Artificial Intelligence Reporting Guidelines’ Adherence in Nephrology for Improved Research and Clinical Outcomes

Author:

Salybekov Amankeldi A.123ORCID,Wolfien Markus45ORCID,Hahn Waldemar45,Hidaka Sumi12ORCID,Kobayashi Shuzo12

Affiliation:

1. Kidney Disease and Transplant Center, Shonan Kamakura General Hospital, Kamakura 247-8533, Japan

2. Shonan Research Institute of Innovative Medicine, Shonan Kamakura General Hospital, Kamakura 247-8533, Japan

3. Qazaq Institute of Innovative Medicine, Astana 010000, Kazakhstan

4. Carl Gustav Carus Faculty of Medicine, Institute for Medical Informatics and Biometry, Technische Universität Dresden, 01317 Dresden, Germany

5. Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), 01317 Dresden, Germany

Abstract

The use of artificial intelligence (AI) in healthcare is transforming a number of medical fields, including nephrology. The integration of various AI techniques in nephrology facilitates the prediction of the early detection, diagnosis, prognosis, and treatment of kidney disease. Nevertheless, recent reports have demonstrated that the majority of published clinical AI studies lack uniform AI reporting standards, which poses significant challenges in interpreting, replicating, and translating the studies into routine clinical use. In response to these issues, worldwide initiatives have created guidelines for publishing AI-related studies that outline the minimal necessary information that researchers should include. By following standardized reporting frameworks, researchers and clinicians can ensure the reproducibility, reliability, and ethical use of AI models. This will ultimately lead to improved research outcomes, enhanced clinical decision-making, and better patient management. This review article highlights the importance of adhering to AI reporting guidelines in medical research, with a focus on nephrology and urology, and clinical practice for advancing the field and optimizing patient care.

Funder

This research was funded by the Science Committee of the Ministry of Science and Higher Educa-tion of the Republic of Kazakhstan grant

Publisher

MDPI AG

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