{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T04:09:49Z","timestamp":1791518989376,"version":"4.3.4"},"reference-count":120,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J Med Internet Res"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec sec-type=\"background\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Explainable artificial intelligence (xAI) is increasingly used in medical imaging to enhance transparency, clinical interpretability, and trust in artificial intelligence (AI)\u2013assisted diagnostics, particularly in oncology. Evidence on how explainability is implemented, validated, and reported in cancer imaging remains fragmented.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"objective\">\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This scoping review aimed to systematically map research applying xAI methods to radiologic cancer imaging, summarize methodological and clinical trends, and identify persistent gaps in validation and integration.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"methods\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We conducted a structured search of PubMed and Scopus (final search executed on October 20, 2025), covering studies published from 2017 to December 2024. Eligible peer-reviewed articles using machine learning or deep learning were analyzed with a focus on xAI components. Data from 371 studies were extracted into predefined categories covering cancer type, imaging modality, AI model, xAI method, terminology, validation, code availability, and decision support system integration.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"results\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Studies focused primarily on breast (112\/371, 30.2%), lung (87\/371, 23.5%), and brain (56\/371, 15.1%) cancers, with prostate, thyroid, and liver cancers also represented. The primary imaging modalities were computed tomography (139\/371, 37.5%) and magnetic resonance imaging (104\/371, 28%). Deep learning was used in 70.1% (260\/371) of studies, classical machine learning in 18.1% (67\/371), hybrid pipeline methods for 10% (37\/371), and emerging concept-, prototype-, or causal-based approaches accounted for 1.9% (7\/371) of studies. Post hoc xAI methods were dominant (305\/371, 82.2%), with visualization (163\/371, 53.4%), and feature relevance (111\/371, 36.4%) as the most common subcategories. Hybrid post hoc or inherent approaches comprised 12.1% (45\/371) and intrinsically interpretable methods 5.7% (21\/371). Data sources were mostly public (149\/371, 40.2%) or mixed (100\/371, 26.9%); 22.9% (85\/371) used private institutional datasets, and 7.8% (29\/371) did not report data sources. Among validated studies, expert or user-based validation was most common (104\/193, 53.9%), followed by mixed methods (74\/193, 38.3%), while quantitative metrics (10\/193, 5.2%) and clinical knowledge\u2013based (8\/193, 4.1%) validation remained rare. Only 17.5% (65\/371) of studies provided code and 12.1% (45\/371) reported decision support system integration, with few achieving actual clinical deployment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"conclusions\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This scoping review maps xAI implementation across multiple cancer imaging modalities, revealing methodological inconsistency and insufficient validation. Most research emphasizes visualization over quantitative interpretability, and few models are reproducible or clinically implemented. These findings provide an evidence base for researchers, clinicians, and regulators to prioritize standardization of xAI reporting, quantitative validation, and user-centered frameworks to advance trustworthy AI in oncology imaging.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2196\/80645","type":"journal-article","created":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T16:05:08Z","timestamp":1770998708000},"page":"e80645-e80645","source":"Crossref","is-referenced-by-count":2,"title":["Explainable AI in Cancer Imaging: Scoping Review of Methods, Modalities, and Clinical Integration"],"prefix":"10.2196","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8605-8593","authenticated-orcid":false,"given":"Dimitris","family":"Fotopoulos","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3457-1333","authenticated-orcid":false,"given":"Ioannis","family":"Ladakis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5613-652X","authenticated-orcid":false,"given":"Dimitrios","family":"Filos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3987-1789","authenticated-orcid":false,"given":"Pedro A","family":"Moreno-S\u00e1nchez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0029-1771","authenticated-orcid":false,"given":"Mark","family":"van Gils","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8915-6658","authenticated-orcid":false,"given":"Ioanna","family":"Chouvarda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,5,20]]},"reference":[{"issue":"3","key":"R1","doi-asserted-by":"publisher","first-page":"209","DOI":"10.3322\/caac.21660","article-title":"Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"71","author":"Sung","journal-title":"CA Cancer J Clin"},{"issue":"3","key":"R2","doi-asserted-by":"publisher","first-page":"229","DOI":"10.3322\/caac.21834","article-title":"Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"74","author":"Bray","journal-title":"CA Cancer J Clin"},{"issue":"1","key":"R3","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-024-01103-x","article-title":"A systematic review and meta-analysis of artificial intelligence versus clinicians for skin cancer diagnosis","volume":"7","author":"Salinas","journal-title":"NPJ Digit Med"},{"key":"R4","doi-asserted-by":"publisher","DOI":"10.3389\/frai.2024.1398205","article-title":"Diagnostic performance of AI-based models versus physicians among patients with hepatocellular carcinoma: a systematic review and meta-analysis","volume":"7","author":"Al-Obeidat","journal-title":"Front Artif Intell"},{"key":"R5","doi-asserted-by":"publisher","DOI":"10.2196\/56851","article-title":"Development and validation of a computed tomography-based model for noninvasive prediction of the T Stage in gastric cancer: multicenter retrospective study","volume":"26","author":"Tao","journal-title":"J Med Internet Res"},{"issue":"11","key":"R6","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2025.42338","article-title":"FDA approval of artificial intelligence and machine learning devices in radiology: a systematic review","volume":"8","author":"Sivakumar","journal-title":"JAMA Netw Open"},{"issue":"2","key":"R7","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pdig.0000016","article-title":"To explain or not to explain?-Artificial intelligence explainability in clinical decision support systems","volume":"1","author":"Amann","journal-title":"PLOS Digit Health"},{"issue":"1","key":"R8","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-024-01232-3","article-title":"Navigating the EU AI act: implications for regulated digital medical products","volume":"7","author":"Aboy","journal-title":"NPJ Digit Med"},{"key":"R9","unstructured":"Artificial intelligence and amending regulations. 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