Artificial intelligence (AI), the wide spectrum of technologies aiming to give machines or computers the ability to perform human-like cognitive functions, began in the 1940s with the first abstract models of intelligent machines. Soon after, in the 1950s and 1960s, machine learning algorithms such as neural networks and decision trees ignited significant enthusiasm. More recent advancements include the refinement of learning algorithms, the development of convolutional neural networks to efficiently analyze images, and methods to synthesize new images. This renewed enthusiasm was also due to the increase in computational power with graphical processing units and the availability of large digital databases to be mined by neural networks. AI soon began to be applied in medicine, first through expert systems designed to support the clinician’s decision and later with neural networks for the detection, classification, or segmentation of malignant lesions in medical images. A recent prospective clinical trial demonstrated the non-inferiority of AI alone compared with a double reading by two radiologists on screening mammography. Natural language processing, recurrent neural networks, transformers, and generative models have both improved the capabilities of making an automated reading of medical images and moved AI to new domains, including the text analysis of electronic health records, image self-labeling, and self-reporting. The availability of open-source and free libraries, as well as powerful computing resources, has greatly facilitated the adoption of deep learning by researchers and clinicians. Key concerns surrounding AI in healthcare include the need for clinical trials to demonstrate efficacy, the perception of AI tools as ‘black boxes’ that require greater interpretability and explainability, and ethical issues related to ensuring fairness and trustworthiness in AI systems. Thanks to its versatility and impressive results, AI is one of the most promising resources for frontier research and applications in medicine, in particular for oncological applications.
人工智能（AI）作为一系列旨在使机器或计算机具备类人认知功能的技术，始于20世纪40年代首批智能机器的抽象模型。随后在20世纪50至60年代，神经网络和决策树等机器学习算法引发了广泛的研究热情。近年来的进展包括学习算法的优化、用于高效分析图像的卷积神经网络的开发，以及图像合成新方法的出现。这一轮热潮的兴起还得益于图形处理器带来的算力提升，以及可供神经网络挖掘的大型数字数据库的普及。 人工智能很快被应用于医学领域：最初是通过专家系统辅助临床决策，随后利用神经网络实现医学图像中恶性病变的检测、分类与分割。近期一项前瞻性临床试验表明，在乳腺筛查钼靶影像分析中，单独使用人工智能系统的效能不亚于两位放射科医师的双重判读。自然语言处理、循环神经网络、Transformer架构和生成模型的发展，不仅提升了医学影像自动解读的能力，更将人工智能拓展至电子健康记录文本分析、图像自标注与自动报告等新领域。开源免费算法库与强大计算资源的普及，极大促进了研究人员和临床工作者对深度学习技术的应用。 当前医疗人工智能领域的关键问题包括：需要通过临床试验验证其有效性；人工智能工具常被视为需要更高可解释性的“黑箱”；以及确保系统公平性与可信度的伦理议题。凭借其多功能性与卓越成效，人工智能已成为医学前沿研究和应用中最具前景的技术资源，尤其在肿瘤学领域展现出巨大潜力。
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