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Multimodal biomedical AI

📅 September 1, 2022 👤 Julián Acosta, Guido J. Falcone, Pranav Rajpurkar et al. 📖 Nature Medicine 📊 1,104 citations

🤖 Plain-English Summary

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. Further, we survey the data, modeling and privacy challenges that must be overcome to realize the full potential of multimodal artificial intelligence in he...

🔑 Key Findings

  • 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.

💡 Why This Matters

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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📋 Article Details

Category 🤖 Artificial Intelligence
Published Sep 01, 2022
Journal Nature Medicine
Authors Julián Acosta, Guido J. Falcone, Pranav Rajpurkar, Eric J. Topol
DOI 10.1038/s41591-022-01981-2
Citations 1,104
Source OpenAlex

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