TY  - JOUR
AU  - Fukuzawa, Fumitoshi
AU  - Yanagita, Yasutaka
AU  - Yokokawa, Daiki
AU  - Uchida, Shun
AU  - Yamashita, Shiho
AU  - Li, Yu
AU  - Shikino, Kiyoshi
AU  - Tsukamoto, Tomoko
AU  - Noda, Kazutaka
AU  - Uehara, Takanori
AU  - Ikusaka, Masatomi
PY  - 2024
DA  - 2024/4/8
TI  - Importance of Patient History in Artificial Intelligence–Assisted Medical Diagnosis: Comparison Study
JO  - JMIR Med Educ
SP  - e52674
VL  - 10
KW  - medical diagnosis
KW  - ChatGPT
KW  - AI in medicine
KW  - diagnostic accuracy
KW  - patient history
KW  - medical history
KW  - artificial intelligence
KW  - AI
KW  - physical examination
KW  - physical examinations
KW  - laboratory investigation
KW  - laboratory investigations
KW  - mHealth
KW  - accuracy
KW  - public health
KW  - United States
KW  - AI diagnosis
KW  - treatment
KW  - male
KW  - female
KW  - child
KW  - children
KW  - youth
KW  - adolescent
KW  - adolescents
KW  - teen
KW  - teens
KW  - teenager
KW  - teenagers
KW  - older adult
KW  - older adults
KW  - elder
KW  - elderly
KW  - older person
KW  - older people
KW  - investigative
KW  - mobile health
KW  - digital health
AB  - Background: Medical history contributes approximately 80% to a diagnosis, although physical examinations and laboratory investigations increase a physician’s confidence in the medical diagnosis. The concept of artificial intelligence (AI) was first proposed more than 70 years ago. Recently, its role in various fields of medicine has grown remarkably. However, no studies have evaluated the importance of patient history in AI-assisted medical diagnosis. Objective: This study explored the contribution of patient history to AI-assisted medical diagnoses and assessed the accuracy of ChatGPT in reaching a clinical diagnosis based on the medical history provided. Methods: Using clinical vignettes of 30 cases identified in The BMJ, we evaluated the accuracy of diagnoses generated by ChatGPT. We compared the diagnoses made by ChatGPT based solely on medical history with the correct diagnoses. We also compared the diagnoses made by ChatGPT after incorporating additional physical examination findings and laboratory data alongside history with the correct diagnoses. Results: ChatGPT accurately diagnosed 76.6% (23/30) of the cases with only the medical history, consistent with previous research targeting physicians. We also found that this rate was 93.3% (28/30) when additional information was included. Conclusions: Although adding additional information improves diagnostic accuracy, patient history remains a significant factor in AI-assisted medical diagnosis. Thus, when using AI in medical diagnosis, it is crucial to include pertinent and correct patient histories for an accurate diagnosis. Our findings emphasize the continued significance of patient history in clinical diagnoses in this age and highlight the need for its integration into AI-assisted medical diagnosis systems. 
SN  - 2369-3762
UR  - https://mededu.jmir.org/2024/1/e52674
UR  - https://doi.org/10.2196/52674
DO  - 10.2196/52674
ID  - info:doi/10.2196/52674
ER  -