Publication Name	World Journal of Nephrology
Manuscript ID	121458
Country	India

Category	Transplantation
Manuscript Type	Editorial
Article Title	Quantity, quality & complexity: Lessons on clinical machine learning from transplantation
Manuscript Source	Invited Manuscript
All Author List	Susan Paulin, Ashwin Rammohan, Mohit Vanvari and Neha Harchandani
Funding Agency and Grant Number
Corresponding Author	Susan Paulin, Consultant, Department of Liver Transplant Anaesthesia and Critical Care, Dr Rela Institute and Medical Centre, No. 7 CLC works road, Nagappa Nagar, Chennai 600044, Tamil Nādu, India. susanvercetti@gmail.com
Key Words	Machine learning; Transplantation; Artificial intelligence; Learning curves; Clinical machine learning; Predictive models; Machine learning models
Core Tip	Machine learning (ML) methods are increasingly applied in transplantation research to improve the prediction of multiple clinical outcomes. However, recent comparative studies, including the work by Salgado et al, demonstrate that ML algorithms often provide only modest improvements over traditional statistical models when applied to structured clinical datasets. These highlight that predictive performance depends not only on the algorithm but also on the dataset quantity, data quality, and the appropriateness of model complexity. Future research should therefore focus on improving data infrastructure, integrating high-dimensional data sources, and evaluating clinical utility to determine when ML methods provide meaningful advantages in clinical Transplantation.
Citation	Paulin S, Rammohan A, Vanvari M, Harchandani N. Quantity, quality & complexity: Lessons on clinical machine learning from transplantation. World J Nephrol 2026; In press

Received	2026-03-27 03:31
Peer-Review Started	2026-03-27 03:31
First Decision by Editorial Office Director	2026-04-04 02:49
Return for Revision	2026-04-04 02:49
Revised	2026-04-14 06:46
Publication Fee Transferred
Second Decision by Editor	2026-04-24 02:40
Second Decision by Editor-in-Chief
Final Decision by Editorial Office Director	2026-04-24 12:53
Articles in Press	2026-04-24 12:53
Edit the Manuscript by Language Editor
Typeset the Manuscript

ISSN	2220-6124 (online)
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