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This study introduces and evaluates 2 LLM-based frameworks, implemented within the Rhazes Clinician platform, designed to address these challenges: generation-assisted retrieval-augmented generation (GARAG) for automated evidence-based treatment planning and generation-assisted vector search (GAVS) for automated medical coding.

We have released one of our biggest updates yet across the entire Rhazes Clinician platform. Everything is now faster, cleaner, more accurate, and more intuitive, making the clinical workflow smoother than ever.

In this study, we set out to compare the diagnostic ability of 18 LLMs from Google, OpenAI, Meta, Mistral, Cohere, and Anthropic, using 3 prompts, 2 temperature settings, and 1000 randomly selected Medical Information Mart for Intensive Care-IV (MIMIC-IV) hospital admissions. We also explore improving the diagnostic hit rate of GPT-4o 05‐13 with retrieval-augmented generation (RAG) by utilizing reference ranges provided by the American Board of Internal Medicine.

AI is no longer a nice-to-have for modern healthcare. It is essential to improving patient outcomes, reducing pressure on staff, and creating a more resilient system for the future. The question is not whether to embrace AI, but how to do so quickly, effectively, and in a way that works for both patients and clinicians. The UK has every reason to seize this opportunity, and every capability to make it happen.