Filippo Chiabrando

Background: Health care practitioners use clinical decision support systems (CDSS) as an aid in the crucial task of clinical reasoning and decision-making. Traditional CDSS are online repositories (ORs) and clinical practice guidelines (CPG). Recently, large language models (LLMs) such as ChatGPT have emerged as potential alternatives. They have proven to be powerful, innovative tools, yet they are not devoid of worrisome risks. Objective: This study aims to explore how medical students perform in an evaluated clinical case through the use of different CDSS tools. Methods: The authors randomly divided medical students into 3 groups, CPG, n=6 (38%); OR, n=5 (31%); and ChatGPT, n=5 (31%); and assigned each group a different type of CDSS for guidance in answering prespecified questions, assessing how students’ speed and ability at resolving the same clinical case varied accordingly. External reviewers evaluated all answers based on accuracy and completeness metrics (score: 1‐5). The authors analyzed and categorized group scores according to the skill investigated: differential diagnosis, diagnostic workup, and clinical decision-making. Results: Answering time showed a trend for the ChatGPT group to be the fastest. The mean scores for completeness were as follows: CPG 4.0, OR 3.7, and ChatGPT 3.8 ( P =.49). The mean scores for accuracy were as follows: CPG 4.0, OR 3.3, and ChatGPT 3.7 ( P =.02). Aggregating scores according to the 3 students’ skill domains, trends in differences among the groups emerge more clearly, with the CPG group that performed best in nearly all domains and maintained almost perfect alignment between its completeness and accuracy. Conclusions: This hands-on session provided valuable insights into the potential perks and associated pitfalls of LLMs in medical education and practice. It suggested the critical need to include teachings in medical degree courses on how to properly take advantage of LLMs, as the potential for misuse is evident and real.

UNSTRUCTURED The medical community is both excited and worried by the advent of artificial intelligence in healthcare, whereby two disciplines meet for the benefit of patients. This is especially true when considering large language models (LLM) for generative functions. This letter briefly describes a successful collaboration between physicians and informaticians to develop a LLM-powered chatbot for outpatient management of COVID-19.

BACKGROUND Healthcare practitioners use Clinical Decision Support Systems (CDSS) as an aid in the crucial task of clinical reasoning and decision making. Traditional CDSS are Online Repositories (OR) and Clinical Practice Guidelines (CPG). Recently, Large Language Models (LLMs) like ChatGPT have emerged as potential alternatives. They have proven to be powerful innovative tools, yet they are not devoid of worrisome risks. OBJECTIVE This study aims to explore how medical students utilize ChatGPT as a CDSS in a teaching environment. METHODS The authors randomly divided medical students into three groups and assigned each group a different type of CDSS for guidance in answering prespecified questions, assessing how students’ ability at resolving the same clinical case varied accordingly. External reviewers evaluated all answers based on accuracy and completeness metrics (Score: 1-5). The authors analyzed and categorized group scores according to the skill investigated: Differential Diagnosis, Diagnostic Workup, and Clinical Decision Making. RESULTS Answering time showed a trend for the ChatGPT group to be the fastest. The mean scores for completeness were: 4.0 (CPG), 3.7 (OR), 3.8 (ChatGPT). The mean scores for accuracy were: 4.0 (CPG), 3.3 (OR), 3.7 (ChatGPT). Aggregating scores according to the three students’ skill domains, trends in differences among the groups emerge more clearly, with the CPG group that performed best in nearly all domains and maintained almost perfect alignment between its completeness and accuracy. CONCLUSIONS This hands-on session provided valuable insights into the potential perks and associated pitfalls of LLMs in medical education and practice. It suggested the critical need to include teachings in Medical Degree Courses on how to properly take advantage of LLMs, as the potential for misuse is evident and real.