2026 ARCHIVES

AI for Oncology, Precision Medicine & Health

Transform Multimodal Data into Clinical Impact with Trusted AI

May 20 - 21, 2026

The AI for Oncology, Precision Medicine & Health track highlights how next-generation AI is reshaping cancer research, clinical decision-making, and patient care. In 2025, we focused on applying AI to bridge gaps in real-world data and drive early advances in precision medicine. In 2026, we shift to operationalization and validation. Sessions will cover multi-modal fusion (pathology, radiology, omics, clinical notes), foundation models for imaging and pathology, and AI-enabled clinical trials for trial matching and cohort selection. We’ll also explore digital twins for oncology, bias/fairness in clinical AI, and regulatory readiness with explainable AI and validation benchmarks. Designed for oncologists, data scientists, and health system leaders, this track delivers both the scientific depth and the practical playbooks needed to responsibly integrate AI into oncology workflows—accelerating precision medicine while ensuring equity, trust, and clinical impact.

Tuesday, May 19

8:30 amRecommended Pre-Conference Workshops and Symposia*

On Tuesday, May 19, 2026, Cambridge Healthtech Institute is pleased to offer six pre-conference Workshops scheduled across two time slots (9:00 am–12:00 pm and 1:15–4:15 pm) and three Symposia from 8:30 am–3:45 pm. All are designed to be instructional, interactive, and provide in-depth information on a specific topic. They allow for one-on-one interaction and provide a great way to explain more technical aspects that would otherwise not be covered during the main conference tracks that take place Wednesday–Thursday.

*Separate registration required. Additional details:

Symposia: www.bio-itworldexpo.com/symposia

Workshops: www.bio-itworldexpo.com/workshops

4:30 pm

Grab Your Seat! 25th Annual Golden Ticket Prize Giveaway & Organizer’s Remarks*

Cindy Crowninshield, Executive Event Director, Cambridge Healthtech Institute

*Must be present to win.

4:35 pm

Welcome Remarks from the City of Boston and the Office of Mayor Michelle Wu

Donald Wright, Interim Chief, Economic Opportunity & Inclusion Cabinet, City of Boston

4:40 pm PLENARY KEYNOTE INTRODUCTION:

Getting Ready for Effective AI: Starting with FAIR Principles

Diana Gamez Diaz, Head, Data & AI Engineering, RCH Solutions

4:50 pm PLENARY KEYNOTE PRESENTATION:

Rare Conversations: Explorations of the Research, Funding, and Advocacy for Rare Diseases

Thomas Bartlett, Ambassador, MG Uniter Myasthenia Gravis, Amgen

Catherine Brownstein, PhD, Manager, Molecular Genomics Core Facility, Boston Children's Hospital; Scientific Director, Manton Center for Orphan Disease Research Gene Discovery Core; Assistant Professor, Harvard Medical School

Morgan Cheatham, MD, Partner, Head of Healthcare & Life Sciences, Breyer Capital

Sebastien Lefebvre, Head of Technology, Data and AI, Aurelis Insights

Dylan V. Livingston, Founder and President, The Alliance for Longevity Initiatives (A4LI)

William Van Etten, PhD, Co-Founder, CEO & Principal Scientist, StarfleetBio

Susan J. Ward, PhD, Founder & Executive Director, cTAP

In a unique plenary series of intimate conversations, we will explore the models, drivers, and challenges facing rare disease research. By uniting leaders in precision medicine, bioinformatics, national rare-disease infrastructure, and real-world legislative advocacy, we will give attendees an expansive, cross-disciplinary view of what’s required to deliver faster, more accurate, and more equitable rare-disease cures.

6:00 pmWelcome Reception & 25th Anniversary Celebration in the Exhibit Hall with Poster Viewing

The Bio-IT Kickoff Reception is a reunion, reconnect with friends, explore cutting-edge research, and celebrate innovation! This year, join us in celebrating the 25th Anniversary of Bio-IT World Conference & Expo with cake and champagne as we mark a quarter century of advancing science and technology. Enjoy poster presentations, networking, and vote for the Best of Show and Poster awards.

7:15 pmClose of Day

Wednesday, May 20

6:30 amBio-IT World’s 5K Rise and Shine Fun Run! (Sponsorship Opportunities Available)

RUN COORDINATORS:
Bridget Kotelly, Senior Conference Director, Cambridge Healthtech Institute
Eileen Murphy, Conference Producer, Cambridge Healthtech Institute

Lace up and join Bio-IT’s Coordinators for the Fun Run on Wednesday, May 20! Sprint, jog, walk, or talk-your-way-through—ALL abilities are welcome. This informal event is all about getting moving together. Full details to come…just don’t forget your sneakers!

7:00 amRegistration and Morning Coffee

8:00 am

Grab Your Seat! 25th Annual Golden Ticket Prize Giveaway & Organizer’s Remarks*

Allison Proffitt, Editorial Director, Bio-IT World and Clinical Research News

*Must be present to win.

8:05 am PLENARY KEYNOTE INTRODUCTION:

From Tools to AI Teammates: How AI-Driven Scientific Workbenches Can Redefine the Scientist User Experience

Sayan Lahiri, Vice President, CLOVERTEX

Life sciences R&D is drowning in powerful tools, massive data, and advanced infrastructure—yet scientists still spend too much time managing environments, rerunning workflows, and navigating fragmented systems. The real bottleneck is no longer data or compute; it is the scientist’s user experience. In this plenary, we explore how Clovertex’s AI-driven scientific workbench called NUMEN is changing the role of technology from passive infrastructure to an active scientific collaborator. By embedding AI directly into the workbench, these platforms guide scientists in choosing the right compute, optimizing cost, enforcing reproducibility, tracking metadata, and scaling experiments seamlessly from exploration to production—all while governance and security remain invisible. AI-powered workbenches create a shared operating model where science moves faster, results are trusted, and innovation scales. The future of life sciences R&D will not be defined by more tools—but by intelligent platforms that work like a teammate alongside scientists.

8:15 am PLENARY KEYNOTE PRESENTATION:

The Collaboration Breakthrough: How Federated Learning Is Rewriting the Rules of Drug Discovery

Mohammed AlQuraishi, PhD, Assistant Professor, Systems Biology, Columbia University

Jonathan B. Gilbert, PhD, Senior Director, Ecosystem Growth and Contributor Partnerships, Eli Lilly and Company

José-Tomás Prieto, PhD, Director of AI Programs, Apheris

Woody Sherman, PhD, Founder and Chief Innovation Officer, PsiThera

Christina Taylor, PhD, Senior Science Fellow and Computational Molecular Design Lead, Bayer

Arman Zaribafiyan, PhD, Head of Strategic Alliances, AI Simulation, SandboxAQ

The pharmaceutical industry sits on a collective treasure trove of proprietary structural biology data, yet competitive concerns have historically prevented the data sharing necessary to train the most powerful AI models for drug discovery. Federated learning is changing this paradigm, enabling biopharma companies to collaborate on AI model training while keeping sensitive data secure and confidential. This plenary session explores the groundbreaking AI Structural Biology (AISB) Network, where industry leaders are pooling proprietary protein-ligand structure data to collaboratively train OpenFold3, an AI model designed to predict molecular interactions with precision approaching X-ray crystallography. Through the federated computing platform, thousands of experimentally determined protein–small molecule structures remain securely at their original locations while contributing to a shared learning framework that no single organization could achieve alone. This session reveals how federated learning solves the industry's most persistent challenge: unlocking collective intelligence while protecting intellectual property. Attendees will hear directly from consortium leaders about:

The technical architecture enabling privacy-preserving collaborative AI training across competing organizations
Real-world implementation of federated learning platforms and computational governance frameworks
Strategic rationale for industry collaboration: why sharing model training beats going it alone
Impact and outcomes from early OpenFold3 results in predicting binding affinities and accelerating small molecule discovery
The future of collaborative AI in biopharma, from structural biology to clinical development

9:30 amCoffee Break in the Exhibit Hall with Poster Viewing (Sponsorship Opportunity Available)

Start your morning with coffee, connections, and cutting-edge research! Enjoy poster presentations, network in the Exhibit Hall, vote for awards, and a chance at a fabulous raffle prize!

10:15 am

Organizer's Welcome Remarks

Marianne McGonagle, Room Operations Lead, Cambridge Healthtech Institute

10:20 am Chairperson's Remarks

Vivek Adarsh, Co-Founder & CEO, Mithrl

10:25 am

Spatial AI Platform for Precision Biomarker Discovery in Tumor Microenvironments

Sandeep Singhal, PhD, Associate Professor, Pathology, University of North Dakota

We developed a containerized deep learning pipeline for automated segmentation and classification of tumor, immune, and stromal cells from whole-transcriptome spatial imaging data. By integrating per-cell transcriptomic, morphological, and spatial features, the framework generates harmonized, high-confidence single-cell annotations across sites. These outputs enable robust spatial and topological biomarker analyses to advance precision oncology.

10:55 am

MethylFM: A DNA Methylation Foundation Model for Modeling Epigenomic Regulatory Dynamics

Xiang Chen, PhD, Associated Member, Computational Biology, St. Jude Children's Research Hospital

MethylFM is a transformer-based foundation model to capture context-aware methylation patterns and to enable multiple downstream tasks. Our work addresses the challenge of leveraging WGBS data to uncover relationships between methylation, histone modifications, and cellular identity, with applications in disease biomarker discovery and therapeutic development.

11:25 am

Bridging AI Predictions and Biological Reality: Experimental Validation Frameworks for Genetic Diseases

Chris Dayton, CoFounder & CEO, Quality Assured AI

Marianna Weener, MD, PhD, Senior Researcher, Broad Institute of MIT and Harvard

As AI models (AlphaMissense, AlphaGenome etc) variant interpretation algorithms increasingly shape genomic research, their predictions remain probabilistic—powerful but unverified. This talk presents a practical framework for experimentally validating AI-predicted pathogenic variants using high-throughput splicing assays (HTSA), multimodal genomic data, and clinical correlations from 15,000+ patient registry. Attendees will learn how integrating AI-driven insights and real-world patient data transforms variant hypotheses into actionable, clinically credible conclusions.

11:55 am

The Evidence AI Can't Find: How Systematic Literature Curation Outperforms AI Retrieval for Precision Drug Development

Joe Jacher, MSC, CGC, Genomenon

AI tools are fast, but they miss critical evidence in published biomedical literature. Joe Jacher shares data from rare-disease and precision-oncology programs showing the gap: expert curation identified 83% more PRKAG2 patients than ChatGPT and OpenEvidence combined, and expanded clinically actionable GLA variant coverage by 129% over ClinVar. For pharma teams focused on precision drug development, systematically curated published evidence is not optional and cannot be replaced by AI alone.

12:10 pm

From Design to Execution: How Causal AI Is Transforming Clinical Development

Raviv Pryluk, CEO & Co-Founder, PhaseV Trials, Inc.

Clinical development remains fragmented across indication selection, design, execution, and analysis. PhaseV's causal AI moves teams from correlation to causation, delivering up to 50% lower development cost, 40% trial acceleration, and 40% reduced enrollment by identifying true responders, optimizing trial design, and selecting higher-performing sites for the specific protocol and MoA. Built on 80+ validated models and trusted by 45+ biopharma sponsors, PhaseV is redefining how clinical trials are designed and executed.

12:40 pm

Cell-Level Prediction of Pathway Activity in Cancer Single Cell Data

Joseph Pearson, Director, Global Product Management OmicSoft, QIAGEN

Translating scRNA-seq tumor cell heterogeneity into pathway‑level insights remains difficult. In this presentation, we'll explore how to use embedding vectors derived from directional bipartite gene signatures to predict cell-level pathway activity. Robust inferences into pathway activity can be made with embeddings based on curated biological knowledge. This allows direct investigation of pathways across tumors to uncover biologically meaningful structure in scRNA-seq data.

12:55 pmTransition to Lunch

1:05 pm

LUNCHEON PRESENTATION: Transforming Cancer Care: AI-Driven Molecular Tumor Boards for Enhanced Patient Outcomes

Sanjay Jaiswal, Principal, Data & AI, Ernst & Young LLP

Leveraging AI for molecular tumor boards across diverse data sets and interdisciplinary workflows can revolutionize cancer treatment by automating clinical summarization, enhancing decision-making, and optimizing personalized therapy and clinical-trial matching for patients. This presentation will showcase a real-world case study from a leading cancer-healthcare system, detailing the business case, implementation strategies, and key lessons learned. Attendees will discover how this approach achieved 30% to 50% reductions in costs and time, while delivering more comprehensive, effective, and safe cancer care, all with a human-in-the-loop framework.

1:35 pmRefreshment Break in the Exhibit Hall with Poster Viewing (Sponsorship Opportunity Available)

Bio-IT's hall is bigger than ever; one break won’t cut it! Enjoy dessert and coffee after lunch, explore booths and posters, vote for awards, and participate in our raffle for a chance to win a prize!

2:25 pm

Chairperson's Remarks

Karen Weisinger, PhD, CSO, IQUrium

2:30 pm

More Data, Less Insight: Why Scientific AI Needs a New Framework

Vivek Adarsh, Co-Founder & CEO, Mithrl

The multiomics data stack in drug discovery has never been richer or harder to act on. For computational and research teams in pharma and biotech, the real bottleneck is no longer generating data but generating defensible conclusions from it, at the speed programs demand. Eos, Mithrl's scientific decision engine, was built with computational and informatics teams, not around them. It runs validated multiomics analyses on demand, orchestrating the pipelines, QC, and statistical methods your teams would otherwise stand up by hand, and grounds every result in the underlying data and literature. Outputs are reproducible, fully traceable, and audit-ready by construction. This session shows how embedding informatics rigor into AI reasoning compresses time-to-insight from weeks to hours, and what that means for how discovery teams make go/no-go decisions.

3:00 pm

AI/ML for Biomedical Technologies Development for Individualized Patient Diagnostics

Umer Hassan, PhD, Associate Professor, Electrical & Computer Engineering, Rutgers University

Next-generation biomedical technologies for individualized diagnostics and longitudinal patient monitoring increasingly embed AI and machine learning directly into the devices to enhance accuracy, robustness, and clinical utility. By combining novel biomarker data from these sensors with hospital EHR systems, we can enable advanced, truly personalized patient monitoring platforms. This talk will highlight recent work from Dr. Hassan’s research laboratory in developing such AI-enabled biomedical sensing technologies.

3:30 pm

Integrating AI and Molecular Biomarkers for Precision Suicide Prevention: The Proteus-AI and cf-mtDNA Framework

Arpitha Parthasarathy, PhD, MBA, Clinical Health Scientist, Behavioral & Mental Health, VA Caribbean Healthcare System

AI in healthcare has accelerated dramatically, transforming radiology, genomics, and oncology, but psychiatry has remained largely untouched by this precision-medicine revolution. Existing suicide-risk algorithms focus on static, historical predictors and yield probabilistic scores that clinicians describe as “informative but not actionable.” At the same time, breakthroughs in liquid biopsies and mitochondrial DNA (cf-mtDNA) biomarkers are revealing that psychiatric crises may have measurable molecular signatures. Yet, no current platform meaningfully integrates biological and computational signals into a clinician-usable, real-time decision support system.

This talk introduces Proteus-AI, a new precision-behavioral-health framework designed to bridge that gap. This talk will synthesize current evidence from AI/ML in psychiatry, digital phenotyping, cf-mtDNA biomarker research, and implementation science. The Proteus-AI framework layers:
1) Explainable AI trained on longitudinal EHR trajectories; 2) Behavioral-health deterioration detection rather than static suicide prediction; and 3) Mitochondrial cell-free DNA and autoantibody biomarker signals to ground psychiatric risk in measurable biology.

The talk will examine how AI can be integrated into clinical workflow, how explainability builds clinician trust, and how ethical guardrails ensure responsible deployment among high-risk Veterans/civilians. By reframing AI not as a black box but as a transparent interventional tool. Proteus-AI aims to redefine precision psychiatry for suicide prevention, moving from prediction to prevention.

Audience members will leave with: 1) A clear view of global trends in psychiatric AI and precision biomarkers, 2) An implementation blueprint for integrating AI/ML into EHR workflows, and 3) A vision for how mental health can finally achieve what oncology has already demonstrated: precision medicine that saves lives.

4:00 pm

Transforming Precision Medicine: AI-Driven Biomarker Discovery and Patient Stratification in Data-Constrained Environments

Jannick Bendtsen, Vice President of Bioinformatics and Data Science, Bioinformatics, Excelra

Identifying predictive biomarkers for patient stratification remains a critical challenge in rare and heterogeneous malignancies. Artificial intelligence (AI) and machine learning (ML)-driven approaches that integrate multi-modal molecular and clinical data can effectively predict treatment response and enable data-driven patient stratification. These approaches employ imputation, feature selection, regularization, and cross-validation to identify distinct molecular signatures associated with treatment response, stratify patients into likely responders and non-responders, and generate biologically relevant hypotheses on mechanisms of action and resistance. These findings highlight the transformative potential of AI in oncology, supporting the feasibility of ML-driven biomarker discovery and providing a scalable framework to advance clinical trial design, patient stratification, and precision medicine in data-constrained environments.

4:15 pm

From Literature Search to Living Model: Causal Reasoning for Oncology Target Nomination

Andreas Stuhlmuller, Co-Founder and CEO, Elicit

Despite billions in R&D investment, oncology target selection remains remarkably qualitative. We describe an agentic AI system that replaces narrative target reviews with a living causal model of viability, spanning mechanism, drugability, toxicity, competitive landscape, and patient selection. Applied to a molecular glue ADC payload for TNBC, it integrated multimodal evidence, surfaced contradictory published claims, and generated auditable artifacts, updating its model as new evidence arrives.

4:30 pmBest of Show Awards Reception in the Exhibit Hall with Poster Viewing

Unwind with colleagues at our lively reception! Explore posters, vote for the best, network with exhibitors, enjoy a drink, and try to win a raffle prize. Celebrate Best of Show winners!

5:45 pmClose of Day

Thursday, May 21

7:00 amRegistration Open

7:00 amConnect & Collaborate: Breakfast Networking Roundtables (Sponsorship Opportunities Available)

Start the day with small-group roundtable discussions designed to spark collaboration and exchange insights across the Bio-IT community. Attendees join themed tables—spanning AI, data ecosystems, foundational models, and more—for focused, peer-driven discussions that foster problem-solving, connection, and cross-functional perspectives ahead of the plenary keynote.

IN-PERSON ONLY –

TABLE 1: Knowledge Graphs

Tom Plasterer, PhD, CEO & Co-Founder, Knowledge3

How are knowledge graphs supporting reliable scientific intelligence
Lessons from early deployments
Remaining challenges and barriers to scale

IN-PERSON ONLY –

TABLE 2: From Molecules to Qubits: A Collaborative Conversation on Pharma’s Quantum Future

Christopher Bishop, Chief Reinvention Officer, Improvising Careers

Learn how leading global pharma companies are applying quantum principles to real-world research.
Discover the quantum companies transforming traditional processes around drug development and drug discovery.
Discuss how quantum, along with HPC and AI, is poised to help researchers tackle historically intractable problems and potentially find new treatments for diseases like cancer, Alzheimer’s, and diabetes.

IN-PERSON ONLY –

TABLE 3: From AI Tools to Autonomous Discovery: Are We Ready for Agentic AI in Drug Discovery?

Parthiban Srinivasan, PhD, Professor and Director, Centre for AI in Medicine, Vinayaka Mission's Research Foundation, India

Where are we today? Are AI tools truly integrated into workflows, or still operating in silos?
What changes with agents? How do LLM-based agents shift drug discovery from prediction to decision-making?
What is blocking autonomy? Data quality, validation, trust, or organizational readiness for AI-driven discovery?

IN-PERSON ONLY –

TABLE 4: Bridging Tech Transfer and Industry: Unlocking Real Collaboration at Bio-IT

Nancy Wetherbee, Director Commercialization, Research Innovation Center, Northeastern University

Where are the highest-value collaboration opportunities between academia/TTOs and industry (biopharma, startups, AI/data, investors), and what makes them actually work?
What types of partnerships are most effective in practice (licensing, co-development, startup creation, sponsored research), and how do both sides evaluate quality and fit quickly?
What specific formats, access points, or structures at the Bio-IT event would actually drive follow-up, partnerships, and deal-making?

IN-PERSON ONLY –

TABLE 5: Supporting Innovation While Managing Risk: The CIO’s Dilemma in AI-Driven R&D

Chris Dwan, Fractional CIO, Triveni Bio

How do CIOs enable rapid AI and data innovation without breaking governance, compliance, or data integrity frameworks?
What separates pilot-stage innovation from scalable, production-grade systems? Where do most organizations fail?
How do leaders manage emerging risks (model bias, data provenance, regulatory exposure) while still pushing competitive advantage?

IN-PERSON ONLY –

TABLE 6: Real-World Data and Evidence: Unlocking Value Beyond Clinical Trials

Michael Liebman, PhD, Managing Director, IPQ Analytics, LLC

How real-world data is being used alongside clinical and experimental data
Challenges in standardization, access, and regulatory acceptance
Opportunities to improve outcomes, access, and long-term patient insights

IN-PERSON ONLY –

TABLE 7: Beyond Data Management: Why AI Fails without Institutional Memory in Life Sciences

Alexandra Brocato, CEO & Co-Founder, Beakr, Inc.

Why critical experimental knowledge is lost across ELNs, LIMS, and siloed teams, and the cost of reinventing work
How structured experimental memory makes past decisions, failures, and context reusable across teams
How organizations can make scientific knowledge reusable for both humans and AI

IN-PERSON ONLY –

TABLE 8: Intellectual Property in Biotech: Strategy, Patents, and Trademarks

Elizabeth F. Jackson, Acting Director, Northeast Regional Outreach Office, U.S. Patent and Trademark Office

How to think strategically about IP early in biotech and AI-driven research
Common pitfalls in patent and trademark applications and how to avoid them
Navigating ownership, protection, and commercialization of innovations

IN-PERSON ONLY –

TABLE 9: Are Bioinformatics Workflows Broken? Rethinking Pipelines in the Age of No-Code and AI

Daniel Clarke, Biomedical Software Developer, Icahn School of Medicine at Mount Sinai

Why do current workflow systems fail most scientists in practice?
Can no-code and AI-driven workflows meet standards for reproducibility, validation, and clinical readiness?
What would a “production-ready” workflow ecosystem actually look like across teams and organizations?

IN-PERSON ONLY –

TABLE 10: Data Readiness for AI: Why Most AI Programs Fail Before They Start

Bahador Marzban, PhD, Principal Data Scientist, Innovative Medicine R&D, Johnson & Johnson

The gap between AI ambition and the reality of data foundations, platforms, and operating models
What “AI‑ready data” actually means in practice—beyond pilots, dashboards, and proofs of concept
Where AI initiatives most often break down: data integration, data quality, metadata, and governance at scale

8:00 am

Grab Your Seat! 25th Annual Golden Ticket Prize Giveaway & Organizer’s Remarks*

Cindy Crowninshield, Executive Event Director, Cambridge Healthtech Institute

*Must be present to win.

8:05 am

Bio-IT World 2026 Innovative Practices Awards Ceremony (Winners Announced)

Allison Proffitt, Editorial Director, Bio-IT World and Clinical Research News

Since 2003, Bio-IT World’s Innovative Practices Awards have recognized outstanding technology innovation advancing life sciences research. The 2026 winners highlight excellence in open science, patient advocacy, global data access, and real-world AI through collaborations involving Arizona State University and Starfish Storage, CareDx, ASAP Discovery Consortium, and Novartis with Genedata AG. Together, these projects illustrate the modern R&D data lifecycle, from unlocking legacy data to enabling collaboration and driving clinical decision-making.

8:25 am

Bio-IT World 2026 Emerging Innovator Award—NEW (Winner Announced)

Allison Proffitt, Editorial Director, Bio-IT World and Clinical Research News

The Emerging Innovator Award recognizes one exceptional early-career researcher advancing the future of life sciences through breakthrough work in biomedical data, computational methods, or technology-enabled discovery. The 2026 awardee will deliver a 20-minute plenary keynote at Bio-IT World, highlighting the impact of their research and the forward-looking direction of their work.

8:35 am

EMERGING INNOVATOR AWARD PRESENTATION: Scalable Connectomics for AI-Ready Brain Data

Ons M'Saad, PhD, Co-Founder & CEO, panluminate Inc.

Mapping the brain at synaptic resolution—connectomics—could transform neuroscience, medicine, and AI. Until now, the field has depended on electron microscopy: slow, expensive, difficult to scale, and limited to structure alone. Dr. Ons M’Saad presents a new optical approach that adds molecular context to large-scale brain maps, opening new ways to study neurodegenerative disease and inform more biologically grounded AI.

8:55 am PLENARY KEYNOTE INTRODUCTION:

Trusted Data, Accelerated Science: Building a Context-First Data Foundation for AI in BioPharma

Scott Weiss, Vice President, Product & Strategy, IDBS

As AI reshapes BioPharma R&D, the biggest barrier isn’t the model—it’s the data. Experimental and process data remains fragmented across spreadsheets and siloed systems, often stripped of scientific context. In this introduction, Scott Weiss, VP Product & Strategy at IDBS, argues that a context-first data foundation is essential to AI success, showing how unified, traceable lab data becomes AI-ready, GxP-compliant, and accelerates confident decision-making.

9:05 am PLENARY KEYNOTE PRESENTATION:

Generative AI across Drug Discovery Tasks

Jeremy L. Jenkins, PhD, US Head, Discovery Sciences, Novartis BioMedical Research

Many steps in drug discovery are informed by large-scale biological and chemical data, from genomics to the chemical universe, to phenotypic cell profiling. Generative-ML models are increasingly being deployed across these domains, including single-cell foundation models for target discovery, generative chemistry for rapid ligand design, and transfer learning to accelerate image analysis. Practical applications of generative AI in early drug discovery will be described, including simulated functional-genomics screens with in silico perturbations; compound design conditioned on protein pockets; and in silico-labeling approaches that replace traditional image staining. Together, these advances illustrate how generative AI is transforming how drug discovery research is conducted.

9:45 amCoffee Break in the Exhibit Hall with Poster Competition Winners Announced (Sponsorship Opportunity Available)

Bio-IT is all about connections! Explore booths, award-winning posters, and network with clients, colleagues, and exhibitors. Grab coffee, build relationships, and stay for a chance to win a raffle prize!

10:30 am

Organizer's Remarks

Marianne McGonagle, Room Operations Lead, Cambridge Healthtech Institute

10:35 am

Chairperson's Remarks

Nanguneri R. Nirmala, PhD, Co-Founder and CEO, Vindhya Data Science

10:40 am

From Reliability to Clinical Assurance: Predictive Risk Scoring for Healthcare AI Systems

Jeevan Kumar Goud Bandharapu, AI Reliability & Predictive Systems Architect, Independent Researcher & Consultant

As AI becomes embedded in radiology, triage, population health, and operational decision support, ensuring its clinical dependability is essential. This session introduces a predictive assurance framework that scores AI systems based on drift, anomaly likelihood, workflow sensitivity, and safety impact. The approach combines telemetry-driven monitoring with explainable auditing aligned to NIST AI RMF and emerging FDA expectations. Validated in enterprise healthcare environments, it improves early detection of reliability degradation and strengthens collaboration between data science, clinical, and compliance teams. Participants will learn actionable techniques for deploying trustworthy, regulated-grade AI.

11:10 am

Advancing Oncology and Precision Medicine with Biomedical Digital Twins: AI-Driven Insights for Predictive and Personalized Care

Heiko Enderling, PhD, FMSB, Professor, Radiation Oncology, MD Anderson Cancer Center

Tina Hernandez-Boussard, PhD, Associate Dean of Research and Professor of Medicine (Biomedical Informatics), Biomedical Data Sciences, Surgery and Epidemiology & Population Health, Stanford University

Matt Mahoney, Principal Computational Scientist, The Jackson Laboratory

Eric Stahlberg, PhD, Executive Administrative Director, Institute for Data Science in Oncology, MD Anderson Cancer Center

Biomedical digital twins are redefining oncology and precision medicine by integrating multi-modal patient data, AI-driven modeling, and predictive analytics. This session explores how digital twins enhance disease prediction, treatment optimization, and clinical decision-making in oncology and biopharma. Learn how AI-enabled twin models are addressing data and validation challenges while paving the way for more adaptive, personalized, and patient-centered care pathways.

12:10 pm

Building a Measurable Evidence Engine for Oncology: Turning Multimodal Data into Clinical Impact with Trusted AI

Todd Oakland, Senior Vice President, General Manager, Biopharma & Diagnostics, DNAnexus

As oncology programs advance through clinical development, teams face mounting challenges assembling consistent, traceable evidence from increasingly complex multimodal oncology data. This session will introduce a measurable evidence approach to replace disconnected workflows and support decision-making at scale, using structured data ingestion and governance to enable multimodal fusion in trusted research environments. It will also cover how standardized AI/ML workflows accelerate exploration, support AI-enabled trial matching and reproducible analysis, advancing precision medicine while ensuring regulatory readiness and clinical impact.

12:40 pm

Turn Institutional Memory into Action

Shakthi Kumar, Chief Strategy and Business Officer, EDETEK Inc.

Clinical development knowledge remains trapped in unstructured protocols and amendments, forcing every study to relearn risk and intent. This session presents a neuro‑symbolic Digital Data Flow solution that converts documents into USDM‑compliant, executable clinical intelligence using a digitization core and thin AI engines. The result is inspection‑ready AI at scale.

1:10 pmSession Break and Transition to Lunch

1:20 pmEnjoy Lunch on Your Own

1:50 pmRefreshment Break in the Exhibit Hall with Poster Viewing (Sponsorship Opportunity Available)

Feeling tired? Recharge during the final Networking Exhibit Hall break! Visit booths, explore posters, connect with peers, and turn in your Game Cards for a chance to win a raffle prize.

2:30 pm

Chairperson's Remarks

Grant Stephen, CEO & Co-Founder, bPrescient, Inc.

2:35 pm

Three Steps to Success: Framework for Optimizing AI’s Impact on Drug Development

Umut Eser, PhD, Head, Generative AI, Johnson & Johnson Innovative Medicine

Grant Stephen, CEO & Co-Founder, bPrescient, Inc.

Based on extensive research, this presentation explores the three leading ways AI can accelerate the drug development process while substantially reducing costs. We provide a framework for considering how to deploy these capabilities in research and clinical while setting out (i.) the supporting evidence base and (ii.) the remaining challenges to be overcome. Our priority is to provide the audience with practical ways to positively impact their own drug development processes.

3:05 pm PANEL DISCUSSION:

Beyond Discovery: Turning AI Insights into Real-World Clinical Impact

PANEL MODERATOR:

Grant Stephen, CEO & Co-Founder, bPrescient, Inc.

PANELISTS:

Anastasia Christianson, PhD, Pharma Industry Data Science Leader

Charmaine Demanuele, PhD, Vice President, R&D Data Science & Digital Health - Neuroscience, Johnson & Johnson Innovative Medicine

Arturo J. Morales, PhD, Vice President, Technology Enabled Science, CSL Behring

Alexander Sherman, Director, Center for Innovation and Bioinformatics, Massachusetts General Hospital

Nagaraja "Sri" Srivatsan, CEO, Endpoint Clinical, Inc.

While AI dominates early-stage drug discovery discussions, pharmaceutical organizations struggle to connect these innovations to downstream clinical operations and real-world patient care. This panel tackles three critical gaps in current AI implementation: 1) integrating AI across R&D functions from target identification through clinical development, 2) leveraging digital health platforms and AI as the bridge between research insights and patient outcomes, and 3) enabling cross-team collaboration and external partnerships through shared AI infrastructure. Industry leaders from biopharma, hospitals, and digital health will debate what it really takes to break down silos between discovery, development, clinical, and commercial teams. Through candid discussion of real implementations, panelists will explore how to build communication channels that connect internal teams with external partners (hospitals, payers, and patients), creating an AI ecosystem that delivers measurable impact across the full therapeutic lifecycle.

4:05 pmClose of Conference