to commercial success. Many science-led companies struggle to translate capability into revenue. This session introduces a platform that identifies commercial gaps and turnsinnovation into a clear, execution-ready growth strategy.
A Smarter Path to Regulatory-Grade Evidence Synthesis Using Agentic AI
The AI-Native Platform for Drug Development
WittGen: GenAI Drug Response Prediction, Cell by Cell
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AI in medical authoring and regulatory intelligence
Beyond SBIRs: NIH as your technology development and commercialisation partner
Building Contextual Layer for Agentic Framework for Clinical Data Science
Harnessing the Power of Generative AI: Unlocking Insights from Real-World Data for Data-Driven Decisions in Pharma R&D
How AI can help early stage companies in drug development
When Quantum Meets Pharma: Inside the Race to Design Drugs Atom by Atom
From Hours to Minutes: Reimagining Site Operations with AI
Impact of insilico innovations on drug discovery and development
Multi-modal real world data foundation models: Challenges & opportunities
Structuring high-consequence decisions from discovery to the clinic
AI to select biomarker candidates
Driving Innovation in Oncological care with Radiopharmaceuticals
Maximizing the Value of Real-World Evidence for Patient Safety
As the pharmaceutical industry becomes increasingly data-driven, organizations are generating more real-world data (RWD) and real-world evidence (RWE) than ever before. While RWE presents significant opportunities to enhance patient safety, realizing its full value requires navigating operational, methodological, and regulatory complexities.
Realizing this value requires strong collaboration between patient safety and RWE functions to ensure evidence is appropriately contextualized and translated into meaningful action.
Drawing on practical examples and lessons learned, this session explores the opportunities and challenges at the intersection of patient safety and RWE.
Attendees will gain insights into approaches for strengthening cross-functional collaboration, leveraging RWE more effectively, and improving confidence in the safe use of medicines.
Running Large Language Models on AWS: Bedrock, SageMaker, and Purpose-Built AI Infrastructure
Virtual cell modelling
How Novo Nordisk Transformed Customer targeting using AI
Identifying Tumour Biomarkers Using Real World Data
Leveraging Real-World Data and AI for the Evaluation of Clinical Trial Efficacy
Background:Traditional clinical trial evaluations heavily rely on the Average Treatment Effect (ATE), a metric that can obscure substantial heterogeneity in individual patient responses. This limitation is magnified in observational real-world data (RWD), where simple statistical associations often conflate correlation with systemic selection bias. To enhance regulatory confidence and optimize therapeutic positioning, clinical research must move beyond purely associative analyses and shift toward isolating true counterfactual causation by precisely estimating Individual Treatment Effects (ITE) to enable personalized treatment selection.
Methods:This study presents a deep causal learning framework that adjusts for high-dimensional baseline confounding. Utilizing RWD from a diverse clinical cohort in which more than 80% of participants originate from communities historically underrepresented in biomedical research, the pipeline maps longitudinal, time-fixed, and time-varying covariate vectors into a dense latent space via a pre-trained Med-BERT transformer encoder. These phenotypic embeddings parameterize an integrated causal neural network architecture that features an objective-focused propensity score gate and dual counterfactual potential-outcome heads. To evaluate individual treatment benefit under non-proportional hazards, the architecture maps continuous counterfactual survival curves for patients within the balanced common support region where the propensity score is approximately 0.5. Rather than collapsing temporal dynamics into static metrics, the framework evaluates the global geometry of these curves using non-parametric omnibus tests to isolate and rank an optimized sub-cohort of the top ntreatment responders.
Results:Validation audits demonstrated exceptional predictive fidelity, with the outcome survival network achieving a baseline Concordance Index of 0.9439 ± 0.0771. The causal estimation engine yielded a statistically significant Conditional Average Treatment Effect (CATE) point estimate of 0.6205 (95% Confidence Interval: 0.5934 to 0.6476), with structural validity confirmed by a Leave-One-Out (LOO) cohort stability index of 99.71%. The integrated KONP and multi-directional screening framework successfully isolated the optimized high-responder cohort from the balanced common-support distribution.
Conclusion:Shifting from associative correlation to robust counterfactual inference on highly diverse RWD provides a scalable, causal methodology for predicting personalized treatment response, optimizing trial cohort enrichment strategies, and accelerating precision evidence generation without modifying active clinical trajectories.
AI-driven phenotyping system that extracts operational definitions of patient cohorts
How to integrate multi-omics
Leveraging NIH as your commercialization partner
Leveraging RWD in the Drug Development Process
Mind the Gap: How Artificial Intelligence Can Strengthen the Clinical Trial Enterprise from Protocol Design to External Controls
Revolutionizing immunotherapy through patient-centric therapeutic hardware platform, ATT-AI
Chair's remarks
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AI influenced nano technology in drug discovery and development
Detecting Dengue, Chikungunya, and Oropouche Viruses Using Radio Frequency Waves and Machine Learning.
Driving field medical innovation
How AI is used to support the three R’s in drug discovery
Sponsored presentation
Not All Bias Breaks Decisions: Relative Ignorability for Real-World Evidence
Preparing the Biotech Workforce of the Future: The Role of Academic-Industry Partnerships
Regulatory affairs, The regulations and guidance globally
Pediatric Precision Medicine
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