Dr. Goh's research focuses on AI in healthcare, digital health, and informatics. He has successfully led multi-site, grant-funded evaluation studies on Large Language Models applications within healthcare. Prior to Stanford, he was an Internal Medicine clinician, startup founder and technology consultant, working with partners like Google, OpenAI, Roche, Samsung, IDEO, and the NHS in the development, validation and commercialization of digital health products and AI technology. He holds an MD from Imperial College, London, and a Masters in Clinical Informatics and Management from Stanford University.

Stanford Advisors

All Publications

  • 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


    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

  • 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


    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