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A comprehensive review: deep learning-powered revolution in antitumor drug research-exploring multimodal data integration and ethical governance framework
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DOI：10.1016/j.lddd.2025.100235.png)
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Abstract
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The traditional paradigm of antitumor drug development is plagued by protracted timelines, exorbitant costs, and high attrition rates, creating a pressing need for innovative solutions. Despite the demonstrable efficiency gains brought by artificial intelligence (AI) to antitumor drug research, a systematic examination of its ethical implications and a clear governance pathway are conspicuously absent from the literature. This gap poses a substantial barrier to the full realization of AI's potential. Our review aims to bridge this critical gap by first delineating AI's role in revolutionizing key developmental stages and then providing a thorough critique of the ensuing ethical risks. Our analysis demonstrates that AI significantly accelerates antitumor drug development by enhancing target discovery, molecular design, and clinical trial efficiency, while also introducing ethical risks such as data bias and accountability gaps. To address these challenges, we propose a technology-ethics-law trinity governance framework, which integrates explainable AI, federated learning, ethical oversight, and international data-sharing alliances. This model ensures that AI-driven innovations align with patient rights and global equity, fostering sustainable and trustworthy progress in oncology drug research.
Keywords:
Artificial intelligence
Antitumor drug development
Ethical challenges
Governance system
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