Ethan Goh, MD, MS

All Publications

• Large Language Model Influence on Diagnostic Reasoning: A Randomized Clinical Trial. JAMA network open Goh, E., Gallo, R., Hom, J., Strong, E., Weng, Y., Kerman, H., Cool, J. A., Kanjee, Z., Parsons, A. S., Ahuja, N., Horvitz, E., Yang, D., Milstein, A., Olson, A. P., Rodman, A., Chen, J. H. 2024; 7 (10): e2440969

  Abstract

  Large language models (LLMs) have shown promise in their performance on both multiple-choice and open-ended medical reasoning examinations, but it remains unknown whether the use of such tools improves physician diagnostic reasoning.To assess the effect of an LLM on physicians' diagnostic reasoning compared with conventional resources.A single-blind randomized clinical trial was conducted from November 29 to December 29, 2023. Using remote video conferencing and in-person participation across multiple academic medical institutions, physicians with training in family medicine, internal medicine, or emergency medicine were recruited.Participants were randomized to either access the LLM in addition to conventional diagnostic resources or conventional resources only, stratified by career stage. Participants were allocated 60 minutes to review up to 6 clinical vignettes.The primary outcome was performance on a standardized rubric of diagnostic performance based on differential diagnosis accuracy, appropriateness of supporting and opposing factors, and next diagnostic evaluation steps, validated and graded via blinded expert consensus. Secondary outcomes included time spent per case (in seconds) and final diagnosis accuracy. All analyses followed the intention-to-treat principle. A secondary exploratory analysis evaluated the standalone performance of the LLM by comparing the primary outcomes between the LLM alone group and the conventional resource group.Fifty physicians (26 attendings, 24 residents; median years in practice, 3 [IQR, 2-8]) participated virtually as well as at 1 in-person site. The median diagnostic reasoning score per case was 76% (IQR, 66%-87%) for the LLM group and 74% (IQR, 63%-84%) for the conventional resources-only group, with an adjusted difference of 2 percentage points (95% CI, -4 to 8 percentage points; P = .60). The median time spent per case for the LLM group was 519 (IQR, 371-668) seconds, compared with 565 (IQR, 456-788) seconds for the conventional resources group, with a time difference of -82 (95% CI, -195 to 31; P = .20) seconds. The LLM alone scored 16 percentage points (95% CI, 2-30 percentage points; P = .03) higher than the conventional resources group.In this trial, the availability of an LLM to physicians as a diagnostic aid did not significantly improve clinical reasoning compared with conventional resources. The LLM alone demonstrated higher performance than both physician groups, indicating the need for technology and workforce development to realize the potential of physician-artificial intelligence collaboration in clinical practice.ClinicalTrials.gov Identifier: NCT06157944.

  View details for DOI 10.1001/jamanetworkopen.2024.40969

  View details for PubMedID 39466245

• Large Language Model Influence on Management Reasoning: A Randomized Controlled Trial. medRxiv : the preprint server for health sciences Goh, E., Gallo, R., Strong, E., Weng, Y., Kerman, H., Freed, J., Cool, J. A., Kanjee, Z., Lane, K. P., Parsons, A. S., Ahuja, N., Horvitz, E., Yang, D., Milstein, A., Olson, A. P., Hom, J., Chen, J. H., Rodman, A. 2024

  Abstract

  Large language model (LLM) artificial intelligence (AI) systems have shown promise in diagnostic reasoning, but their utility in management reasoning with no clear right answers is unknown.To determine whether LLM assistance improves physician performance on open-ended management reasoning tasks compared to conventional resources.Prospective, randomized controlled trial conducted from 30 November 2023 to 21 April 2024.Multi-institutional study from Stanford University, Beth Israel Deaconess Medical Center, and the University of Virginia involving physicians from across the United States.92 practicing attending physicians and residents with training in internal medicine, family medicine, or emergency medicine.Five expert-developed clinical case vignettes were presented with multiple open-ended management questions and scoring rubrics created through a Delphi process. Physicians were randomized to use either GPT-4 via ChatGPT Plus in addition to conventional resources (e.g., UpToDate, Google), or conventional resources alone.The primary outcome was difference in total score between groups on expert-developed scoring rubrics. Secondary outcomes included domain-specific scores and time spent per case.Physicians using the LLM scored higher compared to those using conventional resources (mean difference 6.5 %, 95% CI 2.7-10.2, p<0.001). Significant improvements were seen in management decisions (6.1%, 95% CI 2.5-9.7, p=0.001), diagnostic decisions (12.1%, 95% CI 3.1-21.0, p=0.009), and case-specific (6.2%, 95% CI 2.4-9.9, p=0.002) domains. GPT-4 users spent more time per case (mean difference 119.3 seconds, 95% CI 17.4-221.2, p=0.02). There was no significant difference between GPT-4-augmented physicians and GPT-4 alone (-0.9%, 95% CI -9.0 to 7.2, p=0.8).LLM assistance improved physician management reasoning compared to conventional resources, with particular gains in contextual and patient-specific decision-making. These findings indicate that LLMs can augment management decision-making in complex cases.ClinicalTrials.gov Identifier: NCT06208423 ; https://classic.clinicaltrials.gov/ct2/show/NCT06208423.Question: Does large language model (LLM) assistance improve physician performance on complex management reasoning tasks compared to conventional resources?Findings: In this randomized controlled trial of 92 physicians, participants using GPT-4 achieved higher scores on management reasoning compared to those using conventional resources (e.g., UpToDate).Meaning: LLM assistance enhances physician management reasoning performance in complex cases with no clear right answers.

  View details for DOI 10.1101/2024.08.05.24311485

  View details for PubMedID 39148822

  View details for PubMedCentralID PMC11326321

• Influence of a Large Language Model on Diagnostic Reasoning: A Randomized Clinical Vignette Study. medRxiv : the preprint server for health sciences Goh, E., Gallo, R., Hom, J., Strong, E., Weng, Y., Kerman, H., Cool, J., Kanjee, Z., Parsons, A. S., Ahuja, N., Horvitz, E., Yang, D., Milstein, A., Olson, A. P., Rodman, A., Chen, J. H. 2024

  Abstract

  Diagnostic errors are common and cause significant morbidity. Large language models (LLMs) have shown promise in their performance on both multiple-choice and open-ended medical reasoning examinations, but it remains unknown whether the use of such tools improves diagnostic reasoning.To assess the impact of the GPT-4 LLM on physicians' diagnostic reasoning compared to conventional resources.Multi-center, randomized clinical vignette study.The study was conducted using remote video conferencing with physicians across the country and in-person participation across multiple academic medical institutions.Resident and attending physicians with training in family medicine, internal medicine, or emergency medicine.Participants were randomized to access GPT-4 in addition to conventional diagnostic resources or to just conventional resources. They were allocated 60 minutes to review up to six clinical vignettes adapted from established diagnostic reasoning exams.The primary outcome was diagnostic performance based on differential diagnosis accuracy, appropriateness of supporting and opposing factors, and next diagnostic evaluation steps. Secondary outcomes included time spent per case and final diagnosis.50 physicians (26 attendings, 24 residents) participated, with an average of 5.2 cases completed per participant. The median diagnostic reasoning score per case was 76.3 percent (IQR 65.8 to 86.8) for the GPT-4 group and 73.7 percent (IQR 63.2 to 84.2) for the conventional resources group, with an adjusted difference of 1.6 percentage points (95% CI -4.4 to 7.6; p=0.60). The median time spent on cases for the GPT-4 group was 519 seconds (IQR 371 to 668 seconds), compared to 565 seconds (IQR 456 to 788 seconds) for the conventional resources group, with a time difference of -82 seconds (95% CI -195 to 31; p=0.20). GPT-4 alone scored 15.5 percentage points (95% CI 1.5 to 29, p=0.03) higher than the conventional resources group.In a clinical vignette-based study, the availability of GPT-4 to physicians as a diagnostic aid did not significantly improve clinical reasoning compared to conventional resources, although it may improve components of clinical reasoning such as efficiency. GPT-4 alone demonstrated higher performance than both physician groups, suggesting opportunities for further improvement in physician-AI collaboration in clinical practice.

  View details for DOI 10.1101/2024.03.12.24303785

  View details for PubMedID 38559045

  View details for PubMedCentralID PMC10980135

• MEDALIGN: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records Fleming, S. L., Lozano, A., Haberkorn, W. J., Jindal, J. A., Reis, E., Thapa, R., Blankemeier, L., Genkins, J. Z., Steinberg, E., Nayak, A., Patel, B., Chiang, C., Callahan, A., Huo, Z., Gatidis, S., Adams, S., Fayanju, O., Shah, S. J., Savage, T., Goh, E., Chaudhari, A. S., Aghaeepour, N., Sharp, C., Pfeffer, M. A., Liang, P., Chen, J. H., Morse, K. E., Brunskill, E. P., Fries, J. A., Shah, N. H., Wooldridge, M., Dy, J., Natarajan, S. ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE. 2024: 22021-22030

  View details for Web of Science ID 001239985800017

• ChatGPT Influence on Medical Decision-Making, Bias, and Equity: A Randomized Study of Clinicians Evaluating Clinical Vignettes. medRxiv : the preprint server for health sciences Goh, E., Bunning, B., Khoong, E., Gallo, R., Milstein, A., Centola, D., Chen, J. H. 2023

  Abstract

  In a randomized, pre-post intervention study, we evaluated the influence of a large language model (LLM) generative AI system on accuracy of physician decision-making and bias in healthcare. 50 US-licensed physicians reviewed a video clinical vignette, featuring actors representing different demographics (a White male or a Black female) with chest pain. Participants were asked to answer clinical questions around triage, risk, and treatment based on these vignettes, then asked to reconsider after receiving advice generated by ChatGPT+ (GPT4). The primary outcome was the accuracy of clinical decisions based on pre-established evidence-based guidelines. Results showed that physicians are willing to change their initial clinical impressions given AI assistance, and that this led to a significant improvement in clinical decision-making accuracy in a chest pain evaluation scenario without introducing or exacerbating existing race or gender biases. A survey of physician participants indicates that the majority expect LLM tools to play a significant role in clinical decision making.

  View details for DOI 10.1101/2023.11.24.23298844

  View details for PubMedID 38076944

  View details for PubMedCentralID PMC10705632

• Remote evaluation of NAVIFY Oncology Hub using clinical simulation Halligan, J., Goh, E., Lo, E. N., Chabut, D., Prime, M., Ghafur, S. LIPPINCOTT WILLIAMS & WILKINS. 2023

  View details for Web of Science ID 001053772001507