Assessing the Ability of GPT to Generate Illness Scripts: An Evaluation Study
Yanagita, Y.; Yokokawa, D.; Fukuzawa, F.; Uchida, S.; Uehara, T.; Ikusaka, M.
Show abstract
BackgroundIllness scripts, which are structured summaries of clinical knowledge concerning diseases, are crucial in disease prediction and problem representation during clinical reasoning. Clinicians iteratively enhance their illness scripts through their clinical practice. Because illness scripts are unique to each physician, no systematic summary of specific examples of illness scripts has been reported. ObjectiveGenerative artificial intelligence (AI) stands out as an educational aid in continuing medical education. The effortless creation of a typical illness script by generative AI could enhance the comprehension of disease concepts and increase diagnostic accuracy. This study investigated whether generative AI possesses the capability to generate illness scripts. MethodsWe used ChatGPT, a generative AI, to create illness scripts for 184 diseases based on the diseases and conditions integral to the National Model Core Curriculum for undergraduate medical education (2022 revised edition) and primary care specialist training in Japan. Three physicians applied a three-tier grading scale: "A" if the content of each diseases illness script proves sufficient for training medical students, "B" if it is partially lacking but acceptable, and "C" if it is deficient in multiple respects. Moreover, any identified deficiencies in the illness scripts were discussed during the evaluation process. ResultsLeveraging ChatGPT, we successfully generated each component of the illness script for 184 diseases without any omission. The illness scripts received "A," "B," and "C" ratings of 56.0% (103/184), 28.3% (52/184), and 15.8% (29/184), respectively. ConclusionUseful illness scripts were seamlessly and instantaneously created by ChatGPT using prompts appropriate for medical students. The technology-driven illness script is a valuable tool for introducing medical students to disease conceptualization.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Large language models for generating medical examinations: systematic review 96%
- Performance of ChatGPT on Chinese National Medical Licensing Examinations: A Five-Year Examination Evaluation Study for Physicians, Pharmacists and Nurses 95%
- Medical students' perceptions towards artificial intelligence in education and practice: A multinational, multicenter cross-sectional study 94%
Similar papers in this journal
Similar papers in this journal
- A typology of physician input approaches to using AI chatbots for clinical decision-making: a mixed methods study 94%
- Utilization of Generative AI-drafted Responses for Managing Patient-Provider Communication 94%
- Bridging the Literacy Gap for Surgical Consents: An AI-Human Expert Collaborative Approach 93%
Similar papers in this journal
Similar papers in this journal
- Medical Clinical Minds Meet Artificial Intelligence: Italian Physicians' Knowledge, Attitudes, and Concordance between Italian Physicians and AI-Generated Diagnoses. A National Cross-Sectional Study 94%
- Large Language Models in Real-World Clinical Workflows: A Systematic Review of Applications and Implementation 92%
- Development and Validation of a Machine Learning Model Integrated with the Clinical Workflow for Inpatient Discharge Date Prediction 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.