Tutorials on Clinical Decision Making

Watch: Agentic RAG vs RAGs by Rakesh Gohel RAG technology was replaced in healthcare due to critical limitations that undermined its reliability, safety, and scalability in clinical settings. While RAG systems initially promised to bridge knowledge gaps by grounding AI responses in curated data, healthcare clients like Schmitt-Thompson Clinical Content (STCC) and NHS South Yorkshire discovered systemic flaws that made them unsuitable for high-stakes applications. Below is a detailed breakdown of the challenges that led to its replacement.. In healthcare, hallucinations-fabricated or incorrect information generated by AI-pose life-threatening risks. STCC’s clinical triage guidelines, used by over 400 health systems, revealed that traditional RAG systems misinterpreted logic-based decision trees as natural language, leading to unsafe recommendations. For example, in 329 validated scenarios, 13 out of 16 guidelines fell below expert benchmarks, with errors in complex cases like Neurologic Deficit (85% accuracy vs. 96% benchmark). These inaccuracies stemmed from RAG’s inability to parse structured clinical logic, resulting in responses that prioritized fluency over factual correctness.