arXiv:2607.01973v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) are increasingly applied in medical tasks such as pathology description, report generation, and visual question answering. Medical Image Quality Assessment (MIQA) supports diagnostic accuracy and patient safety by determining whether images meet the standards required for clinical decision-making. Automating MIQA with VLMs may reduce workload, but their behavior under real-world conditions, where images may be degraded or textual context may affect judgments, should be further explored before deployment. We benchma
The rapid deployment of Vision-Language Models (VLMs) across various domains, including sensitive applications like healthcare, necessitates immediate and rigorous assessment of their reliability before widespread adoption.
This research is crucial for ensuring the safety and efficacy of AI in medical diagnostics, directly impacting patient outcomes and trust in AI-powered healthcare solutions.
The understanding of VLM limitations and vulnerabilities, particularly under real-world data corruption and bias in medical imaging, will evolve, leading to more robust deployment strategies and regulatory frameworks.
- · AI safety researchers
- · Healthcare providers adopting validated AI
- · Patients benefiting from reliable AI diagnostics
- · Uncritically deployed VLM solutions
- · Developers neglecting reliability testing
- · Healthcare systems facing liability from unchecked AI
Increased focus on transparent and explainable AI in medical imaging will become a standard.
New regulatory guidelines and industry standards for AI-driven Medical Image Quality Assessment (MIQA) will emerge.
The development of 'adversarial' medical data sets specifically designed to test VLM robustness will accelerate, fostering a more secure AI ecosystem.
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