Trustworthy Artificial Intelligence in Oncology: Managing Bias, Privacy, Explainability, Safety, and Clinical Accountability in Practice - Sangida; Nasir Uddin (American Journal of Technology Advancement, 2026)
Artificial intelligence (AI) has quickly gained traction in oncology, assisting with cancer diagnosis, prognosis prediction, treatment selection, and precision medicine. Numerous concerns, such as bias, privacy, explainability, safety, and clinical accountability, go against the rapid implementation of AI in clinical settings. To ensure trustworthiness of AI systems, safe, equitable, and patient-centered cancer care must be developed. This systematic review will assess evidence on trusted AI applications in oncology and discuss current challenges and solutions in fairness, data privacy, interpretability, safety, validation, and clinical governance. We conducted a literature search in PubMed, Scopus, Web of Science, IEEE Xplore, and Embase, following PRISMA guidelines. We included only publications that evaluated key AI applications in oncology and addressed at least one key domain of trustworthy AI. We categorized all qualitative data by study characteristics, AI technique, clinical application, and trust-related aspects. The reviewed literature showed a wide range of diagnostic, histological, prognosis, response, and precision oncology applications of AI across various studies. Some studies reported positive clinical performance with deep learning and machine learning models; however, privacy concerns, limited external validation, a lack of clear accountability frameworks, limited model explainability, and concerns about dataset bias were commonly reported. Strategies for successful, trustworthy AI deployment included privacy-preserving approaches, fairness assessment, explainable AI methods, and robust clinical validation. AI is a significant player in oncology, but it must be implemented appropriately and ethically, with the right clinical protocols in place. A key focus of future research is developing clear, secure, fair, and clinically proven AI systems to support the safe and equitable use of AI in cancer care.