100 citations · 322 across the 12 of their papers we have counts for
13 papers
Reinforcement Learning Towards Broadly and Persistently Beneficial Models
Akshay V. Jagadeesh, Rahul K. Arora, Khaled Saab +5
As AI systems are deployed across increasingly diverse and high-stakes settings, model alignment must generalize beyond the tasks and domains seen during training. This is especial…
HealthBench Professional: Evaluating Large Language Models on Real Clinician Chats
Rebecca Soskin Hicks, Mikhail Trofimov, Dominick Lim +13
Millions of clinicians use ChatGPT to support clinical care, but evaluations of the most common use cases in model-clinician conversations are limited. We introduce HealthBench Pro…
A prospective clinical feasibility study of a conversational diagnostic AI in an ambulatory primary care clinic
Peter Brodeur, Jacob M. Koshy, Anil Palepu +45
Large language model (LLM)-based AI systems have shown promise for patient-facing diagnostic and management conversations in simulated settings. Translating these systems into clin…
Complementary Human-AI Clinical Reasoning in Ophthalmology
Mertcan Sevgi, Fares Antaki, Abdullah Zafar Khan +26
Vision impairment and blindness are a major global health challenge where gaps in the ophthalmology workforce limit access to specialist care. We evaluate AMIE, a medically fine-tu…
Advancing Conversational Diagnostic AI with Multimodal Reasoning
Khaled Saab, Jan Freyberg, Chunjong Park +33
Large Language Models (LLMs) have demonstrated great potential for conducting diagnostic conversations but evaluation has been largely limited to language-only interactions, deviat…
Systematic Evaluation of Large Vision-Language Models for Surgical Artificial Intelligence
Anita Rau, Mark Endo, Josiah Aklilu +6
Large Vision-Language Models offer a new paradigm for AI-driven image understanding, enabling models to perform tasks without task-specific training. This flexibility holds particu…
Towards Conversational AI for Disease Management
Anil Palepu, Valentin Liévin, Wei-Hung Weng +17
While large language models (LLMs) have shown promise in diagnostic dialogue, their capabilities for effective management reasoning - including disease progression, therapeutic res…
Accelerating scientific discovery with Co-Scientist
Juraj Gottweis, Wei-Hung Weng, Alexander Daryin +48
Scientific discovery is driven by scientists generating novel hypotheses for complex problems that undergo rigorous experimental validation. To augment this process, we introduce C…
Exploring Large Language Models for Specialist-level Oncology Care
Anil Palepu, Vikram Dhillon, Polly Niravath +18
Large language models (LLMs) have shown remarkable progress in encoding clinical knowledge and responding to complex medical queries with appropriate clinical reasoning. However, t…
Towards Democratization of Subspeciality Medical Expertise
Jack W. O'Sullivan, Anil Palepu, Khaled Saab +23
The scarcity of subspecialist medical expertise, particularly in rare, complex and life-threatening diseases, poses a significant challenge for healthcare delivery. This issue is p…
Advancing Multimodal Medical Capabilities of Gemini
Lin Yang, Shawn Xu, Andrew Sellergren +44
Many clinical tasks require an understanding of specialized data, such as medical images and genomics, which is not typically found in general-purpose large multimodal models. Buil…
Capabilities of Gemini Models in Medicine
Khaled Saab, Tao Tu, Wei-Hung Weng +64
Excellence in a wide variety of medical applications poses considerable challenges for AI, requiring advanced reasoning, access to up-to-date medical knowledge and understanding of…
Towards Conversational Diagnostic AI
Tao Tu, Anil Palepu, Mike Schaekermann +22
At the heart of medicine lies the physician-patient dialogue, where skillful history-taking paves the way for accurate diagnosis, effective management, and enduring trust. Artifici…