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Kimi K3 AI model successfully reads a chest X-ray after OpenMed removes all 23 patient identifiers from the DICOM data, ensuring privacy. The model correctly identifies a left-sided whiteout.
LMU researchers developed a Quantum Boltzmann Machine model for pneumonia detection from chest X-rays. Using fewer than 9,000 parameters (vs. millions in classical CNNs), the model achieves 84–86% accuracy, demonstrating potential advantages of quantum machine learning for small medical datasets.
This paper studies the fairness problem of thresholded subgroup underdiagnosis in long-tailed chest X-ray classification, demonstrating that rare-label fairness depends jointly on the finding, subgroup, and operating threshold, not on label frequency or ranking metrics alone.
This paper introduces a pipeline that converts free-text chest radiograph reports into multi-label matrices with a single structured annotation pass, enabling reconfiguration of label schemas via dictionary edits without relabeling, saving significant cost and time.
Proposes an attention-guided encoder-decoder for longitudinal medical visual question answering, using a frozen DINO-based mask generator and auxiliary losses to improve consistency and interpretability, achieving strong results on the Medical-Diff-VQA benchmark.