ABSTRACT
BACKGROUND: The complex nature of rheumatic diseases poses considerable challenges for clinicians when developing individualized treatment plans. Large language models (LLMs) such as ChatGPT could enable treatment decision support. OBJECTIVE: To compare treatment plans generated by ChatGPT-3.5 and GPT-4 to those of a clinical rheumatology board (RB). DESIGN/METHODS: Fictional patient vignettes were created and GPT-3.5, GPT-4, and the RB were queried to provide respective first- and second-line treatment plans with underlying justifications. Four rheumatologists from different centers, blinded to the origin of treatment plans, selected the overall preferred treatment concept and assessed treatment plans' safety, EULAR guideline adherence, medical adequacy, overall quality, justification of the treatment plans and their completeness as well as patient vignette difficulty using a 5-point Likert scale. RESULTS: 20 fictional vignettes covering various rheumatic diseases and varying difficulty levels were assembled and a total of 160 ratings were assessed. In 68.8% (110/160) of cases, raters preferred the RB's treatment plans over those generated by GPT-4 (16.3%; 26/160) and GPT-3.5 (15.0%; 24/160). GPT-4's plans were chosen more frequently for first-line treatments compared to GPT-3.5. No significant safety differences were observed between RB and GPT-4's first-line treatment plans. Rheumatologists' plans received significantly higher ratings in guideline adherence, medical appropriateness, completeness and overall quality. Ratings did not correlate with the vignette difficulty. LLM-generated plans were notably longer and more detailed. CONCLUSION: GPT-4 and GPT-3.5 generated safe, high-quality treatment plans for rheumatic diseases, demonstrating promise in clinical decision support. Future research should investigate detailed standardized prompts and the impact of LLM usage on clinical decisions.
Subject(s)
Clinical Decision-Making , Rheumatic Diseases , Humans , Rheumatic Diseases/therapy , Decision Support Techniques , Guideline Adherence , Rheumatology , Female , Male , Rheumatologists , Patient Care Planning , Practice Guidelines as TopicABSTRACT
Autologous hematopoietic stem cell transplantation (aHSCT) represents an effective treatment option in patients with severe forms of systemic sclerosis (SSc) by resetting the immune system. Nevertheless, secondary autoimmune disorders and progressive disease after aHSCT might necessitate renewed immunosuppressive treatments. This is particularly challenging when organ dysfunction, i.e., end-stage kidney failure, is present. In this case report, we present the unique case of a 43-year-old female patient with rapidly progressive diffuse systemic sclerosis who underwent aHSCT despite end-stage renal failure as consequence of SSc-renal crisis. Therefore, conditioning chemotherapy was performed with melphalan instead of cyclophosphamide with no occurrence of severe adverse events during the aplastic period and thereafter. After aHSCT, early disease progression of the skin occurred and was successfully treated with secukinumab. Thereby, to the best of our knowledge, we report the first case of successful aHSCT in a SSc-patient with end-stage kidney failure and also the first successful use of an IL-17 inhibitor to treat early disease progression after aHSCT.