10 citations · 24 across the 15 of their papers we have counts for
15 papers
Towards Expert-level Medical AI for Real-time Video Consultations
Mahvish Nagda, Jihyeon Lee, Matthew Thompson +37
Audio-visual interaction is the standard for patient-physician consultations, enabling natural communication and effective assessment of illness through non-verbal cues. While text…
CARE: A Conformal Safety Layer for Medical Summarization
Suhana Bedi, Bridget Lin, Anson Y. Zhou +5
Large language models (LLMs) are increasingly used for medical summarization, but their outputs can omit medically important information and introduce unsupported claims. Existing…
Quantifying and Mitigating Premature Closure in Frontier LLMs
Rebecca Handler, Suhana Bedi, Nigam Shah
Premature closure, or committing to a conclusion before sufficient information is available, is a recognized contributor to diagnostic error but remains underexamined in large lang…
HealthAdminBench: Evaluating Computer-Use Agents on Healthcare Administration Tasks
Suhana Bedi, Ryan Welch, Ethan Steinberg +12
Healthcare administration accounts for over $1 trillion in annual spending, making it a promising target for LLM-based computer-use agents (CUAs). While clinical applications of LL…
Adoption and Use of LLMs at an Academic Medical Center
Nigam H. Shah, Nerissa Ambers, Abby Pandya +55
While large language models (LLMs) can support clinical documentation needs, standalone tools struggle with "workflow friction" from manual data entry. We developed ChatEHR, a syst…
Reflections from Research Roundtables at the Conference on Health, Inference, and Learning (CHIL) 2025
Emily Alsentzer, Marie-Laure Charpignon, Bill Chen +90
The 6th Annual Conference on Health, Inference, and Learning (CHIL 2025), hosted by the Association for Health Learning and Inference (AHLI), was held in person on June 25-27, 2025…
Structured Prompts Improve Evaluation of Language Models
Asad Aali, Muhammad Ahmed Mohsin, Vasiliki Bikia +15
As language models (LMs) are increasingly adopted across domains, high-quality benchmarking frameworks are essential for guiding deployment decisions. In practice, however, framewo…
Measurement to Meaning: A Validity-Centered Framework for AI Evaluation
Olawale Salaudeen, Anka Reuel, Ahmed Ahmed +6
While the capabilities and utility of AI systems have advanced, rigorous norms for evaluating these systems have lagged. Grand claims, such as models achieving general reasoning ca…
Disentangling Reasoning and Knowledge in Medical Large Language Models
Rahul Thapa, Qingyang Wu, Kevin Wu +11
Medical reasoning in large language models (LLMs) aims to emulate clinicians' diagnostic thinking, but current benchmarks such as MedQA-USMLE, MedMCQA, and PubMedQA often mix reaso…
The Optimization Paradox in Clinical AI Multi-Agent Systems
Suhana Bedi, Iddah Mlauzi, Daniel Shin +2
Multi-agent artificial intelligence systems are increasingly deployed in clinical settings, yet the relationship between component-level optimization and system-wide performance re…
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…
Distilling Large Language Models for Efficient Clinical Information Extraction
Karthik S. Vedula, Annika Gupta, Akshay Swaminathan +3
Large language models (LLMs) excel at clinical information extraction but their computational demands limit practical deployment. Knowledge distillation--the process of transferrin…
Context Clues: Evaluating Long Context Models for Clinical Prediction Tasks on EHRs
Michael Wornow, Suhana Bedi, Miguel Angel Fuentes Hernandez +5
Foundation Models (FMs) trained on Electronic Health Records (EHRs) have achieved state-of-the-art results on numerous clinical prediction tasks. However, most existing EHR FMs hav…
ZIP-FIT: Embedding-Free Data Selection via Compression-Based Alignment
Elyas Obbad, Iddah Mlauzi, Brando Miranda +4
Data selection is crucial for optimizing language model (LM) performance on specific tasks, yet most existing methods fail to effectively consider the target task distribution. Cur…
meds_reader: A fast and efficient EHR processing library
Ethan Steinberg, Michael Wornow, Suhana Bedi +3
The growing demand for machine learning in healthcare requires processing increasingly large electronic health record (EHR) datasets, but existing pipelines are not computationally…