activity
20202026
most citedMedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records

19 citations · 33 across the 6 of their papers we have counts for

collaborators

6 papers

cs.AI2026

Evidence-Grounded Subspecialty Reasoning: Evaluating a Curated Clinical Intelligence Layer on the 2025 Endocrinology Board-Style Examination

Amir Hosseinian, MohammadReza Zare Shahneh, Umer Mansoor +3

Background: Large language models have demonstrated strong performance on general medical examinations, but subspecialty clinical reasoning remains challenging due to rapidly evolv…

cs.CL2025★ 10 cited

MedHELM: Holistic Evaluation of Large Language Models for Medical Tasks

Suhana Bedi, Hejie Cui, Miguel Fuentes +78

While large language models (LLMs) achieve near-perfect scores on medical licensing exams, these evaluations inadequately reflect the complexity and diversity of real-world clinica…

cs.AI2025★ 2 cited

VeriFact: Verifying Facts in LLM-Generated Clinical Text with Electronic Health Records

Philip Chung, Akshay Swaminathan, Alex J. Goodell +26

Methods to ensure factual accuracy of text generated by large language models (LLM) in clinical medicine are lacking. VeriFact is an artificial intelligence system that combines re…

cs.AI2024★ 2 cited

Large Language Model Capabilities in Perioperative Risk Prediction and Prognostication

Philip Chung, Christine T Fong, Andrew M Walters +3

We investigate whether general-domain large language models such as GPT-4 Turbo can perform risk stratification and predict post-operative outcome measures using a description of t…

cs.CL2023★ 19 cited

MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records

Scott L. Fleming, Alejandro Lozano, William J. Haberkorn +27

The ability of large language models (LLMs) to follow natural language instructions with human-level fluency suggests many opportunities in healthcare to reduce administrative burd…

stat.ME2020

Feature-weighted elastic net: using "features of features" for better prediction

J. Kenneth Tay, Nima Aghaeepour, Trevor Hastie +1

In some supervised learning settings, the practitioner might have additional information on the features used for prediction. We propose a new method which leverages this additiona…