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Related Experiment Video

Updated: Jun 27, 2026

A Teleoperated Robotic System-Assisted Percutaneous Transiliac-Transsacral Screw Fixation Technique
05:57

A Teleoperated Robotic System-Assisted Percutaneous Transiliac-Transsacral Screw Fixation Technique

Published on: January 6, 2023

Artificial Intelligence Applications in Implant Positioning, Dislocation Risk Prediction, and Surgical Indications in

Mihai Emanuel Gherghe1,2, Alex-Gabriel Grigore2, Iosif-Aliodor Timofticiuc1

  • 1Faculty of Medicine, The "Carol Davila" University of Medicine and Pharmacy, 050474 Bucharest, Romania.

Bioengineering (Basel, Switzerland)
|June 26, 2026
PubMed
Summary

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Issues And Trends In Healthcare Delivery System

The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...

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Artificial intelligence (AI) shows promise in orthopaedic surgery for planning and prediction. However, more validation is needed to link AI advancements to improved patient outcomes across all anatomical regions.

Area of Science:

  • Orthopaedic Surgery
  • Artificial Intelligence
  • Machine Learning
  • Deep Learning

Background:

  • Artificial intelligence (AI) is increasingly used in orthopaedic surgery for tasks like implant positioning and predicting dislocation risk.
  • Current evidence on AI applications in orthopaedics is inconsistent across different anatomical regions and specific uses.

Purpose of the Study:

  • To conduct a structured narrative review of studies applying machine learning or deep learning in orthopaedic procedures.
  • To analyze AI applications based on anatomical region, data types, AI tasks, evaluation designs, and validation strategies.

Main Methods:

  • Searched PubMed and Web of Science Core Collection for relevant studies.
  • Excluded reviews and meta-analyses.
Keywords:
arthroplastyartificial intelligencedeep learningmachine learningorthopaedic surgeryoutcome predictionpreoperative planning

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Last Updated: Jun 27, 2026

A Teleoperated Robotic System-Assisted Percutaneous Transiliac-Transsacral Screw Fixation Technique
05:57

A Teleoperated Robotic System-Assisted Percutaneous Transiliac-Transsacral Screw Fixation Technique

Published on: January 6, 2023

  • Synthesized findings narratively, focusing on anatomical region, AI task, imaging, validation, and clinical relevance.

    • Main Results:

    • Identified three key AI application areas: imaging-driven planning/implant positioning, postoperative implant evaluation, and outcome prediction (e.g., dislocation risk).
    • Strongest evidence for AI in hip arthroplasty, improving accuracy and efficiency; less developed in knee, shoulder, and spine surgery.
    • Risk prediction models show good performance but limited generalizability due to insufficient external validation and inconsistent reporting.

    Conclusions:

    • AI holds significant potential to enhance orthopaedic surgery, but stronger connections between technical progress and patient outcomes are required.
    • Future research must focus on extensive validation, workflow integration, failure analysis, and adherence to AI reporting guidelines for safe clinical implementation.