Page 21 - Read Online

Dababneh et al. Art Int Surg 2024;4:214-32 https://dx.doi.org/10.20517/ais.2024.50 Page 226

understandable answers for patients. Moreover, the algorithm did not provide any patient-specific advice,
but instead directed individuals to consult healthcare professionals. Nevertheless, some AI
recommendations lacked evidence, and the “black box” concern, which refers to the lack of transparency
regarding the source of the information generated by AI, remains a challenge.

In 2023, Croen et al. compared ChatGPT 3.5’s answers to those of Google Web Search regarding frequently
[79]
asked questions about CTD . Although ChatGPT’s answers were more detailed and were based on
multiple academic sources, they were significantly more difficult to understand. Pohl et al. highlighted
similar results when comparing ChatGPT’s answers to MedMD and Mayo Clinic regarding various types of
hand surgeries including CTD . Similarly, when asked to provide answers at a fourth-grade reading level,
[80]
ChatGPT generated answers at an average of a tenth-grade reading level. Browne et al. also found that
ChatGPT-4 reduced the reading level of information related to hand procedures by a mean of two grade
levels, reaching a sixth-grade reading level .
[81]

Other diagnostic applications
Avascular necrosis detection
Avascular necrosis (AVN) of the lunate is a rare and potentially asymptomatic condition, but its delayed
diagnosis and treatment can lead to decreased hand function. To address this, Wernér et al. conducted a
study investigating a DL model’s potential to diagnose AVN of the lunate using radiographs . A DL model
[82]
was developed by the authors within the AI environment Aiforia Create (version 5.5) and was trained to
detect AVN of the lunate. The model achieved an AUC of 0.94 and accurately detected AVN in 28 out of 30
cases. The model was outperformed by a hand surgeon and a radiologist but demonstrated significant
screening potential.

TFCC injuries prediction
Visualizing the TFCC remains a significant challenge in hand surgery. In 2022, Lin et al. explored the
[83]
potential of DL for predicting TFCC injuries based on magnetic resonance imaging (MRI) scans . Two
CNNs, MRNet and ResNet50, were trained and tasked with detecting the presence of TFCC injuries.
ResNet50 significantly outperformed MRNet and both radiologists.

Enchondroma diagnosis
Enchondromas are common benign bone masses that can cause pain and edema in the hand. Their
presence also increases the risk of bone fractures. In 2023, Anttila et al. investigated the capability of DL to
detect enchondroma on hand radiographs . The DL model achieved an AUC of 0.95, with a diagnosis
[84]
accuracy of 0.93, but was slightly outperformed by all three clinical experts.

Ganglion cysts identification
Ganglion cysts, commonly found in the hand and wrist, present a diagnostic challenge as they are often
hypoechoic. To address this issue, Kim et al. explored the potential application of AI models for diagnosing
[85]
this condition . The authors developed a DL model composed of two sequential algorithms, which
achieved a diagnostic accuracy of 75.43%. The results also indicate that the two-step process enhances the
model’s performance and reduces false positive rates, thereby improving the diagnostic accuracy of small
hypoechoic ganglion cysts.

Carpal instability identification
Carpal instability frequently occurs as a consequence of acute trauma, such as scaphoid or distal radius
fractures, often due to tears in the scapholunate (SL) ligament. Therefore, early identification of carpal