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Generative AI Models in Time-Varying Biomedical Data: Scoping Review.

Rosemary He1,2, Varuni Sarwal1,2, Xinru Qiu3

  • 1Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, United States.

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Summary
This summary is machine-generated.

Generative artificial intelligence (AI) offers powerful tools for healthcare trajectory modeling, overcoming limitations of traditional methods. This review bridges the gap between AI advancements and clinical practice for better disease modeling.

Keywords:
algorithmsartificial intelligencedisease trajectoryelectronic health recordselectronic medical recordsforecastinggenerative artificial intelligencemachine learningsystematic reviewstime series

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Area of Science:

  • * Computational health sciences
  • * Health informatics
  • * Artificial intelligence in medicine

Background:

  • * Traditional statistical and machine learning methods struggle with complex, multimodal health data and long-term dependencies.
  • * Generative artificial intelligence (AI) excels at representing complex data distributions and patterns.
  • * AI applications in finance and environmental sciences show promise for healthcare disease modeling.

Purpose of the Study:

  • * Introduce basic concepts of generative AI for healthcare practitioners.
  • * Discuss current generative AI algorithms and their applications in healthcare.
  • * Address the complexity barrier limiting AI adoption in clinical practice.

Main Methods:

  • * Systematic literature review of peer-reviewed papers on generative AI for time-series health data.
  • * Included single- and multimodal generative AI models across structured/unstructured data, waveforms, imaging, and multi-omics.
  • * Analyzed current methods, applications, limitations, and future directions.

Main Results:

  • * Reviewed 155 articles on generative AI applications in time-series healthcare data, adhering to PRISMA-ScR guidelines.
  • * Identified and categorized generative AI models across various healthcare data modalities.
  • * Developed a systematic framework to aid clinicians in selecting appropriate AI methods.

Conclusions:

  • * Critiqued existing generative AI applications for time-series health data to bridge the computational-clinical gap.
  • * Highlighted shortcomings in current generative AI approaches for healthcare modeling.
  • * Identified promising recent advances in generative AI for future healthcare applications.