LLM-Generated Transplant Algorithms: Comparing GPT and Gemini in Producing Step-Wise Diagnostic and Management Pathways

Background Solid organ transplantation relies on dynamic, evidence-based clinical algorithms. This study evaluates the educational reliability and feasibility of large language models (LLMs) for rapid protocol drafting by comparing GPT-4 and Gemini 2.5 Pro in generating step-wise diagnostic and management algorithms for common post-transplant scenarios. We assess LLMs as tools for synthesizing existing guidance rather than proposing new clinical practice guidelines. Methods Ten high-stakes post-transplant scenarios (eg, acute cellular rejection and unexplained graft dysfunction) were evaluated. Both LLMs received identical, structured prompts. Three transplant specialists independently scored each algorithm using a 5-point rubric for Clinical Concordance (primary outcome), Logical Flow, and Completeness. Inter-rater reliability was assessed with weighted kappa, and median scores were compared using non-parametric tests. Model identifiers, access dates, and prompting procedures were reported to support reproducibility. Results Inter-rater agreement was high (κ = 0.81). Gemini 2.5 Pro achieved a higher median Clinical Concordance score than GPT-4 (4.5 vs 3.8, P < .01) and higher Logical Flow and Completeness scores. Performance differences were most apparent in high-complexity scenarios requiring sequential differentiation of infectious vs alloimmune causes of graft dysfunction. Conclusion Gemini 2.5 Pro outperformed GPT-4 in generating clinically concordant, structured, and complete transplant algorithms. LLM outputs remain model- and version-dependent and require expert validation prior to any clinical use. In practice, LLMs should be used only within a governance framework (specialist review, institutional oversight, and periodic revalidation after model updates), not as autonomous clinical tools.