肝癌是全球癌症相关死亡的第四大原因。在过去的十年中，人工智能（AI）领域的突破激发了癌症领域的算法开发。最近的一系列研究评估了机器学习（ML）和深度学习（DL）算法在通过诊断图像分析、生物标志物发现和预测个性化临床结果中进行预筛选、诊断和管理肝癌患者方面的应用。虽然这些早期的AI工具充满希望，但仍然有必要解释AI的“黑匣子”，并努力推动部署，以实现最终的临床可转化性。某些新兴领域，如RNA纳米医学，对于肝癌定向治疗也可能受益于应用AI，特别是在纳米制剂研究和开发方面，因为它们仍然在很大程度上依赖于漫长的试错实验。本文提出了肝癌中AI的当前格局，以及AI在肝癌诊断和管理中面临的挑战。最后，我们讨论了AI在肝癌应用中的未来前景，以及如何使用纳米医学中的跨学科方法加速个性化肝癌医学从实验室转移至临床。版权所有©2023作者。由Elsevier Ltd.出版。保留所有权利。

Liver cancers are the fourth leading cause of cancer-related mortality worldwide. In the past decade, breakthroughs in the field of artificial intelligence (AI) have inspired development of algorithms in the cancer setting. A growing body of recent studies have evaluated machine learning (ML) and deep learning (DL) algorithms for pre-screening, diagnosis and management of liver cancer patients through diagnostic image analysis, biomarker discovery and predicting personalized clinical outcomes. Despite the promise of these early AI tools, there is a significant need to explain the 'black box' of AI and work towards deployment to enable ultimate clinical translatability. Certain emerging fields such as RNA nanomedicine for targeted liver cancer therapy may also benefit from application of AI, specifically in nano-formulation research and development given that they are still largely reliant on lengthy trial-and-error experiments. In this paper, we put forward the current landscape of AI in liver cancers along with the challenges of AI in liver cancer diagnosis and management. Finally, we have discussed the future perspectives of AI application in liver cancer and how a multidisciplinary approach using AI in nanomedicine could accelerate the transition of personalized liver cancer medicine from bench side to the clinic.Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.