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Page 227 Dababneh et al. Art Int Surg 2024;4:214-32 https://dx.doi.org/10.20517/ais.2024.50

instability is crucial to avoid deterioration of this condition. Nevertheless, signs of carpal instability are often
[86]
unnoticed on conventional radiographs . In response to this diagnostic challenge, Hendrix et al. developed
[87]
an AI model to identify and assess signs of carpal instability on X-rays . The model demonstrated mean
absolute errors (MAE) of 0.65 mm in measuring SL distances, 7.9 degrees for SL angles, and 5.9 degrees for
capitolunate angles. Furthermore, the algorithm achieved an AUC of 0.87 for carpal arc interruption
detection, outperforming four out of five experts.

Peripheral nerve injuries
Gu et al. conducted a study focusing on the remote screening of peripheral nerve injuries . Three gestures,
[88]
each corresponding to a specific nerve, were developed by an expert in the field to detect functional
abnormalities caused by radial, ulnar, or median nerve injury. The authors trained multiple algorithms, all
of which achieved an accuracy rate above 95% for all three gestures, demonstrating their efficacy in
detecting abnormalities in the radial, ulnar, and median nerves.

Prolonged postoperative opioid use prediction
It is well known that opioid use is common after hand surgery. To address this, Baxter et al. conducted a
[89]
study exploring the potential of AI in predicting prolonged opioid use post-hand surgery . Their results
indicate that AI, with further training, can potentially be used to identify patients at risk of prolonged opioid
use, with one of the models achieving an AUC of 0.84.

AI LIMITATIONS
Despite the rapid advancement in the field of AI and its promising performance in controlled settings,
several challenges must be addressed before its full integration into hand surgery. One significant limitation
of many studies examining AI’s role in fracture detection is the lack of validation using external datasets,
often due to the small size and homogeneity of the samples. This limitation arises from data privacy and the
absence of large, labeled datasets across multiple institutions, as well as the need for expert labeling in
[33]
supervised learning . Additionally, methodological variations, concerns about applicability, risks of bias,
and differences in diagnostic protocols between centers further complicate the integration of AI into clinical
practice, highlighting the necessity of standardized guidelines to ensure the quality and reliability of AI-
driven models and provide structured and consistent methodologies . Moreover, most existing studies are
[46]
retrospective, which, while useful for demonstrating proof of concept, fall short of establishing the robust
evidence required for clinical application. Therefore, prospective studies are needed to confirm the
performance metrics and to demonstrate AI’s potential to enhance patient management and outcomes.

Moreover, while LLMs have shown their potential in accessing, interpreting, and synthesizing extensive
amounts of information, they still struggle with complex cases that require the integration of nuanced
clinical contexts. Despite advancements in newer versions, these models are not yet fully equipped to meet
the clinical needs of patients and healthcare providers. Similarly, to ensure the effective integration of AI
models into academic contexts, there is a need for further training to enhance the validity of AI-generated
content and to improve the transparency of the sources from which this information is derived.

Finally, it is also essential to acknowledge that at this stage of its evolution, AI does not possess the
capability to replace humans. Numerous ethical and liability concerns must be thoroughly examined before
such a possibility can even be considered. As AI technology continues to advance, it is crucial to develop
clear guidelines and establish a robust regulatory framework in parallel, ensuring that these innovations are
integrated responsibly and ethically.