Nature Medicine· 2022Q1· Review
Multimodal biomedical AI
- 1,165citations
- Q1SCImago
- 2022year
Short summary
Multimodal AI models, integrating diverse data like EHRs, imaging, and genomics, are poised to revolutionize healthcare by capturing health complexity.
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Key points
- Multimodal AI integrates diverse biomedical data (EHRs, imaging, genomics, biosensors) to model health complexity.
- Key applications include personalized medicine, digital clinical trials, remote monitoring, and pandemic surveillance.
- Significant technical and analytical challenges in data integration, modeling, and privacy must be addressed.
AI-generated from the title and abstract; the full text is not read.
Abstract
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. 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 authors' abstract, as published at the source. Nature Medicine, 2022 · DOI ↗
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Field: Health Informatics
Health InformaticsMedicine