The Potential of ChatGPT as a Self-Diagnostic Tool in Common Orthopedic Diseases: Exploratory Study

• Published in: Journal of Medical Internet Research Vol. 25; no. 11; p. e47621
• Main Authors: Kuroiwa, Tomoyuki, Sarcon, Aida, Ibara, Takuya, Yamada, Eriku, Yamamoto, Akiko, Tsukamoto, Kazuya, Fujita, Koji
• Format: Journal Article
• Language: English
• Published: Canada JMIR Publications Inc 15.09.2023; Journal of Medical Internet Research; Gunther Eysenbach MD MPH, Associate Professor; JMIR Publications
• Subjects: Accuracy; Answers; Arthritis; Artificial Intelligence; Attention; Chatbots; Clinical standards; Communication; Computational linguistics; Computer applications to medicine. Medical informatics; Diagnostic tests; Disease; Health care; Health care delivery; Humans; Knee; Language processing; Lumbar spinal stenosis; Medical care; Medical diagnosis; Medical research; Medicine, Experimental; Multimedia; Musculoskeletal Diseases; Natural language interfaces; Natural Language Processing; Original Paper; Orthopedics; Osteoarthritis; Osteoarthritis, Knee; Pain; Patients; Popularity; Public aspects of medicine; Quality management; R858-859.7; RA1-1270; Repetitive strain injuries; Reproducibility; Reproducibility of Results; Selfdiagnosis; Spinal Cord Diseases; chatbot; natural language processing; precision; accuracy; diagnosis; language model; health information; orthopedic disease; artificial intelligence; generative pretrained transformer; AI model; ChatGPT; self-diagnosis
• DOI: 10.2196/47621
• ISSN: 1438-8871; 1439-4456

Artificial intelligence (AI) has gained tremendous popularity recently, especially the use of natural language processing (NLP). ChatGPT is a state-of-the-art chatbot capable of creating natural conversations using NLP. The use of AI in medicine can have a tremendous impact on health care delivery. Although some studies have evaluated ChatGPT's accuracy in self-diagnosis, there is no research regarding its precision and the degree to which it recommends medical consultations. The aim of this study was to evaluate ChatGPT's ability to accurately and precisely self-diagnose common orthopedic diseases, as well as the degree of recommendation it provides for medical consultations. Over a 5-day course, each of the study authors submitted the same questions to ChatGPT. The conditions evaluated were carpal tunnel syndrome (CTS), cervical myelopathy (CM), lumbar spinal stenosis (LSS), knee osteoarthritis (KOA), and hip osteoarthritis (HOA). Answers were categorized as either correct, partially correct, incorrect, or a differential diagnosis. The percentage of correct answers and reproducibility were calculated. The reproducibility between days and raters were calculated using the Fleiss κ coefficient. Answers that recommended that the patient seek medical attention were recategorized according to the strength of the recommendation as defined by the study. The ratios of correct answers were 25/25, 1/25, 24/25, 16/25, and 17/25 for CTS, CM, LSS, KOA, and HOA, respectively. The ratios of incorrect answers were 23/25 for CM and 0/25 for all other conditions. The reproducibility between days was 1.0, 0.15, 0.7, 0.6, and 0.6 for CTS, CM, LSS, KOA, and HOA, respectively. The reproducibility between raters was 1.0, 0.1, 0.64, -0.12, and 0.04 for CTS, CM, LSS, KOA, and HOA, respectively. Among the answers recommending medical attention, the phrases "essential," "recommended," "best," and "important" were used. Specifically, "essential" occurred in 4 out of 125, "recommended" in 12 out of 125, "best" in 6 out of 125, and "important" in 94 out of 125 answers. Additionally, 7 out of the 125 answers did not include a recommendation to seek medical attention. The accuracy and reproducibility of ChatGPT to self-diagnose five common orthopedic conditions were inconsistent. The accuracy could potentially be improved by adding symptoms that could easily identify a specific location. Only a few answers were accompanied by a strong recommendation to seek medical attention according to our study standards. Although ChatGPT could serve as a potential first step in accessing care, we found variability in accurate self-diagnosis. Given the risk of harm with self-diagnosis without medical follow-up, it would be prudent for an NLP to include clear language alerting patients to seek expert medical opinions. We hope to shed further light on the use of AI in a future clinical study.