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Background <p>Artificial intelligence is very likely to be a&#xa0;pioneering technology in arthroplasty, with a&#xa0;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.</p> Data availability <p>Despite these diverse possibilities, there are currently only a&#xa0;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&#xa0;priori for AI applications.</p> Prospects <p>The successful integration of AI, therefore, requires a&#xa0;targeted focus on the development of a&#xa0;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&#xa0;multicentric approach. In this context, ethical and data protection issues remain a&#xa0;further question, and not only in orthopaedics. Cooperative efforts at&#xa0;national and international levels are, therefore, essential in order to research and develop new AI applications.</p>

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Künstliche Intelligenz in der Endoprothetik

  • 期刊论文

  • 发表日期：

    2025年2月3日

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  • Vincent Lallinger,
  • Florian Hinterwimmer,
  • Rüdiger von Eisenhart-Rothe,
  • Igor Lazic

摘要

Background

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.

Data availability

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.

Prospects

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.