Published on in Vol 10 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/92549, first published .
Correction: Comparing ChatGPT and DeepSeek for Assessment of Multiple-Choice Questions in Orthopedic Medical Education: Cross-Sectional Study

Correction: Comparing ChatGPT and DeepSeek for Assessment of Multiple-Choice Questions in Orthopedic Medical Education: Cross-Sectional Study

Correction: Comparing ChatGPT and DeepSeek for Assessment of Multiple-Choice Questions in Orthopedic Medical Education: Cross-Sectional Study

1Department of Orthopedics, Faculty of Medicine, Prince of Songkla University, 15 Karnjanavanich Road, Hat Yai, Thailand

2Department of Orthopaedics, Faculty of Medicine, Vajira Hospital, Navamindradhiraj University, Bangkok, Thailand

Corresponding Author:

Boonsin Tangtrakulwanich, MD, PhD



In “Comparing ChatGPT and DeepSeek for Assessment of Multiple-Choice Questions in Orthopedic Medical Education: Cross-Sectional Study” [1], a typographical error has been found.

In Table 2, the accuracy of the “Reason” function, reported as n (%) in the pelvic and spine injury category, was “16 (68.8%).” On re-examination, the correct accuracy for the pelvic and spine injury category using the “Reason” function was 16 out of 19, corresponding to 84.2%. Thus, the value reported in Table 2 has been revised from “16 (68.8)” to “16 (84.2).” The revised version of Table 2 is shown below (with change shown in italics).

Table 1. Comparison of accuracy between “Reason” (ChatGPT) and “DeepThink” (DeepSeek) functions across orthopedic multiple choice question (MCQ) categories (n=209).
MCQs in each categoryReason responses, n (%)DeepThink responses, n (%)P value
Upper limb injury (n=28)25 (89.3)25 (89.3)>.99
Upper limb disease (n=21)21 (100.0)20 (95.2).32
Pelvic and spine injury (n=19)16 (84.2)14 (73.7).16
Lower limb injury (n=16)11 (68.8)9 (56.3).41
Lower limb disease (n=12)11 (91.7)10 (83.3).31
Back and neck problems (n=26)22 (84.6)22 (84.6)>.99
Sprain and strain (n=8)7 (87.5)7 (87.5)>.99
Pediatric and anomaly (n=20)16 (80.0)13 (65.0).18
Tumor and infection (n=14)13 (92.9)11 (78.6).16
Complication in orthopedics (n=11)7 (63.6)10 (90.9).08
Rehabilitation and physical therapy (n=34)28 (82.4)27 (79.4).56
Total (n=209)177 (84.7)168 (80.4).12

The correction will appear in the online version of the paper on the JMIR Publications website, together with the publication of this correction notice. Because this was made after submission to PubMed, PubMed Central, and other full-text repositories, the corrected article has also been resubmitted to those repositories.

  1. Anusitviwat C, Suwannaphisit S, Bvonpanttarananon J, Tangtrakulwanich B. Comparing ChatGPT and DeepSeek for assessment of multiple-choice questions in orthopedic medical education: cross-sectional study. JMIR Form Res. Dec 19, 2025;9:e75607. [CrossRef] [Medline]

This is a non–peer-reviewed article. submitted 31.Jan.2026; accepted 10.Feb.2026; published 26.Feb.2026.

Copyright

© Chirathit Anusitviwat, Sitthiphong Suwannaphisit, Jongdee Bvonpanttarananon, Boonsin Tangtrakulwanich. Originally published in JMIR Formative Research (https://formative.jmir.org), 26.Feb.2026.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The complete bibliographic information, a link to the original publication on https://formative.jmir.org, as well as this copyright and license information must be included.