26 citations · 54 across the 12 of their papers we have counts for
14 papers
Scaling Clinical Judgment to Evaluate Medical AI
Thomas A. Buckley, Zahir Kanjee, Peter G. Brodeur +15
Blinded physician evaluation has been considered by many to be the gold standard for assessing clinical reasoning in large language models (LLMs). This is difficult to scale; thus,…
CLExEval: A Human-in-the-Loop Framework for Qualitative Evaluation of LLM Clinical Reasoning
Ajmal M., Abin Roy, Afthab Salam Kanniyan +4
Large Language Models (LLMs) achieve strong results on many medical benchmarks, but their clinical reasoning remains difficult to evaluate reliably. A central risk is an evaluation…
Towards Conversational Medical AI with Eyes, Ears and a Voice
Meet Shah, Jason Gusdorf, Anil Palepu +50
The practice of medicine relies not only upon skillful dialogue but also on the nuanced exchange and interpretation of rich auditory and visual cues between doctors and patients. B…
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…
A global log for medical AI
Ayush Noori, Aaron E. Boussina, Hai Ho Bich +48
Modern computer systems rely on syslog, a universal protocol that records critical events across heterogeneous infrastructure. Medicine's rapidly growing AI stack has no equivalent…
RadGame: An AI-Powered Platform for Radiology Education
Mohammed Baharoon, Siavash Raissi, John S. Jun +29
We introduce RadGame, an AI-powered gamified platform for radiology education that targets two core skills: localizing findings and generating reports. Traditional radiology traini…
Teaching large language models to reason like expert diagnosticians
Thomas A. Buckley, Riccardo Conci, Peter G. Brodeur +23
Differential diagnosis is an iterative process that integrates patient information with broader medical knowledge. Clinical case series such as the NEJM Clinicopathologic Conferenc…
One Patient, Many Contexts: Scaling Medical AI with Contextual Intelligence
Michelle M. Li, Ben Y. Reis, Adam Rodman +6
Medical AI, including clinical language models, vision-language models, and multimodal health record models, already summarizes notes, answers questions, and supports decisions. Th…
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…
ER-Reason: A Benchmark Dataset for LLM Clinical Reasoning in the Emergency Room
Nikita Mehandru, Niloufar Golchini, Namrata Garg +9
Existing benchmarks for evaluating the clinical reasoning capabilities of large language models (LLMs) often lack a clear definition of "clinical reasoning" as a construct, fail to…
BRIDGE: Benchmarking Large Language Models for Understanding Real-world Clinical Practice Text
Jiageng Wu, Bowen Gu, Ren Zhou +14
Large language models (LLMs) hold great promise for medical applications and are evolving rapidly, with new models being released at an accelerated pace. However, benchmarking on l…
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…
Superhuman performance of a large language model on the reasoning tasks of a physician
Peter G. Brodeur, Thomas A. Buckley, Zahir Kanjee +22
A seminal paper published by Ledley and Lusted in 1959 introduced complex clinical diagnostic reasoning cases as the gold standard for the evaluation of expert medical computing sy…
Multimodal Foundation Models Exploit Text to Make Medical Image Predictions
Thomas Buckley, James A. Diao, Pranav Rajpurkar +2
Multimodal foundation models have shown compelling but conflicting performance in medical image interpretation. However, the mechanisms by which these models integrate and prioriti…