CPHAI Symposium 2025

AI in Healthcare: From Research to Real-World Clinical Impact

HANOVER INN, GRAND BALLROOM

Friday, APRIL 18th

CPHAI Symposium 2025 Overview

Join us for the Annual Dartmouth Symposium on Precision Health & AI on April 18, 2025, at the Hanover Inn Grand Ballroom. This year’s symposium brings together leaders in AI, clinicians, and biomedical researchers to explore the latest advancements in AI-driven healthcare.


Keynote speakers include Dr. Pranav Rajpurkar (Harvard University, Forbes 30 Under 30, MIT Tech Review Innovator Under 35) and Dr. Nigam Shah (Stanford University, Chief Data Scientist for Stanford Health Care), who will share their expertise on AI in medical imaging, clinical data interpretation, and ethical AI deployment in healthcare. The event will open with remarks by Dr. Duane Compton, the Dean of Dartmouth Geisel School of Medicine and include discussions on AI leadership, research frontiers, and industry collaborations.

Keynote Presentations

Pranav Rajpurkar, PhD

Harvard University

Beyond Assistance: Rethinking AI-Human Integration in Radiology

Recent evidence challenges a fundamental assumption in medical AI: that combining AI with physician expertise naturally leads to better outcomes. Studies show that AI assistance often fails to improve diagnostic accuracy and can even slow down clinical workflows. This talk presents an alternative vision: instead of forcing integration, we should embrace clear role separation between AI systems and physicians. Drawing from recent large-scale studies and advances in generalist medical AI systems, I will examine promising models where AI and doctors work separately but complementarily, each leveraging their unique strengths. Through practical examples and emerging evidence, I will demonstrate how this approach could transform clinical practice while maintaining the essential role of human medical expertise.

Nigam H. Shah, MBBS, PhD

Stanford University

Responsible AI in a Healthcare System

As the use of artificial intelligence (AI) moves from being a curiosity to a necessity, it is clear that the benefit obtained from using AI models to prioritize care interventions is an interplay of the model’s performance, the capacity to intervene, and the benefit/harm profile of the intervention. After a brief review of the kinds of use cases that AI can serve across multiple medical specialties, we will discuss Stanford Healthcare’s efforts to shape the adoption of health AI tools to be useful, reliable, and fair so that they lead to cost-effective and sustainable solutions. The conversation will draw on examples from multiple specialities, including Pathology, Cardiology, Internal Medicine, Surgery, Psychiatry and Oncology, as well as discuss the implications of the choice of business model to ensure the use of AI enhances care quality while managing healthcare costs. We will discuss how the adoption of LLMs in medicine needs to be shaped by performing the evaluations that specify the desired benefits and verifying those benefits via testing in real-world deployments. We will conclude with the rationale and vision for collaborative activities such as the Coalition for Health AI (CHAI) and the Medical Event Data Standard (MEDS)

Opening Remarks

Duane Compton, PhD

Dean of the Geisel School of Medicine at Dartmouth

Guest Speakers

Pedram Hosseini, PhD

AI Lead Scientist at Lavita.AI

Title: Toward Meaningful Progress in Clinical AI

High-quality evaluation is a critical component of building any robust AI product or pipeline. This becomes even more essential in a highly sensitive domain like healthcare, where deploying AI models in real-world clinical settings directly impacts patients' health and well-being. Currently, there is a significant gap between existing tools and benchmarks for rigorously testing AI models in healthcare and the need for thorough evaluations in real-world medical and clinical tasks. In this talk, we will review the current state of AI model evaluation in the medical domain, highlighting key use cases such as medical AI assistants and medical question-answering systems. We will then discuss the steps we are taking—through partnerships with academic and industry labs—to bridge this gap, including a benchmark we developed from consumer medical questions on Lavita's Medical AI Assist platform, in collaboration with medical professionals at Dartmouth.

Katharina Schmolly, MD

Primary Care Resident Physician at Dartmouth Health & Founder at ZebraMD

Title: Innovation Highlight at Dartmouth Health: AI for Clinical Impact 

Despite the abundance of clinical research, disease databases, and -omics data, integrating this information into real-time clinical practice remains difficult. With 1 in 10 individuals developing a rare disease in their lifetime—on par with diabetes—demand for specialists continues to outpace availability. zebraMD is an AI-powered clinical decision support tool designed to address this critical gap. It aggregates multimodal patient data from electronic health records (EHRs), patient portals, wearables, patient-submitted PDFs, videos, and images to enable predictive modeling and precision management algorithms. The tool functions through a patient-facing app, a standalone provider interface, or direct integration with EHR systems, allowing synchronous or asynchronous AI-driven analysis. zebraMD provides rare disease likelihood scores, suggesting appropriate next diagnostic steps, and, for diagnosed patients, offers evidence-based treatment recommendations through best-practice alerts based on the latest clinical research and guidelines.

Research Panel

This panel brought together leading voices from across Dartmouth and Dartmouth Health to discuss how AI is reshaping the landscape of healthcare research and practice. Dr. Tim Burdick emphasized the promise of predictive analytics and AI integration in clinical workflows, while Dr. David Naeger highlighted how radiology is evolving with AI-assisted tools that enhance, rather than replace, human interpretation. Dr. Margaret Karagas spoke to the importance of maintaining rigorous epidemiological standards and data quality in AI studies, especially in population health. Dr. Parth Shah shared how genomic data is enabling personalized treatment plans, with AI helping unlock complex patterns in hematologic disorders. Dr. Soroush Vosoughi discussed cutting-edge applications in natural language processing and multimodal data fusion to improve patient care and medical documentation. Moderated by Dr. Indrani Bhattacharya, the discussion underscored the importance of collaboration across disciplines, thoughtful implementation, and ethical considerations to ensure that AI serves both clinicians and patients equitably.

Timothy Burdick, MD, MBA, MSc

Associate Chief Research Officer for Informatics at Dartmouth Health

David Naeger, MD, FACR, FAAR

Chair of Radiology at Dartmouth Health

Margaret Karagas, PhD

Chair of Epidemiology at Dartmouth College

Parth Shah, MD

Director of Genome Informatics, Hematology, Dartmouth Health

Soroush Vosoughi, PhD

Assistant Professor of Computer Science, Dartmouth College

Indrani Bhattacharya, PhD

Moderator

CPHAI Investigator and Assistant Professor of Biomedical Data Science at Dartmouth College

Leadership Panel

This panel brought together senior academic and clinical leaders to discuss Dartmouth’s evolving vision for AI in healthcare. Panelists emphasized the importance of aligning AI research with patient outcomes, institutional infrastructure, and responsible innovation. Topics included the integration of AI across clinical departments, strategies for fostering cross-disciplinary collaboration, and the challenge of balancing innovation with equity, privacy, and regulatory standards. The discussion highlighted Dartmouth’s strengths in translational science, biomedical data, and patient care, while identifying key opportunities to expand industry partnerships, enhance education pipelines, and build scalable systems that serve both urban and rural populations. Panelists also underscored the value of iterative implementation—developing AI solutions through pilot programs with continuous feedback from clinicians and patients. This session underscored the institution’s commitment to leading AI innovation responsibly and equitably across the academic medical enterprise.

David Kotz, PhD

Dartmouth Provost

Steven Bernstein, MD

Chief Research Officer at Dartmouth Health

Susan Roberts, PhD

Senior Associate Dean of Foundational Research at Geisel School of Medicine

Peter Solberg, MD

Chief Health Information Officer at Dartmouth Health

Keith Paulsen, PhD

Robert A. Pritzker Professor of Biomedical Engineering at Thayer School of Engineering

Moderator

Michael Whitfield, PhD

Chair of Biomedical Data Science at Dartmouth College