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Page 417 Collins et al. Art Int Surg. 2026;6:416-9

Collectively, these contributions suggest that the future of AI in abdominal wall surgery lies not in isolated
algorithms but in the integration of complementary technologies into a continuous cycle of prediction,
guidance, measurement, and learning. Taken together, three overarching messages emerge.

ABDOMINAL WALL SURGERY IS WELL SUITED TO SURGICAL AI
Abdominal wall surgery possesses several characteristics that make it particularly well suited to AI. Its high
procedural volumes provide the scale of data needed to develop and validate robust models, while
laparoscopic and robotic procedures routinely generate rich visual data capturing anatomy, tissue planes,
instruments, and mesh placement. Much of the information required for operative assessment is already
contained within the standard laparoscopic or robotic video stream, making hernia surgery particularly well
suited to video-based AI without the need for additional imaging or sensing technologies.

Clinical decision-making in abdominal wall surgery also presents an important opportunity for AI. The
specialty encompasses a diverse range of patients, defect characteristics, operative techniques, and
reconstructive strategies, with considerable variation in practice and no universally accepted treatment
pathway for many complex cases. This clinical complexity creates a compelling case for AI-assisted decision
support systems capable of integrating multiple sources of information to support more consistent, evidence-
based, and personalized treatment planning.

These characteristics also make the field an ideal setting for AI-driven surgical education, where objective
analysis of operative video can support technical performance evaluation and personalized feedback.

INTEGRATED AI ECOSYSTEMS WILL DELIVER THE GREATEST CLINICAL VALUE
Rather than highlighting isolated AI applications, the greatest clinical value will come from combining
complementary AI technologies across the surgical pathway. Imaging, operative video, clinical records,
patient-reported outcomes, and unstructured clinical narratives each contribute unique information to a
unified decision-support framework. Together, these diverse data streams can support risk prediction before
surgery, guidance during procedures, objective assessment of technical performance, and longitudinal
evaluation.

A recurring theme throughout this Special Issue is the emergence of surgical analytics, in which AI enables
objective measurement of workflow, technical performance, and outcomes to support standardized
assessment, quality assurance, and continuous learning. Rather than functioning as standalone tools, these
technologies are beginning to form integrated ecosystems that learn from every stage of patient care.

BUILDING TRUSTWORTHY AI FOR CLINICAL PRACTICE IS THE MAIN BARRIER
The contributions also provide a realistic perspective on the work that remains before AI can be routinely
integrated into clinical practice. Common priorities include the development of larger and more diverse
datasets, multicentre validation, explainable models, seamless workflow integration, and effective
communication of predictive uncertainty. As foundation models and retrieval-augmented generation
become increasingly embedded within clinical workflows, transparency, traceability, and rigorous evaluation
will become equally important. The goal is not to achieve perfect prediction, but develop trustworthy
decision-support systems that augment clinical judgment.

Ultimately, successful adoption of AI in surgery will depend on more than technological progress. It will
require cultural, educational, and ethical change, with surgeons equipped to interpret AI outputs critically
and apply them appropriately within the broader clinical context. AI systems must not only generate
recommendations but also communicate their confidence, underlying rationale, and limitations.