Zhaohui Su, VP of Biostatistics at Ontada, shared a post about an article written by colleagues on LinkedIn:
“Artificial intelligence (AI) and machine learning (ML) play a pivotal role in oncology drug development, supporting processes from early discovery and screening to clinical decision making and regulatory oversight. AI platforms accelerate innovation, reduce costs, and improve outcomes by identifying drug targets, optimizing molecular design, and aiding both preclinical and clinical decisions. Advanced models such as deep learning networks and large language models are noted for their strong performance.
Clinical decision support systems (CDSS) powered by AI are increasingly embedded into electronic health systems to recommend therapies based on clinical guidelines, molecular tumor board insights, and real-world data (RWD). The article highlights the importance of RWD and real-world evidence (RWE) in AI-driven oncology, which are essential for regulatory science and clinical decision-making.
Perspectives from regulatory agencies such as the U.S. FDA and the European Medicines Agency (EMA) are discussed, emphasizing their proactive engagement with AI/ML applications. Both agencies stress the need for transparency, validation, and risk-based oversight to ensure safe and effective integration of AI in cancer care.
Overall, this insightful review underscores AI’s central role in advancing cancer care while addressing challenges like data quality, bias, interpretability, and regulatory complexity.
Reference: Subhan, A., Manoharan, G. Advancing cancer care through artificial intelligence: from innovative models to clinical decision-making and regulatory integration. Clin. Cancer Bull. 4, 23 (2025).”
Title: Advancing cancer care through artificial intelligence: from innovative models to clinical decision-making and regulatory integration
Authors: Abuhurera Subhan, Geetha Manoharan
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