Machine learning-based COVID-19 prognostic models lag behind in reporting quality: findings from a TRIPOD/TRIPOD + AI systematic review

Lower adherence among machine learning studies reflects the relatively recent publication of the TRIPOD + AI guidelines (April 2024), which postdate many of the included studies. Both conventional and machine learning-based prediction models showed insufficient reporting, with major gaps in model description and performance reporting. Greater compliance with reporting guidelines is critical to improving the clarity, reproducibility, and clinical value of prediction model research.

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Publication Details

Journal
Diagnostic and Prognostic Research
Published
2026-02-02
DOI
https://doi.org/10.1186/s41512-026-00218-x
Primary Topic
COVID-19 diagnosis using AI
Type
article
Field-Weighted Citation Impact
0.00
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article

Machine learning-based COVID-19 prognostic models lag behind in reporting quality: findings from a TRIPOD/TRIPOD + AI systematic review

Persefoni Talimtzi, Ioannis Partheniadis, Anna-Bettina Haidich, Adriani Nikolakopoulou
Diagnostic and Prognostic Research
COVID-19 diagnosis using AI
article

Machine learning-based COVID-19 prognostic models lag behind in reporting quality: findings from a TRIPOD/TRIPOD + AI systematic review

Persefoni Talimtzi, Ioannis Partheniadis, Anna-Bettina Haidich, Adriani Nikolakopoulou
article en

Abstract

Lower adherence among machine learning studies reflects the relatively recent publication of the TRIPOD + AI guidelines (April 2024), which postdate many of the included studies. Both conventional and machine learning-based prediction models showed insufficient reporting, with major gaps in model description and performance reporting. Greater compliance with reporting guidelines is critical to improving the clarity, reproducibility, and clinical value of prediction model research.

Diagnostic and Prognostic ResearchVol. 10(1)
Aristotle University of Thessaloniki (GR), University Medical Center Freiburg (DE)
No poverty
Openalex Percentile: Top 70%
COVID-19 diagnosis using AI
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Machine learning-based COVID-19 prognostic models lag behind in reporting quality: findings from a TRIPOD/TRIPOD + AI systematic review — Persefoni Talimtzi, Ioannis Partheniadis, et al. · Diagnostic and Prognostic Research (2026) | TGRS Research Map | TGRS