Performance vs Consistency: Evaluating a Foundation Model in Lung-RADS Screening
Bridging the gap between foundation model versatility and the rigorous demands of standardized lung cancer screening.
Foundation models have recently demonstrated strong capabilities across a wide range of medical imaging tasks. However, their performance in structured clinical interpretation settings remains insufficiently explored. In lung cancer screening, interpretative variability persists ...
ArXiv CS.CV
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This research highlights the critical trade-off between the high-performing predictive power of foundation models and the rigid consistency required for clinical diagnostic standards. Establishing a baseline for AI reliability in Lung-RADS screening is a vital step toward integrating autonomous diagnostic tools into high-stakes oncology workflows.
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