Applications and Challenges of Artificial Intelligence in Oncologic Surgical Education

Abstract

Surgical training faces significant challenges due to the technical complexity of procedures and the imperative to ensure patient safety during the learning process. In this context, artificial intelligence (AI), machine learning, and robotic platforms are transforming traditional models of surgical education. This paper presents a narrative overview of the impact of these tools on oncologic surgical training. The reviewed evidence indicates that artificial intelligence contributes to improved clinical decision-making, enhances surgical planning, and enables the implementation of automated evaluation systems with objective, real-time feedback. These solutions promote personalized learning pathways and strengthen competency standardization. Moreover, robotic surgery assisted by artificial intelligence provides advanced simulation environments that facilitate the acquisition of technical skills in a safe and controlled manner. Recent developments also emphasize the importance of ethical governance, multi-phase validation in clinical settings, and alignment with international standards such as ISO/IEC 42001:2023 to ensure equitable and effective adoption of these technologies. Nonetheless, significant limitations remain, such as limited validation in real clinical settings, methodological heterogeneity across existing studies, and the high costs associated with implementation. These barriers hinder equitable and sustainable adoption. Future efforts should focus on validating AI-based training systems in real clinical environments to ensure their effectiveness, safety, and relevance in surgical oncology education. This study was registered in the Open Science Framework under the code 10.17605/OSF.IO/QUTC4.

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