Navigating the artificial intelligence (AI) era: a paradigm shift in clinical surgical education

In the pre-artificial intelligence (AI) era, surgical education followed a teaching model based on knowledge learning and skills training, aiming to solve clinical problems and enhance core competencies. The traditional core competency assessment system prioritized evaluating young surgeons’ mastery of basic knowledge, proficiency in operational skills, teamwork, ethical considerations, communication skills, and research innovation (1). However, with the rapid advancement of AI technologies such as large language models (LLMs), machine learning algorithms, image and video recognition, virtual patients, and surgical robots, knowledge acquisition and decision making have become much easier than ever (2). This progress has diminished the importance of memory-based learning and shortened the skills learning curve. The demands on surgeons’ subjective judgment and operational stability in surgery have also gradually decreased (3). Consequently, the advent of AI is transforming the learning model for young surgeons from “rote learning” to “integrative and transformative learning”. The objectives of surgical education are shifting from “remembering knowledge and mastering techniques” to “learning how to ask questions and harness new techniques”. Future surgical education must focus on using AI to create personalized and efficient learning strategies and identifying core competencies that AI cannot replace (4).

AI technology driving innovation in surgical teaching methods

AI technology has brought significant changes to clinical surgical education, enriching teaching methods, enhancing efficiency, and improving assessment models.

The potential applications of LLM as an interactive teaching tool support its utility in the learning of surgical concepts and theories. Research has shown that ChatGPT can pass the United States Medical Licensing Examination (USMLE) (5) and even answer practice questions from the Primary Neurosurgery Written Board Exam (6). In fact, LLMs not only provide accurate answers but also demonstrate their reasoning process, assess student responses, and generate new practice questions to aid understanding.

Technologies such as augmented reality (AR), virtual reality (VR), mixed reality (MR), and extended reality (XR) offer surgeons virtual, unsupervised, repetitive training platforms. These tools help overcome the complexity of anatomical structures, enhance training interest, and promote self-directed learning, ultimately improving training precision and shortening the learning curve. They enable a smoother transition from dry labs to wet labs (7). Additionally, AI-assisted systems use real patient imaging data to create three-dimensional (3D) models of virtual patients, placing surgeons in realistic scenarios. This immersive learning experience helps improve surgical planning and decision-making by enhancing the visibility of critical anatomical structures and aiding in positioning, approach selection, and target avoidance (8). AI-assisted intraoperative navigation systems contribute to identifying critical anatomical structures and providing error warnings, ensuring safer and more precise surgeries (9). AI tools in surgical operations are like walking sticks for young surgeons and safety ropes for patients.

Deep learning—based video segmentation, decoding, and assessment techniques now enable objective evaluation of surgical performance. As early as 2019, segmentation algorithms were developed to divide laparoscopic gastrectomy videos into operational steps (10). Later, systems were created to decode detailed elements of intraoperative activity using vision transformers and supervised contrastive learning (11). Based on these, AI models employing Hidden Markov Models (HMMs), Support Vector Machines (SVMs), and Artificial Neural Networks (ANNs) can evaluate surgical tasks (12). More recently, video-based modular AI scoring systems have been introduced for real-time assessment of surgical performance. These systems allow for multiple serial evaluations, avoid human biases, provide immediate results, and can operate without human supervisors (13).

Shifting goals for surgeon training in the AI era

AI technology not only offers new training tools for surgeons but also challenges traditional training goals. In the foreseeable future, as AI becomes more integrated into medical practice, core competencies may be redefined.

Decreased importance of memorizing medical knowledge

With the development of AI technologies such as LLMs and image/video recognition, basic medical knowledge retrieval and evidence-based decision-making will become easier (14). The need for memorization will diminish, and teaching may shift toward acquiring patient history, extracting diagnostic information, and feeding valuable information to AI systems, while also evaluating the reliability of AI-generated content (15).

Reduced emphasis on mastering standardized operational skills

With the advent of VR, AR, smart intraoperative navigation, and surgical robots, the learning curve for preoperative planning, surgical approaches, and operational skills will shorten. This may lead to a reduced focus on procedural skill acquisition, shifting the emphasis to psychological aspects, such as confidence in handling complex surgeries and the ability to take control of unexpected intraoperative situations.

Increased importance of doctor-patient communication

AI-based diagnostic tools pose new challenges for doctor-patient communication, including the need to explain complex AI algorithms to patients and ensuring that AI recommendations align with clinical judgment. Young surgeons may spend considerable time navigating computer systems and manipulating intellectual systems, which reduces their time spent on face-to-face patient communication. The deficiency of empathetic, compassionate, and trust-building elements in AI tools underscores the necessity for more human touch and emotional support in patient communication. Overreliance on virtual patients may diminish a trainee’s engagement in real clinical situations. In fact, enhanced communication skills require long-term clinical practice and cannot be fully developed through AI training alone.

Strengthened focus on lifelong learning

As AI continues to evolve and integrate into daily clinical practice, the human-AI interaction will be an ongoing reality. Surgeons must be trained not only in using AI tools effectively but also in adapting to and leading the development of new AI technologies.

Change is rarely easy. Some may be hesitant to embrace new technologies, while others are already prepared for the coming transformation. AI technology is undeniably bringing a revolution to surgical training, and we must maintain an open mind to embrace these changes.

Acknowledgments

None.

Footnote

Provenance and Peer Review: This article was commissioned by the editorial office, HepatoBiliary Surgery and Nutrition. The article did not undergo external peer review.

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://hbsn.amegroups.com/article/view/10.21037/hbsn-2025-298/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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