下一个时代:世界领先的人工智能与国家医学健康事业蓬勃发展（英文）
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摘要
<正>本期专辑聚焦人工智能在医学领域的最新进展,包括当前人工智能在医学应用中的重要算法和技术的综述、在一些深耕医学领域及疾病的计算机辅助诊断研究现状、人工智能的多种应用范例,以及人工智能与医学合作继而改变常规医疗实践活动的潜在途径。我们希望本专辑能使人工智能专业人士和医学实践者们更全面地了解医学人工智能的现状,启迪更多的思路,助力医学人工智能领域的深入发展。
引文
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4.He J,Baxter SL,Xu J,et al.The practical implementation of artificial intelligence technologies in medicine.Nature Med 2019;25(1):30-6.doi:10.1038/s41591-018-0307-0.
5.Greenspan H,Ginneken BV,Summers RM.Deep learning in medical imaging:overview and future promise of an exciting new technique.IEEE Trans Med Imaging 2016;35(5):1153-9.doi:10.1109/TMI-2016.2553401.
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7.Ravizza S,Huschto T,Adamov A,et al.Predicting the early risk of chronic kidney disease in patients with diabetes using real-world data.Nature Med 2019;25 (1):57-9.doi:10.1038/s41591-018-0239-8.
2.Mnih V,Kavukcuoglu K,Silver D,et al.Playing atariwith deep reinforcement learning.https://arxiv.org/abs/1312.5602.Published December 19,2013;accessed June 9,2019.
3.Esteva A,Robicquet A,Ramsundar B,et al.A guide to deep learning in healthcare.Nature Med 2019;25 (1):24-9.doi:10.1038/s41591-018-0316-z.
4.He J,Baxter SL,Xu J,et al.The practical implementation of artificial intelligence technologies in medicine.Nature Med 2019;25(1):30-6.doi:10.1038/s41591-018-0307-0.
5.Greenspan H,Ginneken BV,Summers RM.Deep learning in medical imaging:overview and future promise of an exciting new technique.IEEE Trans Med Imaging 2016;35(5):1153-9.doi:10.1109/TMI-2016.2553401.
6.Ravi D,Wong C,Deligianni F,et al.Deep learning for health informatics.IEEE J Biomed Health Informatics2016;21(1):4-21.doi:10.1109/JBHI.2016.2636665.
7.Ravizza S,Huschto T,Adamov A,et al.Predicting the early risk of chronic kidney disease in patients with diabetes using real-world data.Nature Med 2019;25 (1):57-9.doi:10.1038/s41591-018-0239-8.