Comparative evaluation of artificial intelligence systems' accuracy in providing medical drug dosages: A methodological study

doi:10.5662/wjm.v14.i4.92802

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World Journal of Methodology

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World J Methodol. Dec 20, 2024; 14(4): 92802
Published online Dec 20, 2024. doi: 10.5662/wjm.v14.i4.92802

Comparative evaluation of artificial intelligence systems' accuracy in providing medical drug dosages: A methodological study

Swaminathan Ramasubramanian, Sangeetha Balaji, Tejashri Kannan, Naveen Jeyaraman, Shilpa Sharma, Filippo Migliorini, Suhasini Balasubramaniam, Madhan Jeyaraman

Swaminathan Ramasubramanian, Sangeetha Balaji, Tejashri Kannan, Department of Orthopaedics, Government Medical College, Omandurar Government Estate, Chennai 600002, Tamil Nadu, India

Naveen Jeyaraman, Madhan Jeyaraman, Department of Orthopaedics, ACS Medical College and Hospital, Dr MGR Educational and Research Institute, Chennai 600077, Tamil Nadu, India

Shilpa Sharma, Department of Paediatric Surgery, All India Institute of Medical Sciences, New Delhi 110029, India

Filippo Migliorini, Department of Life Sciences, Health, Link Campus University, Rome 00165, Italy

Filippo Migliorini, Department of Orthopaedic and Trauma Surgery, Academic Hospital of Bolzano (SABES-ASDAA), Teaching Hospital of the Paracelsus Medical University, Bolzano 39100, Italy

Suhasini Balasubramaniam, Department of Radio-Diagnosis, Government Stanley Medical College and Hospital, Chennai 600001, Tamil Nadu, India

Author contributions: Ramasubramanian S, Balaji S conceived and designed the study and drafted the manuscript (original and revision); Jeyaraman M and Migliorini F supervised the study and drafted the manuscript (revision); Jeyaraman M, Migliorini F, and Sharma S drafted the manuscript (revision); Ramasubramanian S, Balaji S, Kannan T contributed to drafting (original); Ramasubramanian S, Migliorini F, Balasubramaniam S, Jeyaraman N conducted visualizations and data analysis; All authors have agreed to the final version to be published and agree to be accountable for all aspects of the work.

Institutional review board statement: This study did not require approval from the Institutional Review Board.

Informed consent statement: This study did not require informed consent forms due to the data collection process.

Conflict-of-interest statement: The authors declare no conflicts of interest related to this manuscript.

Data sharing statement: No additional data are available outside of the Supplemental Materials that are published with this article.

STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement- checklist of items.

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 NonCommercial (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: Madhan Jeyaraman, MS, PhD, Assistant Professor, Research Associate, Department of Orthopaedics, ACS Medical College and Hospital, Dr MGR Educational and Research Institute, Velappanchavadi, Chennai 600077, Tamil Nadu, India.madhanjeyaraman@gmail.com

Received: February 6, 2024
Revised: May 29, 2024
Accepted: June 25, 2024
Published online: December 20, 2024
Processing time: 171 Days and 4.1 Hours

Core Tip

Core Tip: This study reveals ChatGPT 4's superior accuracy in providing medical drug dosage information, highlighting the potential of artificial intelligence (AI) to aid healthcare professionals in minimizing medication errors. The analysis, based on Harrison's Principles of Internal Medicine, underscores the need for ongoing AI development to ensure reliability in critical medical situations. Variations in disease-specific and organ system accuracies suggest areas for improvement and continuous refinement of AI systems in medicine.