Clinical Translation of Artificial Intelligence in Oncology: Real-World Validation, Workflow Integration, and Precision Medicine Applications
Year: 2026
Type: article
Abstract: Oncology AI has grown at an exponential rate in recent years. AI has solutions to many cancer issues. AI solutions are being developed by medical facilities, hospital systems, and technology companies to improve clinical decision-making, clinical efficiency, and accessibility to cancer care while also providing high-value, safe oncology treatment. AI has performed well in image analysis, predictive analytics, and precision oncology delivery. However, AI technologies are seldom deployed, and their impact on patient outcomes is uncertain. AI implementation in oncology faces significant challenges due to biased and heterogeneous data, burdens associated with data management and collection, a lack of standardised research reporting, difficulties associated with clinical validation, workflow and user-design issues, out-of-date legal and regulatory frameworks, and dynamic knowledge and data. Key stakeholders can overcome barriers to AI implementation in oncology by training and educating the oncology workforce, standardising data, model validation approaches, and legal and safety regulations, funding and conducting future research, and developing, studying, and deploying AI tools through multidisciplinary collaboration.
Author: Ohmini Krishnamurthy Rajendran
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W7163816499