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Page 217 Dababneh et al. Art Int Surg 2024;4:214-32 https://dx.doi.org/10.20517/ais.2024.50

Figure 1. Flowchart detailing the systematic review process showing the number of articles identified and screened and those included
in the final study.

In contrast to other studies, Leypold et al. showed that ChatGPT-4 was successful in understanding upper
[21]
limb surgical scenarios and in identifying relevant treatment options . These results do not entirely align
with the findings of Seth et al., who showed that although the information provided by ChatGPT was
[20]
accurate, it was mostly superficial and limited to well-studied information . This suggests that ChatGPT
might not be able to interpret more complex cases.

Similarly, Seth et al. conducted two more studies to evaluate ChatGPT’s knowledge and reliability for
common hand surgery conditions, focusing on CTS and scaphoid fracture management . The same
[22]
[23]
methodology was used to assess both conditions. The authors concluded that the algorithm showed a good
understanding of the questions and provided logical answers that were easily understandable. Nevertheless,
some responses were deemed superficial, lacking detailed explanations, and occasionally, the AI model
included references to nonexistent publications.

Ajmera et al. explored a different aspect of ChatGPT’s performance by assessing its performance in
generating anatomical images of six different joints, including the wrist . This study concluded that
[24]
ChatGPT’s performance was below average, with significant anatomical errors such as incorrect
articulations, missing or fake bones.

AI-assisted fracture detection
The main application of AI in hand and wrist surgery remains the detection of fractures. Identified studies
predominantly addressed distal radius fractures, ulna styloid fractures, distal ulna fractures, and scaphoid
fractures.

In 2017, Olczak et al. were one of the first to demonstrate that DL networks performed similarly to senior
orthopedic surgeons in identifying fractures. Their study used a dataset of 256,458 wrist, hand, and ankle
radiographs to train and test five DL networks. The best-performing network achieved an 83% accuracy in
identifying fractures, comparable to senior orthopedic surgeons . Similarly, Lee et al. found that AI
[25]
assistance improved inexperienced radiologists’ accuracy in scaphoid fracture detection . Lindsey et al.
[26]