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Updated: Jul 22, 2025

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Machine learning with multimodal data for COVID-19.

Weijie Chen1,2, Rui C Sá1,3, Yuntong Bai1,2

  • 1Medical Imaging and Data Resource Center (MIDRC), USA.

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|July 24, 2023
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Summary
This summary is machine-generated.

This study reviews multimodal machine learning for COVID-19, integrating diverse data like imaging and omics. It highlights lessons learned and future directions for pandemic preparedness using advanced AI models.

Keywords:
COVID-19Machine learningMultimodal data

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence
  • Public Health

Background:

  • The COVID-19 pandemic necessitated rapid advancements in understanding viral diseases.
  • Multimodal data integration offers a comprehensive approach to disease analysis.
  • Lessons from radiogenomics in cancer inform multimodal strategies.

Purpose of the Study:

  • To provide an overview of state-of-the-art multimodal machine learning for COVID-19.
  • To summarize diverse COVID-19 data modalities investigated in research.
  • To discuss model assessment and future development for pandemic preparedness.

Main Methods:

  • Literature review of multimodal machine learning applications in COVID-19 research.
  • Summarization of various data types: clinical, laboratory, imaging, pathology, physiology, and omics.
  • Discussion of publicly available multimodal COVID-19 datasets.

Main Results:

  • Identified key data modalities including symptoms, clinical data, lab tests, imaging, pathology, physiology, and omics.
  • Reviewed machine learning developments utilizing these multimodal datasets.
  • Highlighted the importance of model assessment for future studies.

Conclusions:

  • Multimodal machine learning shows significant promise for COVID-19 understanding and future pandemic response.
  • Integration of diverse data sources is crucial for robust AI model development.
  • Continued research and data sharing are essential for advancing AI in infectious disease management.