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By synthesizing evidence, this study identifies, strengths, limitations, and areas requiring further research.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Methods<\/jats:title>\n            <jats:p>The review followed the Joanna Briggs Institute's methodology, Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines, and the Population, Concept, and Context mnemonic. Searches were conducted across Embase, IEEE Xplore, PubMed, Scopus, and Web of Science (January 2014-September 2022), targeting English-language quantitative studies in Q1 journals (SciMago Journal and Country Ranking\u2009&gt;\u20091) that used ML to evaluate clinical outcomes for human cancer patients with commonly available data. Eligible models required external validation, clinical utility assessment, and performance metric reporting. Studies involving genetics, synthetic patients, plants, or animals were excluded. Results were presented in tabular, graphical, and descriptive form.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>From 4023 deduplicated abstracts and 636 full-text reviews, 56 studies (2018\u20132022) met the inclusion criteria, covering diverse cancer types and applications. Convolutional neural networks were most prevalent, demonstrating high performance, followed by gradient- and decision tree-based algorithms. Other algorithms, though underrepresented, showed promise. Lung and digestive system cancers were most frequently studied, focusing on diagnosis and outcome predictions. Most studies were retrospective and multi-institutional, primarily using image-based data, followed by text-based and hybrid approaches. Clinical utility assessments involved 499 clinicians and 12 tools, indicating improved clinician performance with AI assistance and superior performance to standard clinical systems.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Discussion<\/jats:title>\n            <jats:p>Interest in ML-based clinical decision-making has grown in recent years alongside increased multi-institutional collaboration. However, small sample sizes likely impacted data quality and generalizability. Persistent challenges include limited international validation across ethnicities, inconsistent data sharing, disparities in validation metrics, and insufficient calibration reporting, hindering model comparison reliability.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusion<\/jats:title>\n            <jats:p>Successful integration of ML in oncology decision-making requires standardized data and methodologies, larger sample sizes, greater transparency, and robust validation and clinical utility assessments.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Other<\/jats:title>\n            <jats:p>Financed by FCT\u2014Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia (Portugal, project LA\/P\/0063\/2020, grant 2021.09040.BD) as part of CSS\u2019s Ph.D. This work was not registered.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Graphical Abstract<\/jats:title>\n            <jats:p>A visual summary (graphical abstract) encapsulating the core findings and future directions of ML applications in oncology patient care.\n<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/s12874-025-02463-y","type":"journal-article","created":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:32:06Z","timestamp":1740123126000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Externally validated and clinically useful machine learning algorithms to support patient-related decision-making in oncology: a scoping review"],"prefix":"10.1186","volume":"25","author":[{"given":"Catarina Sousa","family":"Santos","sequence":"first","affiliation":[]},{"given":"M\u00e1rio","family":"Amorim-Lopes","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,2,21]]},"reference":[{"key":"2463_CR1","doi-asserted-by":"crossref","unstructured":"Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. 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