activity
20202023
most citedEvaluation of GPT-3.5 and GPT-4 for supporting real-world information needs in healthcare delivery

33 citations · 65 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CL2023★ 7 cited

Diagnostic Reasoning Prompts Reveal the Potential for Large Language Model Interpretability in Medicine

Thomas Savage, Ashwin Nayak, Robert Gallo +2

One of the major barriers to using large language models (LLMs) in medicine is the perception they use uninterpretable methods to make clinical decisions that are inherently differ…

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…

cs.AI2023★ 33 cited

Evaluation of GPT-3.5 and GPT-4 for supporting real-world information needs in healthcare delivery

Debadutta Dash, Rahul Thapa, Juan M. Banda +15

Despite growing interest in using large language models (LLMs) in healthcare, current explorations do not assess the real-world utility and safety of LLMs in clinical settings. Our…

cs.LG2023★ 2 cited

DEPLOYR: A technical framework for deploying custom real-time machine learning models into the electronic medical record

Conor K. Corbin, Rob Maclay, Aakash Acharya +6

Machine learning (ML) applications in healthcare are extensively researched, but successful translations to the bedside are scant. Healthcare institutions are establishing framewor…

cs.LG2022

Avoiding Biased Clinical Machine Learning Model Performance Estimates in the Presence of Label Selection

Conor K. Corbin, Michael Baiocchi, Jonathan H. Chen

When evaluating the performance of clinical machine learning models, one must consider the deployment population. When the population of patients with observed labels is only a sub…

cs.LG2020★ 3 cited

Clinical Recommender System: Predicting Medical Specialty Diagnostic Choices with Neural Network Ensembles

Morteza Noshad, Ivana Jankovic, Jonathan H. Chen

The growing demand for key healthcare resources such as clinical expertise and facilities has motivated the emergence of artificial intelligence (AI) based decision support systems…