Presents a clinically grounded privacy evaluation framework for medical LMs that measures verbatim memorization and semantic leakage of diagnoses across tiers of adversarial access, finding high leakage from routine metadata on a model trained on 378k notes.
Clinical validation of deep learning algorithms for radiotherapy targeting of non- small-cell lung cancer: an observational study.The Lancet Digital Health, 4(9):e657–e666
3 Pith papers cite this work. Polarity classification is still indexing.
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Anatomical location dominates prompt alignment in zero-shot VLM segmentation of NSCLC tumors, with VoxTell achieving DSC 0.613 comparable to fine-tuned baselines.
Prospective single-center validation of a cascade deep learning dermoscopy CDSS found no false negatives for five malignant lesions and 88.3% specificity, with quantitative IoU assessment of attention maps.
citing papers explorer
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Clinically Grounded Privacy Evaluation of Medical LMs
Presents a clinically grounded privacy evaluation framework for medical LMs that measures verbatim memorization and semantic leakage of diagnoses across tiers of adversarial access, finding high leakage from routine metadata on a model trained on 378k notes.
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Exploring Prompt Alignment with Clinical Factors in Zero-Shot Segmentation VLMs for NSCLC Tumor Segmentation
Anatomical location dominates prompt alignment in zero-shot VLM segmentation of NSCLC tumors, with VoxTell achieving DSC 0.613 comparable to fine-tuned baselines.
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Clinical Validation of the Melanoscope AI Mobile Dermoscopy Clinical Decision Support System
Prospective single-center validation of a cascade deep learning dermoscopy CDSS found no false negatives for five malignant lesions and 88.3% specificity, with quantitative IoU assessment of attention maps.