This multicenter, peer-reviewed study aimed to assess the diagnostic and treatment accuracy of ChatGPT-4.0, a transformer-based large language model, for osteoporosis management. Out of 206 patient cases, 100 were selected for ChatGPT evaluation, ensuring diversity in the presented clinical group. The demographics considered were adults aged 50 years or older, diagnosed with osteoporosis using DXA scans (T-score ≤ −2.5). ChatGPT was prompted to provide diagnostic labels based on the patient data, along with both pharmacologic and non-pharmacologic treatment plans. These were then compared to recommendations from five independent physical medicine specialists. After the formation of the recommendations, the same specialists were asked to rate the diagnostic assessment and treatment plans generated by ChatGPT on a 4-point Likert scale. The blinded clinician ratings classified 91% of ChatGPT’s responses as “completely accurate and comprehensive,” while 8% were “accurate but incomplete,” primarily lacking detail on medication dosing or exercise specificity. Only 1% were “partially accurate,” and no responses were rated “incorrect.” ChatGPT identified appropriate pharmacologic therapies in 95% of cases and made consistent non-pharmacologic recommendations in 94%. Cohen’s kappa statistical analysis indicated strong agreement with clinician consensus. ChatGPT was also found to deliver results significantly faster than human physicians (2.3 vs. 5.4 minutes; p<0.001).