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From data to precision: The transformative role of AI and machine learning in modern orthopaedic practice
Amit Kumar Yadav1, Prateek Joshi2, Anjali Tiwari3
1Wrightington Hospital, Wigan, UK.
Background:
Artificial Intelligence (AI) and Machine Learning (ML) are revolutionising orthopaedic surgery by transforming clinical problem-solving into data-driven input-output frameworks. AI allows surgeons and clinicians to analyse problems and offer innovative solutions objectively. It also enables clinicians to view these problems as an input-output continuum rather than an obstacle that needs to be solved from the basic principles upwards. These technologies would allow clinicians to bypass traditional reliance on foundational principles, instead leveraging computational models to optimise decision-making and patient outcomes.
Methods:
A scoping review was conducted using PubMed, Scopus, and IEEE Xplore databases (2010-2023), targeting peer-reviewed articles with keywords including Artificial Intelligence, Machine Learning, Generative AI, and Clinical Algorithms. Inclusion criteria prioritised studies demonstrating AI/ML applications in Orthopaedic diagnostics, predictive analytics, or surgical planning.
Results:
Advances in computational power, deep learning architectures, and interoperable data infrastructure have accelerated the development of AI/ML tools for Orthopaedic practice. Key innovations include predictive algorithms for postoperative risk stratification, generative models for patient-specific implant design, and computer vision systems for intraoperative guidance. Ubiquitous adoption of portable data-capture devices (e.g., tablets, voice-recognition systems) and clinician-facing software platforms has further streamlined data aggregation, enhancing model accuracy and clinical relevance.
Conclusion:
The integration of AI/ML into Orthopaedic surgery is driven by synergistic advancements in hardware and software, offering transformative potential for personalised care, surgical precision, and outcome prediction. Future adoption hinges on addressing ethical, regulatory, and interoperability challenges while fostering interdisciplinary collaboration between engineers, clinicians, and data scientists.
