%0 Journal Article %1 salybekov_artificial_2024 %A Salybekov, Amankeldi A. %A Wolfien, Markus %A Hahn, Waldemar %A Hidaka, Sumi %A Kobayashi, Shuzo %D 2024 %J Biomedicines %K topic_lifescience %N 3 %R 10.3390/biomedicines12030606 %T Artificial Intelligence Reporting Guidelines’ Adherence in Nephrology for Improved Research and Clinical Outcomes %U https://www.mdpi.com/2227-9059/12/3/606 %V 12 %X 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.

@article{salybekov_artificial_2024, 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.}, added-at = {2024-09-10T10:41:24.000+0200}, author = {Salybekov, Amankeldi A. and Wolfien, Markus and Hahn, Waldemar and Hidaka, Sumi and Kobayashi, Shuzo}, biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/24fb69b1a378b0bf2c38c2c04e4369bd1/scadsfct}, doi = {10.3390/biomedicines12030606}, interhash = {519d8a5e4a58ecb28f9c71300df5d350}, intrahash = {4fb69b1a378b0bf2c38c2c04e4369bd1}, issn = {2227-9059}, journal = {Biomedicines}, keywords = {topic_lifescience}, number = 3, timestamp = {2024-11-22T15:48:58.000+0100}, title = {Artificial {Intelligence} {Reporting} {Guidelines}’ {Adherence} in {Nephrology} for {Improved} {Research} and {Clinical} {Outcomes}}, url = {https://www.mdpi.com/2227-9059/12/3/606}, volume = 12, year = 2024 }