Agentic AI-based Framework for Mitigating Premature Diagnostic Handoff and Silent Hallucination in Healthcare Applications

2026-06-16 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Medical Devices & Health Technology · Depth: Expert, quick

Summary

An Agentic AI-based framework has been developed to address premature diagnostic handoff and silent clinical hallucinations in healthcare applications utilizing Large Language Models (LLMs). Published on 2026-06-16, this multi-agent system replaces "LLM-as-a-judge" routing with deterministic orchestration constraints. It incorporates two safety mechanisms: a neuro-symbolic state-tracking gate that enforces completeness of the OLDCARTS clinical protocol before diagnostic transitions, and an epistemic uncertainty quantification (UQ) gate that computes semantic entropy across K=5 diagnostic samples to intercept divergent outputs. Evaluated using simulated patient agents powered by the llama-3.1-70b-instruct model on 150 test cases, the architecture achieved 49.3% diagnostic precision, an 11.3 percentage point improvement over an unconstrained baseline. The study also found a negative correlation (r = -0.181, p < 0.05) between OLDCARTS completeness and reduced diagnostic uncertainty.

Key takeaway

For Machine Learning Engineers developing LLM-based diagnostic tools in healthcare, you must integrate robust safety mechanisms to prevent premature diagnostic handoff and silent hallucinations. Consider implementing deterministic orchestration instead of "LLM-as-a-judge" routing. Your systems should enforce clinical protocols like OLDCARTS completeness and utilize epistemic uncertainty quantification to identify and intercept divergent diagnostic outputs, significantly improving precision and patient safety.

Key insights

Multi-agent systems with deterministic orchestration and uncertainty quantification can mitigate LLM diagnostic failures in healthcare.

Principles

• Deterministic orchestration enhances reliability over LLM-as-a-judge.
• Structured information gathering reduces diagnostic uncertainty.
• Epistemic uncertainty quantification identifies divergent outputs.

Method

The framework employs a neuro-symbolic state-tracking gate for OLDCARTS protocol enforcement and an epistemic UQ gate computing semantic entropy across K=5 samples to intercept divergent diagnoses.

In practice

• Implement OLDCARTS protocol enforcement in diagnostic AI.
• Use semantic entropy to identify LLM output divergence.
• Replace "LLM-as-a-judge" with deterministic routing.

Topics

• Agentic AI
• Large Language Models
• Healthcare Diagnostics
• Uncertainty Quantification
• Multi-agent Systems
• OLDCARTS Protocol

Best for: AI Scientist, Research Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.