Hallucination in Medical Imaging AI: A Cross-Modality Analytical Framework for Taxonomy, Detection, and Mitigation under Regulatory Constraints
arXiv:2606.13211v1 Announce Type: new Abstract: AI systems are being deployed across medical imaging faster than their failure modes are understood. At this point in time, the failure of greatest clinical concern is hallucination: clinically plausible but factually incorrect outputs, including fabricated anatomical structures, missed findings, incorrect laterality, and invented measurements in generated reports, with direct consequences, for example, for biopsy decisions, staging, and treatment planning. This structured narrative synthesizes peer-reviewed studies, benchmark datasets, and FDA r
The rapid deployment of AI in medical imaging necessitates a structured understanding of its failure modes, particularly 'hallucination,' before widespread clinical integration.
Hallucinations in medical AI pose direct risks to patient safety and treatment efficacy, requiring robust frameworks for detection and mitigation to ensure responsible innovation and regulatory compliance.
The focus shifts from mere AI deployment to rigorous validation and understanding of AI failure modes, particularly in high-stakes medical applications, impacting development methodologies and regulatory scrutiny.
- · AI safety researchers
- · Medical AI validation platforms
- · Patients
- · Regulatory bodies
- · Untested AI vendors
- · Rushed AI deployments
- · Healthcare providers relying solely on black-box AI
Increased scrutiny and demand for explainability and robustness in medical AI systems.
Development of new benchmark datasets and common evaluation protocols specifically targeting hallucination and safety in medical AI.
Potential for a specialized 'medical AI assurance' industry to emerge, focused on certification and continuous monitoring of AI reliability.
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Read at arXiv cs.AI