Su ZhaohuiOntada's vice president of biostatistics shared a post on LinkedIn about an article written by a colleague.
“artificial intelligence (AI) and machine learning (ml) plays a vital role in oncology drug development, supporting processes from early detection and screening to clinical decision-making and regulatory oversight. AI platforms accelerate innovation, reduce costs, and improve outcomes by identifying drug targets, optimizing molecular designs, and supporting both preclinical and clinical decision-making. Advanced models such as deep learning networks and large-scale language models are gaining attention for their superior performance.
Clinical decision support system (CDSS) AI-powered healthcare systems are increasingly being incorporated into electronic medical systems that recommend treatments based on clinical guidelines, molecular tumor board insights, and real-world data. (Rear wheel drive). This article highlights the importance of RWD and real-world evidence. (RWE) This is AI-driven oncology essential to regulatory science and clinical decision-making.
Perspectives from regulatory authorities such as the United States F.D.A. and the European Medicines Agency (EMA) We will discuss and highlight our active efforts in AI/ML applications. Both organizations emphasize the need for transparency, verification, and risk-based oversight to ensure the safe and effective integration of AI in cancer care.
Overall, this insightful review highlights the central role of AI in advancing cancer treatment while addressing challenges such as data quality, bias, interpretability, and regulatory complexity.
References: Subhan, A., Manoharan, G. Advances in cancer treatment with artificial intelligence: From innovative models to integrating clinical decision-making and regulation. Clin. Cancer bull. 4, 23 (2025).
title: Advances in cancer treatment with artificial intelligence: From innovative models to clinical decision-making and regulatory integration
author: Abuhurela Subhan, Geetha Manoharan
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