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Bone & Joint Research
Vol. 13, Issue 9 | Pages 507 - 512
18 Sep 2024
Farrow L Meek D Leontidis G Campbell M Harrison E Anderson L

Despite the vast quantities of published artificial intelligence (AI) algorithms that target trauma and orthopaedic applications, very few progress to inform clinical practice. One key reason for this is the lack of a clear pathway from development to deployment. In order to assist with this process, we have developed the Clinical Practice Integration of Artificial Intelligence (CPI-AI) framework – a five-stage approach to the clinical practice adoption of AI in the setting of trauma and orthopaedics, based on the IDEAL principles (https://www.ideal-collaboration.net/). Adherence to the framework would provide a robust evidence-based mechanism for developing trust in AI applications, where the underlying algorithms are unlikely to be fully understood by clinical teams.

Cite this article: Bone Joint Res 2024;13(9):507–512.


The Bone & Joint Journal
Vol. 106-B, Issue 7 | Pages 688 - 695
1 Jul 2024
Farrow L Zhong M Anderson L

Aims

To examine whether natural language processing (NLP) using a clinically based large language model (LLM) could be used to predict patient selection for total hip or total knee arthroplasty (THA/TKA) from routinely available free-text radiology reports.

Methods

Data pre-processing and analyses were conducted according to the Artificial intelligence to Revolutionize the patient Care pathway in Hip and knEe aRthroplastY (ARCHERY) project protocol. This included use of de-identified Scottish regional clinical data of patients referred for consideration of THA/TKA, held in a secure data environment designed for artificial intelligence (AI) inference. Only preoperative radiology reports were included. NLP algorithms were based on the freely available GatorTron model, a LLM trained on over 82 billion words of de-identified clinical text. Two inference tasks were performed: assessment after model-fine tuning (50 Epochs and three cycles of k-fold cross validation), and external validation.


Bone & Joint Open
Vol. 3, Issue 10 | Pages 753 - 758
4 Oct 2022
Farrow L Clement ND Smith D Meek DRM Ryan M Gillies K Anderson L Ashcroft GP

Aims

The extended wait that most patients are now experiencing for hip and knee arthroplasty has raised questions about whether reliance on waiting time as the primary driver for prioritization is ethical, and if other additional factors should be included in determining surgical priority. Our Prioritization of THose aWaiting hip and knee ArthroplastY (PATHWAY) project will explore which perioperative factors are important to consider when prioritizing those on the waiting list for hip and knee arthroplasty, and how these factors should be weighted. The final product will include a weighted benefit score that can be used to aid in surgical prioritization for those awaiting elective primary hip and knee arthroplasty.

Methods

There will be two linked work packages focusing on opinion from key stakeholders (patients and surgeons). First, an online modified Delphi process to determine a consensus set of factors that should be involved in patient prioritization. This will be performed using standard Delphi methodology consisting of multiple rounds where following initial individual rating there is feedback, discussion, and further recommendations undertaken towards eventual consensus. The second stage will then consist of a Discrete Choice Experiment (DCE) to allow for priority setting of the factors derived from the Delphi through elicitation of weighted benefit scores. The DCE consists of several choice tasks designed to elicit stakeholder preference regarding included attributes (factors).


The Bone & Joint Journal
Vol. 103-B, Issue 12 | Pages 1754 - 1758
1 Dec 2021
Farrow L Zhong M Ashcroft GP Anderson L Meek RMD

There is increasing popularity in the use of artificial intelligence and machine-learning techniques to provide diagnostic and prognostic models for various aspects of Trauma & Orthopaedic surgery. However, correct interpretation of these models is difficult for those without specific knowledge of computing or health data science methodology. Lack of current reporting standards leads to the potential for significant heterogeneity in the design and quality of published studies. We provide an overview of machine-learning techniques for the lay individual, including key terminology and best practice reporting guidelines.

Cite this article: Bone Joint J 2021;103-B(12):1754–1758.