Gong Miao, Jiang Yingsong, Sun Yingshuo, Liao Rui, Liu Yanyao, Yan Zikang, He Aiting, Zhou Mingming, Yang Jie, Wu Yongzhong, Wu Zhongjun, Huang ZuoTian, Wu Hao, Jiang Liqing
Department of Hepatobiliary Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Department of Obstetrics and Gynecology, Jinan Central Hospital of Shandong Province, Jinan, Shandong, China.
Int J Med Inform. 2025 Mar;195:105782. doi: 10.1016/j.ijmedinf.2024.105782. Epub 2024 Dec 31.
Solid organ transplantation (SOT) is vital for end-stage organ failure but faces challenges like organ shortage and rejection. Artificial intelligence (AI) offers potential to improve outcomes through better matching, success prediction, and automation. However, the evolution of AI in SOT research remains underexplored. This study uses bibliometric analysis to identify trends, hotspots, and key contributors in the field.
821 articles from the Web of Science Core Collection were exported for analysis. Microsoft Excel 2021 was used for descriptive statistics. VOSviewer, CiteSpace, Scimago Graphica, and Biblioshiny were used for bibliometric analysis. The ggalluvial package in R was utilized to create Sankey diagrams, and top articles were selected based on citation count.
This analysis reveals the rapid expansion of AI in SOT. Key areas include robotic surgery, organ allocation, outcome prediction, immunosuppression management, and precision medicine. Robotic surgery has improved transplant outcomes. AI algorithms optimize organ matching and enhance fairness. Machine learning models predict outcomes and guide treatment, while AI-based systems advance personalized immunosuppression. AI in precision medicine, including diagnostics and imaging, is crucial for transplant success.
This study highlights AI's transformative potential in SOT, with significant contributions from countries like the USA, Canada, and the UK. Key institutions such as the University of Toronto and the University of Pittsburgh have played vital roles. However, practical challenges like ethical issues, bias, and data integration remain. Fostering international and interdisciplinary collaborations is crucial for overcoming these challenges and accelerating AI's integration into clinical practice, ultimately improving patient outcomes.
实体器官移植（SOT）对于终末期器官衰竭至关重要，但面临器官短缺和排斥等挑战。人工智能（AI）有潜力通过更好的匹配、成功预测和自动化来改善治疗结果。然而，人工智能在SOT研究中的发展仍未得到充分探索。本研究使用文献计量分析来确定该领域的趋势、热点和关键贡献者。
从科学网核心合集中导出821篇文章进行分析。使用Microsoft Excel 2021进行描述性统计。使用VOSviewer、CiteSpace、Scimago Graphica和Biblioshiny进行文献计量分析。利用R中的ggalluvial包创建桑基图，并根据被引频次选择高影响力文章。
该分析揭示了人工智能在SOT中的快速发展。关键领域包括机器人手术、器官分配、结果预测、免疫抑制管理和精准医学。机器人手术改善了移植结果。人工智能算法优化了器官匹配并提高了公平性。机器学习模型预测结果并指导治疗，而基于人工智能的系统推动了个性化免疫抑制。精准医学中的人工智能，包括诊断和成像，对移植成功至关重要。
本研究强调了人工智能在SOT中的变革潜力，美国、加拿大和英国等国家做出了重大贡献。多伦多大学和匹兹堡大学等关键机构发挥了重要作用。然而，伦理问题、偏差和数据整合等实际挑战仍然存在。促进国际和跨学科合作对于克服这些挑战以及加速人工智能融入临床实践至关重要，最终可改善患者治疗结果。
