Page 28 - Read Online

Page 348 Walker et al. Art Int Surg. 2025;5:345-9 https://dx.doi.org/10.20517/ais.2025.02

surgery, as much as any other discipline, is rapidly evolving. The number of hernia surgeries performed in
[13]
the United States has increased compared to a decade ago . As a relatively young subspecialty, hernia
surgery continues to evolve rapidly. Novel surgical techniques are published each year, accompanied by a
[14]
growing emphasis on academic productivity aimed at generating high-quality data . Now more than ever
is the appropriate time to embrace AI technology to navigate the changing landscape of hernia surgery and
to continue providing the highest standard of care for our patients.

There are two factors that have the potential to positively influence AI’s adoption in the hernia community,
and the two are interconnected. The first factor is the generation of large-scale data. The data consist of
social and demographic information, preoperative imaging, operative technique, and postoperative
outcomes. While the utilization of institutional data is important to track outcomes and improve surgeon
performance, the authors recognize that maximum AI utility will require big data collected over long
periods of time . Structured and well-formulated efforts should be made on the front end to organize and
[15]
standardize data for AI processing. Contrary to the relative immediacy by which AI can process data, the
planning and thought that would go into formulating the gathering of data for meaningful AI model
development would be time- and resource-intensive. The second factor that would help lead to the efficient
and widespread adoption of AI platforms is buy-in from surgeon leaders and surgical societies. Societies like
the American Hernia Society and registries like the Abdominal Health Core Quality Collaborative have the
opportunity to lead the way in warehousing data from surgeons that can be used to enhance outcome
prediction models, refine operative techniques through video review, and gather postoperative information
that will help define hernia care.

Engaging in collaborative projects with non-medical disciplines such as computer science and data analytics
is an opportunity for advancing AI in the surgical field. Such interdisciplinary collaboration fosters a
synergistic relationship where each field enhances the other, driving innovation and improving the quality
of care. Surgeons understand the clinical needs, challenges, and intricacies of surgical procedures, which are
critical for developing relevant AI solutions. Computer scientists bring skills in algorithm design, ML, and
big data analytics, enabling the creation of robust and scalable AI models. Collaboration ensures that AI
tools are clinically relevant, user-friendly, and designed to address real-world surgical problems.
Furthermore, collaborating with data scientists can introduce surgeons to new technologies and research
methodologies, fostering ongoing innovation. Just like subspecialization in the surgical field, relying on
other professionals with specific training helps to fill in knowledge gaps that surgeons are unfamiliar with.

AI holds immense potential to enhance outcomes and precision and optimize patient care. However, it
needs careful implementation and interdisciplinary collaboration to address ethical, practical, and
technological challenges. By fostering collaborations among surgeons, data analysts, and computer
scientists, the surgical community can ensure that AI-driven advancements are both transformative and
patient-centered. In hernia surgery, the future of AI should focus on supporting all stages of care -
preoperative, intraoperative, and postoperative. Although AI has significant potential for integration into
clinical care, its use in surgical care remains limited. Further organized efforts by surgical societies and
organizations to store and leverage large-scale data could facilitate the development of AI models and
technologies for routine clinical use. Ultimately, the advancement of hernia surgery - and medicine more
broadly - may depend on the pace at which AI is adopted in clinical practice.

DECLARATIONS
Authors’ contributions
Responsible for the conceptualization and drafting of the original manuscript, as well as the final editing:
Walker VL, Heniford BT, Ayuso SA