Do No Harm? Hallucination and Actor-Level Abuse in Web-Deployed Medical Large Language Models
arXiv:2605.20591v1 Announce Type: new Abstract: Medical large language models (LLMs), including custom medical GPTs (MedGPTs) and open-source models, are increasingly deployed on web platforms to provide clinical guidance. However, they pose risks of hallucination, policy noncompliance, and unsafe design. We conduct a large-scale assessment of 6,233 MedGPTs, evaluating a stratified sample of 1,500, together with 10 open-source LLMs. We introduce two frameworks: MedGPT-HEval for hallucination detection and an LLM-based pipeline for assessing policy violations and developer intent. Our results s
The rapid deployment of medical Large Language Models (LLMs) on web platforms necessitates immediate, large-scale safety assessments to understand their real-world risks as they become more accessible and integrated into healthcare. The specific mention of 'MedGPTs' being deployed indicates a current and urgent evaluation தேவை.
Medical LLMs offer potential benefits but also introduce critical concerns regarding patient safety due to hallucinations, policy noncompliance, and potential for abuse, directly impacting the adoption and regulation of AI in sensitive sectors. This assessment highlights the need for robust evaluation frameworks and regulatory oversight.
The understanding of the specific risks associated with web-deployed medical LLMs, particularly concerning hallucinations and 'actor-level abuse,' will deepen, prompting clearer development guidelines and potentially stricter regulatory frameworks for their use in healthcare. This will likely shift the focus from pure capability to safety and guardrails.
- · AI safety researchers
- · Healthcare regulatory bodies
- · Validation platform providers
- · Patients (from improved safety)
- · Developers of unsafe MedGPTs
- · Unregulated AI deployment platforms
- · Users relying on unverified AI medical advice
- · Companies neglecting ethical AI development
Enhanced scrutiny and possibly new certification requirements will emerge for medical AI applications, potentially slowing their market entry.
The public trust in AI generally, and medical AI specifically, will become highly dependent on the effective mitigation of these identified risks, influencing broader AI adoption.
This could lead to a 'flight to quality' in medical AI, where only models from highly reputable and compliant developers gain widespread acceptance, consolidating the market.
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