Researchers have developed an agentic AI framework designed to improve diagnostic accuracy in healthcare applications by addressing premature handoffs and silent hallucinations. The system utilizes a multi-agent approach with two key safety mechanisms: a state-tracking gate enforcing the OLDCARTS clinical protocol and an epistemic uncertainty quantification gate to detect divergent outputs. Evaluations using simulated patients and the Llama-3.1-70B-Instruct model showed a 49.3% diagnostic precision, an 11.3 percentage point improvement over a baseline, and a correlation between structured information gathering and reduced diagnostic uncertainty. AI
IMPACT This framework could lead to more reliable AI diagnostic tools in healthcare, reducing errors and improving patient safety.
RANK_REASON The cluster contains an arXiv paper detailing a new research framework and its evaluation.
- Agentic Ai
- arXiv
- CatalyzeX
- DagsHub
- Divyansh Srivastava
- Hugging Face
- large-language models
- Llama-3.1-70B-Instruct
- OLDCARTS
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