Agentic AI framework improves healthcare diagnostics by reducing errors

Researchers have developed a novel agentic AI framework designed to enhance diagnostic accuracy in healthcare applications by addressing premature handoffs and silent hallucinations. The system employs a multi-agent approach with deterministic orchestration, incorporating a neuro-symbolic state-tracking gate to ensure completeness of the OLDCARTS clinical protocol and an epistemic uncertainty quantification gate to detect divergent outputs. Evaluations using simulated patient agents and the Llama-3.1-70B-Instruct model demonstrated a significant improvement in diagnostic precision compared to unconstrained baselines. AI

IMPACT This framework could enhance diagnostic accuracy and patient safety in healthcare settings by reducing errors.

RANK_REASON The cluster contains an academic paper detailing a new AI framework for healthcare applications. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• Agentic Ai
• arXiv
• CatalyzeX
• DagsHub
• Divyansh Srivastava
• Hugging Face
• large-language models
• Llama-3.1-70B-Instruct
• OLDCARTS

• paper
• safety
• model release

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