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Page 248 Elhage et al. Art Int Surg. 2025;5:247-53 https://dx.doi.org/10.20517/ais.2024.87

technology seeming to emerge by the day. Medicine, specifically surgery, has been quick to adopt this
disruptive technology, finding various ways to incorporate it into research and practice. The volume of AI
publications within surgical fields has increased drastically in the last decade without signs of slowing
[1]
down . Numerous early studies have shown the benefits of AI within medicine, surgery, and, specifically,
plastic surgery, including database analysis, image analysis, predictive algorithms, computer vision, and
[2-8]
intraoperative guidance .
To understand the various applications of AI algorithms, it is important to first develop a basic
understanding of the types of AI algorithms available and the terminology currently in use. AI, machine
learning (ML), and deep learning (DL) are just a few of the oft-confused terms currently in use. While AI is
a general term that captures any technology mimicking human intelligence, ML and DL are subtypes of AI,
with unique characteristics distinguishing their algorithms from one another [9,10] . ML is a form of AI where
models learn to perform tasks without prior instruction, learning only from the dataset and the output of
interest. However, ML requires time-intensive feature extraction and the identification of key features for
the algorithm to focus on to help guide the algorithm. DL models, on the other hand, do not require feature
extraction; instead, they identify important features independently. The tradeoff is that DL models generally
require larger data sets and higher computing power, with the added difficulty of determining what features
the model actually finds important [9,11,12] . Both ML and DL models can be either supervised or unsupervised.
Supervised models are given labeled outputs, meaning the model knows the outcome and has the goal of
creating a map from the input to the output. Supervised models are more frequently employed in medicine,
where a specific question is trying to be answered. Unsupervised learning entails giving a model unlabeled
data, serving a more exploratory purpose where the model tries to identify unknown structures and
[11]
relationships within the data . With this basic understanding of AI, we can better interpret the types of
models being created and used within plastic surgery.

Abdominal wall reconstruction (AWR) is an expansive field that crosses general and plastic surgery, and as
a specialty, it has produced significant research to provide patients with reproducible, reliable, and lasting
outcomes. Ventral and incisional hernias are commonplace, with incisional hernias estimated to occur in
more than 1 in 8 patients after laparotomy. Thus, hernia repair and AWR are among the most common
surgical procedures performed across the globe [13-15] . With the increasing complexity of hernia repair, there
is a concomitant increase in complications, with a higher incidence of wound complications, infections, and
recurrences [16-18] . Described as a “vicious cycle of complications”, it has become well understood that
complications after AWR and hernia repair, specifically wound complications and infection, lead to
recurrence, and reoperation for hernia recurrence incurs added risk of complications and recurrence with
[16]
each operation .
With the emergence of AI technology, AWR, a subspecialty of medicine with the need for such quality
outcomes, and surgeons motivated to study and pursue these outcomes, is a field that is well positioned to
take advantage of the technological disruption of AI in medicine. This review aims to summarize the
current body of evidence for the use of AI in AWR and evaluate the future directions and new applications
of AI technology in the field.

AI DATABASE ANALYSIS
A clear early indication of the use of AI within any field is database analysis, due to AI algorithms’ immense
power and speed in the analysis of large datasets. This trend has held true in AWR, where AI has been used
[19]
to detect long-term infections following inguinal, umbilical, and ventral hernia repairs . Using a ML
algorithm, O’Brien et al. were able to analyze 96,435 inguinal, umbilical, and ventral hernia repairs with