The complexity of nanomedicine poses a significant challenge to traditional empirical methods. Machine learning (ML), with its ability to parse high-dimensional data and predict nonlinear interactions, is empowering AI nano fusion platforms, driving innovation in diagnostic and therapeutic evaluation paradigms. This review explores breakthrough applications of ML in core areas of nanomedicine, including intelligent diagnosis (such as ML enhanced nanosensors for high-precision noninvasive cancer early screening), precision therapy (such as closed-loop system driven rational design and delivery optimization of nanomedicine), safety assessment (such as interpretable AI prediction of biological and environmental toxicity of nanomaterials), and clinical translation (to address scientific challenges related to standardization, reproducibility, and regulatory). The paper provides an in-depth analysis of the key bottlenecks currently facing the transition from laboratory to clinical application, such as batch differences, dynamic interference, and regulatory lag. Based on recent research progress, a future path to achieve an intelligent closed-loop of “design diagnosis and treatment evaluation” is proposed, providing key insights for the development of the next generation of safe and efficient intelligent nanodiagnosis and treatment platforms.
Zhou et al. (Sat,) studied this question.