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

Autor: Umesh C; Institute of Computer Science, Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany. chaithra.umesh@uni-rostock.de., Mahendra M; Institute of Computer Science, Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany. manjunath.mahendra@uni-rostock.de., Bej S; School of Data Science, Indian Institute of Science Education and Research (IISER), Thiruvananthapuram, India., Wolkenhauer O; Institute of Computer Science, Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany.; Leibniz-Institute for Food Systems Biology, Technical University of Munich, Freising, Germany., Wolfien M; Faculty of Medicine Carl Gustav Carus, Institute for Medical Informatics and Biometry, TUD Dresden University of Technology, Dresden, Germany.; Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), Dresden, Germany.
Jazyk: angličtina
Zdroj: Pflugers Archiv : European journal of physiology [Pflugers Arch] 2024 Oct 17. Date of Electronic Publication: 2024 Oct 17.
DOI: 10.1007/s00424-024-03024-w
Abstrakt: Recent advancements in generative approaches in AI have opened up the prospect of synthetic tabular clinical data generation. From filling in missing values in real-world data, these approaches have now advanced to creating complex multi-tables. This review explores the development of techniques capable of synthesizing patient data and modeling multiple tables. We highlight the challenges and opportunities of these methods for analyzing patient data in physiology. Additionally, it discusses the challenges and potential of these approaches in improving clinical research, personalized medicine, and healthcare policy. The integration of these generative models into physiological settings may represent both a theoretical advancement and a practical tool that has the potential to improve mechanistic understanding and patient care. By providing a reliable source of synthetic data, these models can also help mitigate privacy concerns and facilitate large-scale data sharing.
(© 2024. The Author(s).)
Databáze: MEDLINE
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