What's new

News, insights and publications.

News, practical tips, insights and academic publications from the Rhazes team.

Explore scientific publications

Academic publication

Challenges and Solutions in Applying Large Language Models to Guideline-Based Management Planning and Automated Medical Coding in Health Care: Algorithm Development and Validation

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.

Read article

Academic publication

Rapidly Benchmarking Large Language Models for Diagnosing Comorbid Patients: Comparative Study Leveraging the LLM-as-a-Judge Method

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.

Read article

Insights

Why strategy will make or break the UK’s AI healthcare future

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.

Read article

Academic publication

A systematic evaluation of the performance of GPT-4 and PaLM2 to diagnose comorbidities in MIMIC-IV patients

The results suggest that artificial intelligence (AI) has the potential when working alongside clinicians to reduce cognitive errors which lead to hundreds of thousands of misdiagnoses every year. However, human oversight of AI remains essential: LLMs cannot replace clinicians, especially when it comes to human understanding and empathy. Furthermore, a significant number of challenges in incorporating AI into health care exist, including ethical, liability and regulatory barriers.

Read article