Multimodal biomedical AI.
Julian N AcostaGuido J FalconePranav RajpurkarEric J TopolPublished in: Nature medicine (2022)
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.
Keyphrases
- artificial intelligence
- human health
- big data
- electronic health record
- healthcare
- risk assessment
- machine learning
- pain management
- public health
- deep learning
- clinical trial
- climate change
- clinical decision support
- health information
- coronavirus disease
- palliative care
- adverse drug
- air pollution
- mental health
- sars cov
- high resolution
- particulate matter
- quality improvement
- single cell
- heart rate
- genome wide
- liquid chromatography
- mass spectrometry
- gene expression
- blood pressure
- dna methylation
- study protocol