CardioLens: Revealing the Clinical Reality Gap of MLLMs via Multi-Sequence Cardiac MRI Evaluations
arXiv:2606.00123v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have shown strong performance on public medical benchmarks, yet existing evaluations often remain weak proxies for clinical use, relying on isolated inputs and simplified recognition-style tasks. We introduce CardioLens, a leakage-resistant evaluation testbed for multi-sequence Cardiovascular Magnetic Resonance (CMR), constructed from private hospital archives through a rigorous report-to-QA construction and verification pipeline. CardioLens contains 473,896 slices and 13,494 verified QA pairs across 4D
The proliferation of MLLMs in medical contexts necessitates robust, clinically relevant evaluation methods to bridge the gap between benchmark performance and real-world utility.
Sophisticated readers should understand that current AI medical benchmarks are often insufficient, and initiatives like CardioLens highlight the need for more rigorous, privacy-preserving validation, which is crucial for AI adoption in healthcare.
The focus for MLLM development in healthcare shifts towards leakage-resistant, multi-modal evaluations derived from real clinical data, moving beyond simplified recognition tasks.
- · AI-powered medical diagnostics
- · Hospitals with rich, well-structured archives
- · Patients requiring advanced cardiac diagnostics
- · AI evaluation framework developers
- · MLLM developers relying solely on public benchmarks
- · Companies offering simplistic AI diagnostic tools
- · Ethical frameworks ignoring data leakage risks
CardioLens provides a more realistic assessment of MLLM capabilities in cardiology, potentially slowing premature deployment of unvalidated models.
This more rigorous evaluation could drive specialized MLLM research towards multi-sequence and context-aware understanding, improving clinical relevance.
The success of CardioLens might inspire similar leakage-resistant, private-data-driven evaluation platforms across other medical specialities, accelerating safe AI integration.
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Read at arXiv cs.LG