Lallinger Vincent, Hinterwimmer Florian, von Eisenhart-Rothe Rüdiger, Lazic Igor
Klinik für Orthopädie und Sportorthopädie, Technische Universität München, School of Medicine, Klinikum rechts der Isar, Ismaninger Str. 22, 81675, München, Deutschland.
Institut für Künstliche Intelligenz in der Medizin, Technische Universität München, München, Deutschland.
Orthopadie (Heidelb). 2025 Mar;54(3):199-204. doi: 10.1007/s00132-025-04619-6. Epub 2025 Feb 3.
Artificial intelligence is very likely to be a pioneering technology in arthroplasty, with a wide range of pre-, intra- and post-operative applications. The opportunities for patients, doctors and healthcare policy are considerable, especially in the context of optimized and individualized patient care.
Despite these diverse possibilities, there are currently only a few AI applications in routine clinical practice, mainly due to the limited availability of analyzable health data. AI systems are only as good as the data they are trained with. If the data is insufficient, incomplete or biased, the AI may draw false conclusions. The current results of such AI applications in arthroplasty must, therefore, be viewed critically, especially as previous data bases were not designed a priori for AI applications.
The successful integration of AI, therefore, requires a targeted focus on the development of a specific data structure. In order to exploit the full potential of AI, comprehensive clinical data volumes are required, which can only be realized through a multicentric approach. In this context, ethical and data protection issues remain a further question, and not only in orthopaedics. Cooperative efforts at national and international levels are, therefore, essential in order to research and develop new AI applications.
人工智能很可能成为关节成形术领域的一项开创性技术，在术前、术中和术后有着广泛的应用。对患者、医生和医疗保健政策而言，机遇巨大，尤其是在优化和个性化患者护理的背景下。
尽管存在这些多样的可能性，但目前在常规临床实践中人工智能应用仅有少数几种，主要原因是可分析的健康数据有限。人工智能系统的性能取决于其训练所使用的数据。如果数据不足、不完整或有偏差，人工智能可能会得出错误结论。因此，必须审慎看待目前关节成形术领域此类人工智能应用的结果，尤其是因为先前的数据库并非一开始就为人工智能应用而设计。
因此，人工智能的成功整合需要有针对性地专注于特定数据结构的开发。为了充分发挥人工智能的潜力，需要大量全面的临床数据，而这只有通过多中心方法才能实现。在这种情况下，伦理和数据保护问题仍然是一个进一步的问题，而且不仅在骨科领域如此。因此，国家和国际层面的合作努力对于研究和开发新的人工智能应用至关重要。
