The inherent limits of traditional diagnoses and therapies have driven the development and application of emerging nanotechnologies for more effective and safer management of diseases, herein referred to as ‘nanotheranostics’. Although many important technological successes have been achieved in this field, widespread adoption of nanotheranostics as a new paradigm is hindered by specific obstacles, including time-consuming synthesis of nanoparticles, incomplete understanding of nano–bio interactions, and challenges regarding chemistry, manufacturing and the controls required for clinical translation and commercialization. As a key branch of artificial intelligence, machine learning (ML) provides a set of tools capable of performing time-consuming and result-perception tasks, thus offering unique opportunities for nanotheranostics. This Review summarizes the progress and challenges in this emerging field of ML-aided nanotheranostics, and discusses the opportunities in developing next-generation nanotheranostics with reliable datasets and advanced ML models to offer better clinical benefits to patients.

传统诊断和疗法的固有局限性推动了新兴纳米技术的发展和应用，以更有效、更安全地管理疾病，这里称为“nanotheranostics”。尽管该领域已经取得了许多重要的技术成功，但 nanotheranostics 作为一种新范式的广泛采用受到特定障碍的阻碍，包括耗时的纳米颗粒合成、对纳米-生物相互作用的不完整理解，以及化学、制造和临床转化和商业化所需的控制方面的挑战。作为人工智能的一个关键分支，机器学习 （ML） 提供了一组能够执行耗时和结果感知任务的工具，从而为另一个诊断提供了独特的机会。本综述总结了 ML 辅助 nanotheranostics 这一新兴领域的进展和挑战，并讨论了使用可靠的数据集和先进的 ML 模型开发下一代 nanotheranostics 的机会，以更好地为患者提供更好的临床益处。