Challenges and applications in generative AI for clinical tabular data in physiology

Area of Science:

• Artificial Intelligence
• Bioinformatics
• Clinical Data Management

Background:

• Generative AI is evolving for synthetic tabular clinical data generation.
• Techniques have advanced from data imputation to complex multi-table synthesis.

Purpose of the Study:

• To review generative AI techniques for patient data synthesis and multi-table modeling.
• To explore challenges and opportunities in physiological data analysis.
• To discuss potential impacts on clinical research, personalized medicine, and healthcare policy.

Main Methods:

• Review of recent advancements in generative AI for tabular data.
• Analysis of techniques for multi-table data synthesis.
• Exploration of applications in physiology and healthcare.

Main Results:

• Generative AI shows promise for creating complex synthetic clinical datasets.
• These models can address challenges in data privacy and sharing.
• Potential for improved mechanistic understanding and patient care.

Conclusions:

• Generative AI offers a theoretical and practical advancement for physiological settings.
• Synthetic data generation can enhance clinical research and personalized medicine.
• Integration of these models can improve patient care and data accessibility.