Trust but Verify: Mitigating Medical Hallucinations via Post-Hoc Adversarial Auditing and Multi-Agent Feedback Loops
arXiv:2606.14149v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed in healthcare settings, yet their tendency to hallucinate poses risks when clinical decisions are involved. This study examine whether LLMs recommend recently banned or withdrawn pharmaceuticals when answering clinical questions and tests an agent-based method for reducing such errors. We developed a five-agent "Trust but Verify" system using a single LLM backbone. To measure regulatory knowledge obsolescence, we created an adversarial dataset of 103 clinical MCQs where historically correct a
The urgent need to integrate AI safely into high-stakes environments like healthcare is driving rapid innovation in AI safety and verification. This paper addresses a critical, immediate challenge for LLM deployment.
Ensuring the reliability and safety of LLMs in medical contexts is paramount for preventing harm and building public trust, which will dictate the pace of AI adoption in critical sectors.
This research introduces concrete methods for LLMs to self-correct and verify high-stakes information, allowing for more robust and trustworthy AI applications in medicine.
- · AI safety researchers
- · Healthcare technology providers
- · Patients
- · LLM developers
- · Developers of unverified medical AI models
- · Hospitals resistant to AI integration
Increased adoption of LLMs in medical diagnostics and patient care with enhanced safety protocols.
Development of industry standards and regulatory frameworks requiring agent-based verification for AI in critical applications.
Extension of adversarial auditing and multi-agent systems to other high-stakes domains beyond healthcare, such as finance or legal sectors.
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