ARTIFICIAL INTELLIGENCE IN SURGERY: CURRENT TRENDS AND FUTURE

Abstract

Objectives. This review synthesizes recent advances in artificial intelligence (AI) across surgical specialties. We aim to summarize applications of AI throughout the perioperative process and identify current challenges and future directions.

Methods. A comprehensive literature survey of articles published in 2024–2025 on AI in surgery was conducted, following PRISMA guidelines for systematic reviews. Articles were identified via PubMed, Embase, Scopus, Web of Science and Cochrane Library, focusing on AI-based diagnostic tools, preoperative planning, intraoperative assistance, and postoperative care across surgical disciplines. Twenty-five relevant articles were selected and analyzed.

Results. AI applications have proliferated across diverse surgical fields. In plastic and reconstructive surgery, AI algorithms have achieved high accuracy (~85–90%) in tasks like outcome prediction, facial landmark detection, and postoperative evaluation. Spinal surgery benefits from AI-driven planning and navigation: deep learning models outperform traditional methods in preoperative deformity prediction and segmentation, while robotics and computer vision improve instrument’s placement. In gastrointestinal surgery, AI systems enhance decision-making (e.g. selecting resection extent or neoadjuvant therapy) with area-under-curve (AUC) values up to 0.97. Cardiac and thoracic surgery also see improvements: AI-enhanced imaging and augmented reality enable precise tumor localization and early lung cancer detection. Across these domains, AI models (notably convolutional neural networks and ensemble methods) often exceed the performance of traditional clinical tools in lesion detection and risk assessment. However, most studies are retrospective and single-center, with limited external validation. Commonly cited obstacles include data scarcity, annotation needs, and algorithmic opacity.

Conclusions. Recent literature indicates that AI has the potential to transform surgical care – from personalized preoperative planning to intraoperative guidance and enhanced postoperative monitoring. To realize these gains, future work must focus on multicenter validation of AI models, development of ethical frameworks, and integration of AI tools into clinical workflows while maintaining surgeon oversight and patient safety.