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The Role of ChatGPT in osteoporosis management: a comparative analysis with clinical expertise
Ömer Faruk Bucak1, Cigdem Cinar2
1Başakşehir Çam and Sakura City Hospital, Physical Medicine and Rehabilitation, University of Health Sciences, İstanbul, Turkey. omerbucak46@gmail.com.
Abstract:
This study evaluates the role of ChatGPT in osteoporosis management, demonstrating 91% diagnostic accuracy and significantly faster response times compared to clinicians. The findings highlight the potential for artificial intelligence (AI) to revolutionize clinical decision-making while emphasizing the critical need for professional oversight to ensure patient safety and comprehensive care.
Objective:
Osteoporosis is a progressive skeletal disease that is characterized by increased bone fragility and an increased risk of fracture. Early diagnosis and effective treatment can significantly reduce healthcare costs; however, limited access to clinical expertise represents a significant challenge to patient care. This study evaluates the diagnostic and treatment recommendations provided by natural language processing (NLP)-based AI models for osteoporosis management and compares them with those of healthcare professionals.
Methods:
A multicenter, cross-sectional study was conducted with the creation of 100 real scenarios from 206 patients with a diagnosis of osteoporosis. The data pertaining to bone mineral density (BMD) and the clinical parameters were subjected to analysis using ChatGPT-4.0. Thereafter, the recommendations proffered by this software were compared to those of five independent physiatrists. A statistical validation of the model's accuracy was conducted through the use of categorical distribution analysis.
Results:
ChatGPT exhibited a high degree of diagnostic accuracy, with 91% of responses being entirely accurate. It provided recommendations for both pharmacological and non-pharmacological interventions that were consistent with current clinical guidelines. Nevertheless, 8% of the responses were reported as incomplete. Furthermore, ChatGPT was able to produce diagnoses and treatment recommendations at a significantly faster rate than clinicians, while the mean answer time is 5.4 ± 2.45 min in clinicians and 2.3 ± 0.76 min in ChatGPT (p < 0.001).
Conclusion:
These findings highlight the potential of AI tools like ChatGPT to improve efficiency in clinical decision-making while underscoring the necessity of collaboration with healthcare professionals to guarantee comprehensive patient care.
