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Liu et al. Art Int Surg. 2025;5:425-33 https://dx.doi.org/10.20517/ais.2025.01 Page 431

[38]
Radiology remains the specialty with the highest volume of AI-related publications . AI has proven useful
in enhancing radiographic diagnoses of conditions such as appendicitis, fractures, and pneumothorax,
thereby facilitating faster emergency care. Studies report high accuracy in detecting stroke, intracranial
[6]
hemorrhage, and suspicious breast lesions . Beyond diagnosis, AI has been creatively applied to workflow
management by tracking imaging processing times and alerting providers to delays, thus expediting patient
[7]
care .
Minimally invasive surgical techniques hold promise for generating abundant new data. Most laparoscopic
and robot-assisted surgical platforms already feature recording capabilities, and AI has shown effectiveness
in image processing [6,7,17,22,25,27,39] . With the ongoing digitization of medicine, intraoperative DLM-based
assistance tools may soon become common. Such models could also shape the future of abdominal wall
reconstruction surgery by integrating patient data - including age, BMI, tobacco use, occupation, and
comorbidities - to predict hernia recurrence risk and identify potential postoperative complications.

Machine learning can further assist by analyzing patient imaging to assess muscle weakness, hernias, fat
distribution, and other anatomical factors influencing surgical outcomes. AI systems may enable augmented
reality via 3D simulations, helping surgeons prepare for procedures and providing real-time anatomical
feedback tailored to the individual patient. Moreover, personalized treatment plans informed by AI can
enhance postoperative care quality following hernia surgery by offering customized recommendations for
nutrition, physical therapy, and recovery.

Limitations
This review included only publications written in English. Although efforts were made to minimize bias,
this restriction may have led to the exclusion of relevant information, introducing some inherent bias. The
use of AI in surgical specialties is an evolving field, as reflected by the recent publication dates of most
included studies - all published within the last 5 years.

In conclusion, AI has significant potential to enhance both the efficacy and efficiency of medical practice.
While machine learning models offer numerous possibilities to support clinical decision making, there is
currently no consensus on optimal methodologies or applications. The diversity of models and variability in
frameworks used across the included studies highlight this lack of standardization. Presently, AI
applications in abdominal wall reconstruction primarily focus on predicting postoperative outcomes;
however, there is considerable potential to expand its utility across all stages of reconstructive care.

DECLARATIONS
Authors’ contributions
Made substantial contributions to the conception and design of the study and performed data analysis and
interpretation: Liu A, Liyanage A, Murariu D
Performed data acquisition, as well as providing administrative, technical, and material support: Chen B,
Deptula P
Drafted the manuscript and made revisions: Liu A, Liyanage A, Chen B, Deptula P, Murariu D

Availability of data and materials
Not applicable.

Financial support and sponsorship
None.