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[Artificial intelligence in arthroplasty].

Vincent Lallinger1, Florian Hinterwimmer2,3, Rüdiger von Eisenhart-Rothe2

  • 1Klinik für Orthopädie und Sportorthopädie, Technische Universität München, School of Medicine, Klinikum rechts der Isar, Ismaninger Str. 22, 81675, München, Deutschland. Vincent.Lallinger@mri.tum.de.

Orthopadie (Heidelberg, Germany)
|February 3, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) offers significant potential in arthroplasty, but its clinical integration is hindered by data limitations. Developing specific data structures and multicentric approaches is crucial for advancing AI in orthopedic care.

Keywords:
Deep learningHip replacementKnee replacementMachine learningRobot surgery

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Area of Science:

  • Exploring the transformative role of artificial intelligence (AI) in orthopedic surgery.
  • Highlighting AI's potential across pre-operative, intra-operative, and post-operative phases of arthroplasty.

Context:

  • Limited availability of analyzable health data currently restricts AI applications in routine clinical practice.
  • Existing health databases were not initially designed for AI applications, impacting current AI performance in arthroplasty.
  • Data quality issues, including insufficiency, incompleteness, and bias, can lead to inaccurate AI conclusions.

Purpose:

  • To underscore the critical need for targeted development of specific data structures for successful AI integration in arthroplasty.
  • To emphasize the requirement for comprehensive clinical data volumes to fully realize AI's potential in patient care.

Summary:

  • Successful AI integration in arthroplasty necessitates a focus on specialized data structures.
  • Multicentric approaches are essential for acquiring the large-scale clinical data volumes required for robust AI development.
  • Ethical and data protection considerations are paramount and require collaborative national and international efforts.

Impact:

  • Facilitating optimized and individualized patient care through advanced AI applications.
  • Driving innovation in orthopedic surgery by overcoming data-related challenges.
  • Promoting cooperative research and development for novel AI solutions in healthcare.