Multimodal biomedical AI

被引：525

作者：

Acosta, Julian N. ^{[1
]}

Falcone, Guido J. ^{[1
]}

Rajpurkar, Pranav ^{[2
]}

Topol, Eric J. ^{[3
]}

机构：

[1] Yale Sch Med, Dept Neurol, New Haven, CT USA

[2] Harvard Med Sch, Dept Biomed Informat, Boston, MA 02115 USA

[3] Scripps Res, Scripps Res Translat Inst, La Jolla, CA 92037 USA

来源：

NATURE MEDICINE | 2022年 / 28卷 / 09期

关键词：

UK BIOBANK; HEALTH-CARE; BIG DATA; CLINICAL-TRIALS; PLATFORM; FUSION; TECHNOLOGY; PREDICTION; MEDICINE; COVID-19;

D O I：

10.1038/s41591-022-01981-2

中图分类号：

Q5 [生物化学]; Q7 [分子生物学];

学科分类号：

071010 ; 081704 ;

摘要：

Multimodal artificial intelligence models could unlock many exciting applications in health and medicine; this Review outlines the most promising uses and the technical pitfalls to avoid. The increasing availability of biomedical data from large biobanks, electronic health records, medical imaging, wearable and ambient biosensors, and the lower cost of genome and microbiome sequencing have set the stage for the development of multimodal artificial intelligence solutions that capture the complexity of human health and disease. In this Review, we outline the key applications enabled, along with the technical and analytical challenges. We explore opportunities in personalized medicine, digital clinical trials, remote monitoring and care, pandemic surveillance, digital twin technology and virtual health assistants. Further, we survey the data, modeling and privacy challenges that must be overcome to realize the full potential of multimodal artificial intelligence in health.

引用

页码：1773 / 1784

页数：12

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