Olawade David B, Marinze Sheila, Qureshi Nabeel, Weerasinghe Kusal, Teke Jennifer
Department of Allied and Public Health, School of Health, Sport and Bioscience, University of East London, London, United Kingdom; Department of Research and Innovation, Medway NHS Foundation Trust, Gillingham ME7 5NY, United Kingdom; Department of Public Health, York St John University, London, United Kingdom; School of Health and Care Management, Arden University, Arden House, Middlemarch Park, Coventry CV3 4FJ, United Kingdom.
Department of Surgery, Medway NHS Foundation Trust, Gillingham ME7 5NY, United Kingdom.
Curr Res Transl Med. 2025 Jan 6;73(2):103493. doi: 10.1016/j.retram.2025.103493.
This narrative review examines the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in organ retrieval and transplantation. AI and ML technologies enhance donor-recipient matching by integrating and analyzing complex datasets encompassing clinical, genetic, and demographic information, leading to more precise organ allocation and improved transplant success rates. In surgical planning, AI-driven image analysis automates organ segmentation, identifies critical anatomical features, and predicts surgical outcomes, aiding pre-operative planning and reducing intraoperative risks. Predictive analytics further enable personalized treatment plans by forecasting organ rejection, infection risks, and patient recovery trajectories, thereby supporting early intervention strategies and long-term patient management. AI also optimizes operational efficiency within transplant centers by predicting organ demand, scheduling surgeries efficiently, and managing inventory to minimize wastage, thus streamlining workflows and enhancing resource allocation. Despite these advancements, several challenges hinder the widespread adoption of AI and ML in organ transplantation. These include data privacy concerns, regulatory compliance issues, interoperability across healthcare systems, and the need for rigorous clinical validation of AI models. Addressing these challenges is essential to ensuring the reliable, safe, and ethical use of AI in clinical settings. Future directions for AI and ML in transplantation medicine include integrating genomic data for precision immunosuppression, advancing robotic surgery for minimally invasive procedures, and developing AI-driven remote monitoring systems for continuous post-transplantation care. Collaborative efforts among clinicians, researchers, and policymakers are crucial to harnessing the full potential of AI and ML, ultimately transforming transplantation medicine and improving patient outcomes while enhancing healthcare delivery efficiency.
这篇叙述性综述探讨了人工智能（AI）和机器学习（ML）在器官获取与移植中的变革性作用。人工智能和机器学习技术通过整合和分析包含临床、基因和人口统计学信息的复杂数据集，增强了供体与受体的匹配度，从而实现更精确的器官分配并提高移植成功率。在手术规划中，人工智能驱动的图像分析可自动进行器官分割、识别关键解剖特征并预测手术结果，有助于术前规划并降低术中风险。预测分析还能通过预测器官排斥、感染风险和患者康复轨迹来制定个性化治疗方案，从而支持早期干预策略和患者的长期管理。人工智能还通过预测器官需求、高效安排手术以及管理库存以减少浪费，优化了移植中心的运营效率，从而简化工作流程并提高资源分配效率。尽管取得了这些进展，但仍有一些挑战阻碍了人工智能和机器学习在器官移植中的广泛应用。这些挑战包括数据隐私问题、监管合规问题、医疗系统之间的互操作性，以及对人工智能模型进行严格临床验证的必要性。应对这些挑战对于确保在临床环境中可靠、安全且符合道德地使用人工智能至关重要。移植医学中人工智能和机器学习的未来发展方向包括整合基因组数据以实现精准免疫抑制、推进机器人手术以实现微创手术，以及开发人工智能驱动的远程监测系统以进行移植后的持续护理。临床医生、研究人员和政策制定者之间的合作努力对于充分发挥人工智能和机器学习的潜力至关重要，最终可改变移植医学、改善患者预后并提高医疗服务效率。
