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PERSPECTIVE article

Front. Artif. Intell.
Sec. Medicine and Public Health
Volume 7 - 2024 | doi: 10.3389/frai.2024.1506676

AI-Assisted Human Clinical Reasoning in the ICU: Beyond "To Err Is Human"

Provisionally accepted
Khalil El Gharib Khalil El Gharib 1Bakr Jundi Bakr Jundi 2David Furfaro David Furfaro 3*Raja- Elie Abdulnour Raja- Elie Abdulnour 2*
  • 1 Rutgers Robert Wood Johnson University Hospital, New Brunswick, United States
  • 2 Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States
  • 3 Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, United States

The final, formatted version of the article will be published soon.

    Diagnostic errors pose a significant public health challenge, affecting nearly 800,000 Americans annually, with even higher rates globally. In the ICU, these errors are particularly prevalent, leading to substantial morbidity and mortality. The clinical reasoning process aims to reduce diagnostic uncertainty and establish a plausible differential diagnosis but is often hindered by cognitive load, patient complexity, and clinician burnout. These factors contribute to cognitive biases that compromise diagnostic accuracy. Emerging technologies like large language models (LLMs) offer potential solutions to enhance clinical reasoning and improve diagnostic precision. In this perspective article, we explore the roles of LLMs, such as GPT-4, in addressing diagnostic challenges in critical care settings through a case study of a critically ill patient managed with LLM assistance.

    Keywords: Large language models, clinical reasoning, Diagnostic Errors, artificial intelligence, Critical Care

    Received: 05 Oct 2024; Accepted: 19 Nov 2024.

    Copyright: © 2024 El Gharib, Jundi, Furfaro and Abdulnour. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    David Furfaro, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, Massachusetts, United States
    Raja- Elie Abdulnour, Brigham and Women's Hospital, Harvard Medical School, Boston, 02115, Massachusetts, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.