arXiv:2606.14697v1 Announce Type: cross Abstract: Building trustworthy medical multimodal large language models (MLLMs) is critical for reliable clinical decision support. Existing medical hallucination benchmarks mainly focus on data collection, but often ignore where hallucinations originate within the reasoning process. We find that hallucination sources vary across samples: errors may arise from visual misrecognition, incorrect medical knowledge recall, or flawed reasoning integration. To enable source-level hallucination diagnosis, we introduce ClinHallu, a benchmark for stage-wise halluc
The rapid deployment of medical multimodal LLMs (MLLMs) necessitates robust and transparent evaluation methods to ensure their reliability and trust in critical applications.
This benchmark directly addresses the trustworthiness of AI in sensitive domains, which is crucial for adoption and preventing harmful errors in medical diagnosis and decision support.
The ability to diagnose stage-wise hallucinations will enable developers to pinpoint and mitigate specific error sources in medical MLLMs, leading to more reliable AI systems.
- · Medical AI developers
- · Healthcare providers
- · Patients
- · AI safety researchers
- · Untrustworthy medical AI solutions
- · Developers neglecting evaluation
Improved debugging and development of medical MLLMs leading to higher accuracy and safety.
Increased adoption of AI in clinical settings due to greater trust and explainability.
Potential for new regulatory frameworks and certification processes for medical AI emphasizing explainable error diagnosis.
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Read at arXiv cs.AI