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Life sciences · Review
International Journal of Drug Delivery Technology · October 23, 2026
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Background: There has been increasing interest in the clinical use of artificial intelligence (AI) across many medical fields including oncology.Some areas for AI application in clinical oncology include cancer awareness, cancer screening, cancer diagnosis, pathology, treatment planning, drug discovery, radiation therapy and cancer survivorship.By processing large and complex clinical datasets, AI offers the ability to enhance patient-specific diagnosis, improve accuracy, and increase efficiency Method: This systematic review evaluating clinical use of AI in oncology followed PRISMA 2020 guidelines.Records were retrieved from 2015-2026 using PubMed, Scopus, Web of Science, and CNKI.Included study types encompassed all forms of AI application in clinical oncology (e.G. Imaging, pathology, treatment planning, radiation oncology, drug discovery, surgery, rehabilitation, cancer survivorship).Results: 250 initial records were identified.Following removal of duplicates 200 records were screened, 50 full text articles were eligible for full text review, and 30 articles were included in the qualitative synthesis.The reviews considered cancer diagnosis and imaging, digital pathology, radiation oncology, treatment planning, precision oncology, drug discovery, surgery, rehabilitation, and cancer survivorship.Overall, the included studies report potential improvement in diagnostic accuracy, tumor detection, prognostic prediction, treatment optimization, and clinical decision support.Conclusion: AI has substantial potential for a myriad of clinical applications within oncology; however, limitations including methodological variability, lack of external validation, lack of interpretability, data privacy concerns, data bias, and regulatory issues warrant further attention.Prospective and multicenter studies are needed to assess the utility, safety and portability of AI in oncology clinical practice.