人工智能赋能减重代谢外科：进展、挑战与前景

AI-enabled metabolic and bariatric surgery: progress, challenges, and future directions

摘要:

肥胖已成为全球性公共卫生难题，减重代谢外科（MBS）是目前最有效的治疗手段之一，但患者异质性、手术流程复杂及术后依从性差等因素导致疗效差异显著。人工智能（AI）技术的快速发展为解决上述问题提供了新的途径。通过整合多维度临床、影像和动态随访数据，机器学习和大语言模型可用于手术适应证判断、方案选择、并发症预测、技能评估以及长期预后管理，逐渐成为 MBS 全流程管理的重要工具。近年来相关研究在决策支持、围手术期风险分层、机器人辅助手术优化、患者教育和术后体质量轨迹预测等方面均取得了进展，但其临床应用仍面临模型泛化能力不足、伦理规范缺乏、数据隐私和决策透明度等挑战。本文综述AI在MBS中的主要应用进展、局限性及未来发展方向，为临床医师理解和规范使用相关技术提供参考。

Abstract:

Obesity has become a global public health challenge, and metabolic and bariatric surgery (MBS) remains one of the most effective treatments for severe obesity. However, substantial variability in patient characteristics, surgical complexity, and postoperative adherence leads to heterogeneous outcomes. The rapid evolution of artificial intelligence (AI) offers new opportunities to address these limitations. By integrating multidimensional clinical, imaging, and longitudinal follow-up data, machine learning and large language models support key aspects of MBS, including candidate selection, surgical decision-making, perioperative risk prediction, skill assessment, and long-term outcome management. Recent studies have demonstrated notable progress in decision support, complication forecasting, robotic surgery optimization, patient counselling, and postoperative weight-trajectory prediction. Nevertheless, challenges remain regarding model generalizability, ethical and regulatory oversight, data privacy, and transparency in AI-assisted decision-making. This review summarizes current advances, limitations, and future directions of AI applications in MBS, providing a reference for clinicians seeking to understand and apply these emerging technologies.