Program

Stop by the registration table to pick up your badge and program.

Majid Afshar

Associate Professor, University of Wisconsin–Madison
Director, Learning Health System

Mary Beth Hamel

Executive Editor, New England Journal of Medicine

Patrick J. Heagerty

Professor, University of Washington

This session will discuss various clinical trial designs to generate robust scientific and statistical evidence of the clinical impact of an AI model. The session will begin with an overview of trial design for clinical AI models, cover advanced study designs such as stepped-wedge and cluster-randomized designs, and highlight these principles in real-world case studies, including the evaluation of AI scribes.

Samuel Finlayson

Resident, University of WA and Seattle Children’s Hospital

Even after receiving a genetic diagnosis, many rare disease patients face a prolonged and uncertain search for viable treatments. The Therapeutic Actionability AI Challenge invites teams to confront this gap by building automated systems, leveraging LLM agents and related AI technologies, that transform a patient’s genetic diagnosis into a personalized therapeutic actionability report. The competition unfolds in two phases: first, a question-answering phase built on a curated benchmark of challenging, real-world therapeutic decision edge case scenarios generated with input from functional genomics experts, clinicians, and genetic counselors; and second, full report generation. Cash prizes will be awarded to top-performing teams. Join us to hear about the challenge goals, how the benchmark is designed to reveal weaknesses in today’s biomedical AI systems, and ways to get involved.

Carol Cain

Executive Director of Clinical Information Services, Kaiser Permanente’s Care Management Institute

Muhammad Mamdani

Director, Toronto Temerty Faculty of Medicine Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM)
Clinical Lead, Artificial Intelligence at Ontario Health
Faculty Affiliate, Vector Institute
Professor, University of Toronto

Hojjat Salmasian

University of Pennsylvania

There is still a big gap between what AI is supposed to deliver in healthcare and what it delivers in practice. Many tools don’t perform as expected once they’re deployed, or they never achieve the impact they were built for. This panel brings together leaders who have been through those hard lessons and will share candid examples of what went wrong and why. We will look at common failure modes–from technical problems (e.g., performance drift and weak generalization) to operational realities (e.g., workflow fit, integration pain, and clinician trust) to broader system issues (e.g., equity, transparency, and governance). The goal isn’t to dwell on failure; it’s to extract the lessons we need, to build AI that works in the real world.

Sara Murray

Vice President and Chief Health AI Officer, UCSF Health
Associate Professor of Clinical Medicine and Associate Chief, Division of Clinical Informatics and Digital Transformation (DoC-IT), Department of Medicine

Adam Rodman

General Internist and Medical Educator, Beth Israel Deaconess Medical Center
Assistant Professor, Harvard Medical School
Director of AI programs, Carl J. Shapiro Center for Education and Research
Associate Editor, NEJM AI

Artificial intelligence is rapidly being integrated into healthcare, promising to improve patient safety, operational efficiency, and care quality. Yet as AI takes on increasingly complex clinical tasks, a critical question emerges: are we inadvertently eroding the very experiences that shape skilled, competent clinicians? Reduced exposure to diagnostic reasoning, procedural practice, and critical decision-making may have long-term consequences for the workforce we are training today. In this debate, we will challenge assumptions on both sides and ask whether we are even framing the right questions about AI and clinical competency.

Alan Karthikesalingam

Research Scientist, Google

Adam Rodman

General Internist and Medical Educator, Beth Israel Deaconess Medical Center
Assistant Professor, Harvard Medical School
Director of AI programs, Carl J. Shapiro Center for Education and Research
Associate Editor, NEJM AI

The feasibility, safety, and promise of conversational diagnostic AI in real-world care. This talk reports results from a pre-registered prospective study of the Articulate Medical Intelligence Explorer (AMIE) at a busy primary care clinic in Boston, MA. This presentation will focus not only on trial results, but on methodological lessons learned for those looking to safely launch patient-facing AI studies.

Megan Bent

Ricky Bloomfield

Chief Medical Officer, Oura

Casey Ross

Chief Investigative Reporter, STAT

Ellyn Winters-Robinson

Co-Creator, AskEllyn

What really happens when AI meets patients at the point of care? In this fireside chat, a journalist, a health-tech executive, and patient advocates share candid, lived experiences of how AI shapes real-world care—at the front door, behind coverage decisions, and inside clinical workflows. Grounded in first-hand experience, this panel will explore how AI is designed and deployed in real-world settings and asks: whose values are these systems serving by default—and what would it take for them to more reliably reflect patients’ interests and human values?

Aditya Kale

University of Birmingham, UK

This talk presents a pre-implementation medical algorithmic audit of an autonomous chest X-ray reporting system (Oxipit ChestLink) conducted at a large NHS university hospital. The session will highlight real-world performance across patient subgroups and discuss implications for safe clinical deployment.

Pierre Elias

Assistant Professor, Columbia University Irving Medical Center
Medical Director for AI, NewYork-Presbyterian

First, we will present findings from the CACTUS pilot where 7,072 patients in 8 emergency departments were screened for structural heart disease using EchoNext, the first AI model to detect all causes of structural heart disease. Second, we will discuss the results of the Cardiac Amyloidosis Discovery trial, the first ever late breaking clinical trial at American Heart Association for AI-based screening technologies. After presenting results from both studies we will discuss the challenges of building, studying, and maintaining a large AI based screening program across 10 hospitals and 200 clinics.

Nicholas Jacobson

Associate Professor of Biomedical Data Science, Psychiatry, and Computer Science, Dartmouth

Roy Perlis

Editor in Chief, JAMA+ AI
Director of the Center for Quantitative Health, Massachusetts General Hospital
Professor of Psychiatry, Harvard Medical School

Inioluwa Deborah Raji

Researcher, University of California, Berkeley
Academic Fellow, Leadership Conference on Civil and Human Rights

Mental health represents a particularly high-stakes use case for medical AI, as recent lawsuits implicating commercial tools in patient suicides have made painfully clear. Yet an equally pressing problem demands attention: a global shortage of qualified mental health providers has left millions without adequate care, and—advised or not—many are already turning to commercial chatbots for counseling and support. What safeguards are needed before deploying AI in these contexts, and who should be responsible for enforcing them? How should we design clinical trials when the risk of harm is acute and the affected population is vulnerable? And how do we balance regulatory caution against the reality that millions are already using unregulated tools? This panel will tackle these questions, bringing together experts in psychiatry, bioethics, algorithmic accountability, and the hands-on development and prospective evaluation of mental health AI systems.

Anna Goldenberg

Professor, University of Toronto
Varma Family Chair, SickKids Research Institute
CIFAR AI Chair, Vector Institute

Patrick J. Heagerty

Professor, University of Washington

Do we always need a trial to evaluate the clinical impact of an AI model? When is it safe to deploy without a trial? Are there truly effective ways to roll out model deployment in real-world settings and still have robust estimates of its clinical impact? Let’s debate!

Ashley Beecy Moderator

Chief AI Officer, Sutter Health

Aaron Neinstein

Chief Medical Officer, Notable

Hong Truong

Principal, Define Ventures

Travis Zack

Chief Medical Officer, OpenEvidence
Assistant Adjunct Professor, University of California San Francisco

As large electronic health records and AI technology vendors increasingly dominate the healthcare landscape, how do venture capitalists, startups, and health systems navigate technology investment decisions? This panel brings together voices from across the ecosystem to uncover the tensions, trade-offs, and strategies at play when major incumbent vendors loom large over innovation and investment priorities in health AI. Panelists will explore how entrepreneurship can succeed in the presence of giants, what health systems weigh when choosing between building on dominant platforms versus betting on emerging players, and how the industry can foster an environment where transformative ideas still have room to thrive.

Andrew Wong

University of Michigan

The Epic Sepsis Model v2 (ESM v2) is a sepsis prediction model deployed at over a hundred hospitals nationwide. This talk will present findings from a multicenter prospective validation study of the ESM v2, report on the variability in model performance and alert burden observed across four major U.S. health systems, and discuss implications for the national generalizability of AI-driven clinical decision support tools.

Jean Feng

Associate Professor, University of California San Francisco

Julian Hong

Associate Professor, University of California San Francisco

This Year in Review session will explore key themes in AI deployment into the clinic over the past year, supported by notable papers and real-world case studies. We will discuss both highlights and lowlights, examining what has worked, what hasn’t, and why. Through a review of impactful research and major events, this session will provide a critical perspective on the state of clinical AI and where it is headed next.

Xiaoxuan Liu

Clinician Scientist, Microsoft AI
Associate Professor, University of Birmingham
Associate Editor, NEJM AI

Harsha Nori

Microsoft AI

Since showcasing the power of language model orchestration in clinical diagnostics with MAI-DxO last year, the health research team at Microsoft AI will share new progress on advanced clinical reasoning and improving the consumer experience of healthcare.

Stephen Ball

Brendan O’Leary

Safeguard now, or stifle breakthroughs? Two leading experts face off in a high-stakes debate. One side warns that without immediate, robust regulation, clinical AI will trigger widespread medical errors and privacy breaches at population scale. The other argues that regulating too soon will deny millions of patients the full power of today’s AI models—especially where human expertise is scarce—and slow the development of tomorrow’s truly high-performance systems. Which path protects patients more: faster guardrails or faster progress? Come decide with us at SAIL.

Nadine Hachach-Haram

CEO and Founder, Proximie
Director of Clinical Innovation, Guy’s + St Thomas’ NHS Foundation Trust

Christopher Morley

Medivis

Surgical AI is rapidly transforming the operating room into a data-rich, learning environment. Robotic surgery, tele-operation, and augmented reality technologies are the new frontier of the interface that is now merging computer vision, language models, and robotic action models to augment surgical skill, safety, and awareness. Real-time analytics driven by machine perception can quantify surgical technique, guide intraoperative decision-making, and provide adaptive feedback to surgeons and trainees. Looking forward, integration of multimodal operative data with learning health system infrastructure will enable closed-loop improvement in surgical workflows, complication prevention, and patient outcomes. This session will explore how AI-augmented robotics and analytics can move surgery toward a measurable, continuously learning discipline that scales expertise and improves care at the system level.

Join other SAIL attendees for a 2-hour kayak tour to experience the rare phenomenon of bioluminescence in Laguna Grande, one of 3 bio-bays found in Puerto Rico (and only 5 worldwide!). The glowing effect in the water is caused by dinoflagellates—microscopic organisms that emit a bluish-green neon glow in reaction to movement in the water.

Plan to depart the hotel by 7:30 PM and arrive for the tour by 8:15 PM. Transportation must be arranged separately (~45 min by Uber or taxi).

Only 25 spaces available–first come, first served–book by April 23rd. Call 787-245-4545 to purchase tickets and reference booking number “335251337”. More info at https://kayakingpuertorico.com/home/tours/bio-bay-kayak-tour/.

($67 per person. Children under the age of 6 are not permitted.)

Join other SAIL attendees for a guided hike into the heart of Puerto Rico’s El Yunque rainforest. Hike through the lush foliage and learn more about the wildlife that calls El Yunque home. Next, swim in a natural pool, go cliff-diving and rope-swinging if you dare, and admire the waterfalls.

Plan to depart the hotel by 7:30 AM. Transportation included to/from hotel.

Limited spaces available. Go to https://micasaventuras.com/el-yunque-rainforest-adventure.html to purchase tickets and for more info. Select the 8:30am hike and apply the promo code DIRECTAVENTURAS. ($68 per person. Children under the age of 2 are not permitted.)

Join other SAIL attendees for an unforgettable guided adventure that takes you off the beaten path to enjoy a 2.5 hour horseback ride, while taking in the Caribbean views and the beauty of the rainforest ecosystem. This tour takes you to the Mameyes River, plus, you’ll have the opportunity to ride on one of the best known beaches in the Caribbean. This tour caters to all levels of riders–beginner, intermediate, and experienced riders are all welcome.

Plan to depart the hotel by 8:00 AM. Transportation must be arranged separately to Carabali Adventure Park (~20 min by Uber or taxi).

Limited spaces available. More details will be posted soon. More info at https://carabalirainforestpark.com/adventures/horseback-beach/. ($140 per person. Children under the age of 8 are not permitted.)