{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T05:27:00Z","timestamp":1792733220967,"version":"4.5.0"},"reference-count":28,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T00:00:00Z","timestamp":1784160000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Digit. Health"],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Responsible artificial intelligence (AI) in medical imaging requires more than high diagnostic accuracy; it also requires transparent reasoning, equitable performance across patient subgroups, privacy protection, calibrated uncertainty, and clinical trustworthiness.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This PRISMA-informed systematic review synthesized 24 studies published between 2020 and 2025 that used AI or deep learning for disease detection or diagnostic support in X-ray, CT, MRI, mammography, ultrasound, dermoscopy, retinal fundus imaging, optical coherence tomography, and abdominal CT. PubMed, Scopus, Web of Science, IEEE Xplore, ScienceDirect, SpringerLink, and Google Scholar were searched, and extracted evidence was appraised qualitatively using adapted QUADAS-2 and PROBAST-AI domains.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The included studies covered lung diseases, COVID-19, pneumonia, lung cancer, breast cancer, melanoma and other dermatological disorders, brain tumors, diabetic retinopathy, chest abnormalities, and pancreatic ductal adenocarcinoma. Explainability methods such as Grad-CAM, Grad-CAM++, LIME, SHAP, saliency maps, and layer-wise relevance propagation dominated the evidence base, whereas fairness, privacy-preserving learning, uncertainty estimation, and human-centered clinical trust were represented by fewer studies. Several papers reported accuracy or sensitivity above 90%, but these values should be interpreted cautiously because many studies relied on internal validation, curated public datasets, class-balanced splits, augmentation, or limited demographic reporting.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>Responsible medical-imaging AI should be evaluated through multidimensional evidence, including external and subgroup validation, calibration, privacy risk analysis, clinician-centered explanation assessment, workflow integration, regulatory readiness, and post-deployment monitoring.<\/jats:p>\n                  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