Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany; email:
Digital Medicine Group, Luxembourg Centre for Systems Biomedicine, Université du Luxembourg, Belvaux, Luxembourg.
Annu Rev Biomed Eng. 2023 Jun 8;25:131-156. doi: 10.1146/annurev-bioeng-110220-030247. Epub 2023 Feb 28.
Artificial intelligence (AI) and machine learning (ML) methods are currently widely employed in medicine and healthcare. A PubMed search returns more than 100,000 articles on these topics published between 2018 and 2022 alone. Notwithstanding several recent reviews in various subfields of AI and ML in medicine, we have yet to see a comprehensive review around the methods' use in longitudinal analysis and prediction of an individual patient's health status within a personalized disease pathway. This review seeks to fill that gap. After an overview of the AI and ML methods employed in this field and of specific medical applications of models of this type, the review discusses the strengths and limitations of current studies and looks ahead to future strands of research in this field. We aim to enable interested readers to gain a detailed impression of the research currently available and accordingly plan future work around predictive models for deterioration in health status.
人工智能 (AI) 和机器学习 (ML) 方法目前在医学和医疗保健领域得到广泛应用。仅在 2018 年至 2022 年期间,PubMed 上就有超过 100,000 篇关于这些主题的文章。尽管最近在医学领域的各个子领域中对 AI 和 ML 进行了几次综述,但我们还没有看到关于这些方法在个体患者健康状况的纵向分析和预测中在个性化疾病途径中的综合应用的综述。本综述旨在填补这一空白。在概述了该领域中使用的 AI 和 ML 方法以及此类模型在特定医学应用之后,本综述讨论了当前研究的优缺点,并展望了该领域未来的研究方向。我们的目标是使有兴趣的读者能够详细了解当前可用的研究,并相应地围绕健康状况恶化的预测模型来规划未来的工作。