Role of Artificial Intelligence in Cancer Research Education

Abstract

Cancer remains a leading cause of mortality worldwide, driving urgent demand for advanced research and education frameworks capable of keeping pace with rapidly evolving scientific knowledge. Traditional cancer research education has increasingly demonstrated its limitations in preparing future researchers to handle the computational complexity of modern oncological data. Artificial intelligence (AI), encompassing machine learning, deep learning, and natural language processing, has emerged as a transformative force in both cancer research and biomedical education. This paper examines the multifaceted role of AI in cancer research education, exploring its applications in diagnostic training, genomic data interpretation, drug discovery pedagogy, and simulation-based learning environments. Despite documented benefits including enhanced diagnostic accuracy, accelerated competency development, and improved clinical reasoning a critical research gap persists: the absence of structured, oncology-specific AI curriculum frameworks with measurable educational outcomes. Current integration remains fragmented, with most programmes lacking standardised competency benchmarks and empirical evaluation. Key challenges include algorithmic bias, data privacy constraints, high computational costs, and insufficient interdisciplinary faculty expertise. This review synthesises evidence from peer-reviewed literature published between 2020 and 2025 to propose a forward-looking agenda for AI-integrated cancer research education. Future directions emphasise adaptive learning platforms, hybrid pedagogical models, and interdisciplinary training pathways as essential strategies for closing the gap between AI's clinical promise and its educational reality.