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<p>Nanomedicine has emerged as a dynamically evolving frontier in contemporary medical research. However, the development of nanomedicine is impeded by significant challenges due to its complex, multidisciplinary nature, necessitating the exploration of innovative solutions. Artificial intelligence (AI) has established itself as a pivotal and rapidly advancing domain within nanomedicine research. By leveraging its robust data processing and analytical capabilities, AI can efficiently analyze large datasets and accurately predict the properties and medical functions of nanomaterials. Over the past years, AI applications have proliferated across critical nanomedicine subdomains, including intelligent nanobiosensors for precision diagnostics, AI-optimized nanocarriers for targeted drug delivery, machine learning-guided adjuvant therapy systems, and predictive computational models for nanosafety evaluation. This review aims to provide a thorough analysis of AI’s influence throughout the entire spectrum of nanomedicine, as well as the formidable challenges and extraordinary potential for pioneering researchers.</p>

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Artificial intelligence for nanomedicine

  • 期刊论文

  • 发表日期：

    2025年9月11日

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  • Xiaolin Song,
  • Xingfa Gao,
  • Hui Wang,
  • Fangzhi Yu,
  • Mengmeng Qin,
  • Yiye Li,
  • Yixuan Liu,
  • Wei Feng,
  • Caiyu Zhou,
  • Nikita N. Chukavin,
  • Liming Wang,
  • Xuejing Cui,
  • Xinghua Shi,
  • Lele Li,
  • Huan Meng,
  • Guangjun Nie,
  • Hao Wang,
  • Jinming Hu,
  • Liang Yan,
  • Yu Chen,
  • Lizeng Gao,
  • Anton L. Popov,
  • Hui Wei,
  • Chunying Chen,
  • Yuliang Zhao

摘要

Nanomedicine has emerged as a dynamically evolving frontier in contemporary medical research. However, the development of nanomedicine is impeded by significant challenges due to its complex, multidisciplinary nature, necessitating the exploration of innovative solutions. Artificial intelligence (AI) has established itself as a pivotal and rapidly advancing domain within nanomedicine research. By leveraging its robust data processing and analytical capabilities, AI can efficiently analyze large datasets and accurately predict the properties and medical functions of nanomaterials. Over the past years, AI applications have proliferated across critical nanomedicine subdomains, including intelligent nanobiosensors for precision diagnostics, AI-optimized nanocarriers for targeted drug delivery, machine learning-guided adjuvant therapy systems, and predictive computational models for nanosafety evaluation. This review aims to provide a thorough analysis of AI’s influence throughout the entire spectrum of nanomedicine, as well as the formidable challenges and extraordinary potential for pioneering researchers.