A comprehensive review: deep learning-powered revolution in antitumor drug research-exploring multimodal data integration and ethical governance framework

Abstract

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. Schematic of the AI-driven therapeutic pipeline. By integrating multi-modal biomedical data, AI models enable target discovery, molecular design, and patient stratification. This process, governed by an ethical framework ensuring data privacy, transparency, and accountability, translates into superior clinical outcomes through enhanced therapeutic efficacy and reduced side effects.

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