Agentic AI-based Framework for Mitigating Premature Diagnostic Handoff and Silent Hallucination in Healthcare Applications
arXiv:2606.18068v1 Announce Type: new Abstract: Recent advances in Large Language Models (LLMs) and multi-agent systems have driven the rise of Agentic AI, showing promise for medical reasoning. However, open-ended conversational agents remain prone to two critical failure modes: premature diagnostic handoff and silent clinical hallucinations that may go undetected before reaching the patient. In this work, we propose a multi-agent framework that addresses both issues by replacing ``LLM-as-a-judge'' routing with deterministic orchestration constraints. The framework incorporates two safety mec
The rapid advancement of LLMs and multi-agent systems has brought their application in critical fields like healthcare to the forefront, necessitating robust safety mechanisms.
This development directly addresses critical safety issues in AI-driven healthcare, potentially accelerating the adoption of agentic AI by increasing trust and mitigating risks.
The proposed framework shifts from an 'LLM-as-a-judge' paradigm to deterministic orchestration, significantly enhancing reliability and safety in AI medical reasoning.
- · Healthcare AI developers
- · Patients
- · AI safety researchers
- · Medical institutions
- · AI developers ignoring safety
- · Companies relying on unconstrained LLM deployments
Wider acceptance and deployment of AI agents in clinical settings due to improved safety and reliability.
Increased regulatory scrutiny and standardization efforts for AI agent safety in medicine, informed by these frameworks.
A shift in AI development methodologies towards safety-by-design and deterministic control for critical applications beyond healthcare.
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Read at arXiv cs.AI