Unlocking new potential of clinical diagnosis with artificial intelligence: Finding new patterns of clinical and lab data

doi:10.4239/wjd.v15.i3.308

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Mar 15, 2024 (publication date) through Oct 31, 2024

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World Journal of Diabetes

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1948-9358

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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World J Diabetes. Mar 15, 2024; 15(3): 308-310
Published online Mar 15, 2024. doi: 10.4239/wjd.v15.i3.308

Unlocking new potential of clinical diagnosis with artificial intelligence: Finding new patterns of clinical and lab data

Pradeep Kumar Dabla

Pradeep Kumar Dabla, Department of Biochemistry, Govind Ballabh Pant Institute of Postgraduate Medical Education and Research, Delhi 110002, India

Author contributions: Dabla PK designed and written the manuscript and all data were generated in-house and no paper mill was used.

Conflict-of-interest statement: The authors declare that they have no conflict of interest.

Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/

Corresponding author: Pradeep Kumar Dabla, MD, Professor, Department of Biochemistry, Govind Ballabh Pant Institute of Postgraduate Medical Education and Research, J.L.N Marg, Delhi 110002, India. pradeep_dabla@yahoo.com

Received: September 28, 2023
Peer-review started: September 28, 2023
First decision: December 15, 2023
Revised: December 19, 2023
Accepted: February 6, 2024
Article in press: February 6, 2024
Published online: March 15, 2024
Processing time: 168 Days and 16.6 Hours

Core Tip

Core Tip: The integration of artificial intelligence (AI) and machine learning in laboratory medicine presents a promising opportunity to improve the patient care, particularly in the context of multi-factorial cardiovascular diseases. However, it is essential to approach this transformation carefully, side by side addressing ethical considerations, biases, while ensuring its responsible implementation through the collaboration between the technology experts and the healthcare professionals. Education and training are key to unlocking the full potential of AI while safeguarding patient privacy and data.