{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T05:04:02Z","timestamp":1784783042050,"version":"3.55.0"},"reference-count":70,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T00:00:00Z","timestamp":1784764800000},"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. Oncol."],"abstract":"<jats:p>Artificial intelligence (AI) is rapidly expanding across the radiation oncology workflow, with applications spanning imaging, contouring, treatment planning, quality assurance, outcome prediction, workflow automation, and clinical decision support. Although technical progress has accelerated substantially, successful clinical translation remains inconsistent. Many of the challenges limiting implementation are not unique to radiation oncology and have previously emerged across healthcare and other high-stakes industries. In this narrative review, we examine radiation oncology AI through the broader lens of cross-industry AI development and deployment. We first summarize the current landscape of AI applications in radiation oncology and then analyze representative examples of successful and unsuccessful AI implementation from healthcare and other sectors. These experiences reveal recurring themes that strongly influence clinical AI success, including data representativeness, robust validation, workflow-centered design, human-AI collaboration, uncertainty management, bias mitigation, operational boundaries, and continuous performance monitoring. We discuss how these lessons apply directly to radiation oncology, where AI systems must function within complex clinical workflows involving imaging, planning, adaptive treatment, quality assurance, and longitudinal patient management. Emerging agentic and multimodal AI systems further amplify both opportunities and risks associated with deployment. Ultimately, the future impact of AI in radiation oncology will likely depend less on isolated algorithmic performance than on the development of trustworthy clinical AI ecosystems. Successful implementation will require rigorous validation, seamless workflow integration, human oversight, regulatory governance, and continuous adaptation. Lessons from healthcare and other industries suggest that the greatest and most durable clinical value may arise from AI systems that augment human expertise, cognitive workflows, and multidisciplinary decision-making rather than replace clinical decision-makers.<\/jats:p>","DOI":"10.3389\/fonc.2026.1912362","type":"journal-article","created":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T04:30:02Z","timestamp":1784781002000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Trustworthy artificial intelligence in radiation oncology: cross-industry lessons for development, validation, and deployment"],"prefix":"10.3389","volume":"16","author":[{"given":"Malinda","family":"Zhu","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology, University of Rochester","place":["Rochester, NY, United 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