Evidence map›Paper›PMID 39417878›Full record

ReviewPflugers Archiv : European journal of physiology2025

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

Chaithra Umesh, Manjunath Mahendra, Saptarshi Bej, Olaf Wolkenhauer, Markus Wolfien

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Pflugers Archiv : European journal of physiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Chaithra Umesh *Institute of Computer Science, Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany. chaithra.umesh@uni-rostock.de.ORCID 0000-0002-4400-5386
Manjunath Mahendra *Institute of Computer Science, Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany. manjunath.mahendra@uni-rostock.de.ORCID 0009-0002-0002-6051
Saptarshi BejSchool of Data Science, Indian Institute of Science Education and Research (IISER), Thiruvananthapuram, India.ORCID 0000-0003-1835-6139
Olaf WolkenhauerInstitute of Computer Science, Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany.ORCID 0000-0001-6105-2937
Markus WolfienFaculty of Medicine Carl Gustav Carus, Institute for Medical Informatics and Biometry, TUD Dresden University of Technology, Dresden, Germany.ORCID 0000-0002-1887-4772

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Artificial IntelligencePhysiologyHumansPrecision MedicineData privacyGenerative AIPhysiological applicationsTabular data

Identifiers

PMID39417878
PMCPMC11958401

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.