Artificial intelligence in cancer care: revolutionizing diagnosis, treatment, and precision medicine amid emerging challenges and future opportunities

Abstract

Artificial intelligence (AI) is increasingly being used in oncology to assist early detection, diagnosis, prognosis, treatment planning, and drug discovery. A systematic review is required to integrate evidence across various AI applications in cancer treatment. Systematically assess the use of AI applications in oncology, integrate study findings, highlight methodological issues, and set directions for future research. According to PRISMA guidelines, we searched systematically PubMed, Scopus, Web of Science, and IEEE Xplore between January 2013 and December 2024. Search terms integrated AI-related terms with oncology-related terms. Peer-reviewed original research studies with the use of AI on cancer care in human or human-derived datasets were the eligible studies. Two reviewers independently screened the studies, extracted data, and evaluated the risk of bias with suitable tools. 120 out of 4852 records were included according to inclusion criteria. Applications fell into five clusters: imaging/radiomics, genomics/biomarker discovery, drug discovery/repurposing, clinical decision support, and patient monitoring. Convolutional neural networks were predominant in imaging tasks, whereas ML classifiers were prevalent in genomics. Most of the studies showed improved performance with respect to conventional methods although most of the studies failed to conduct multi-center validation. Heterogeneity of data, interpretability limitations, and integration problems were common issues. AI holds great potential along the cancer care continuum but is at risk of being threatened by issues with data quality, validation, interpretability, and translation to practice. Addressing these issues will require collaboration among disciplines, reporting to standardized guidelines, and large-scale validation studies.

Keywords: Artificial intelligence; Biomarker discovery; Cancer diagnosis and treatment; Ethical and regulatory challenges; Machine learning; Precision oncology.