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Commercially available artificial intelligence tools for fracture detection: the evidence
Cato Pauling1, Baris Kanber2,3, Owen J Arthurs1,4,5
1UCL Great Ormond Street Institute of Child Health, University College London, London WC1E 6BT, United Kingdom.
Insights
Missed fractures cause significant patient harm and healthcare costs. This review evaluates artificial intelligence (AI) tools for fracture detection, aiming to improve diagnostic accuracy and inform procurement decisions.
Area of Science:
- Radiology and Medical Imaging
- Artificial Intelligence in Healthcare
- Orthopedics
Background:
- Missed fractures lead to patient disability, lost workdays, and substantial medicolegal costs.
- Overlooking fractures in children raises concerns about missed safeguarding opportunities.
- Artificial intelligence (AI) offers potential for improving medical image interpretation in radiology.
Purpose of the Study:
- To review commercially available AI tools for fracture detection in adults and children.
- To summarize the evidence supporting the performance and validation of these AI tools.
- To guide healthcare providers in making informed decisions for AI procurement in clinical settings.
Main Methods:
- Comprehensive review of available AI products for fracture detection.
- Analysis of reported evidence regarding AI tool development, performance, and validation.
- Assessment of intended target populations for each AI solution.
Main Results:
- Several AI tools for fracture detection are available for radiology workflow implementation.
- Information on AI tool development, validation, and performance evidence is often unclear.
- Lack of transparent data hinders informed evaluation of AI solutions for clinical use.
Conclusions:
- Clearer information on AI tool performance and validation is crucial for effective implementation.
- This review provides a framework for evaluating AI solutions to improve fracture detection.
- Informed procurement of AI tools can enhance patient care and reduce healthcare burdens.
Abstract:
Missed fractures are a costly healthcare issue, not only negatively impacting patient lives, leading to potential long-term disability and time off work, but also responsible for high medicolegal disbursements that could otherwise be used to improve other healthcare services. When fractures are overlooked in children, they are particularly concerning as opportunities for safeguarding may be missed. Assistance from artificial intelligence (AI) in interpreting medical images may offer a possible solution for improving patient care, and several commercial AI tools are now available for radiology workflow implementation. However, information regarding their development, evidence for performance and validation as well as the intended target population is not always clear, but vital when evaluating a potential AI solution for implementation. In this article, we review the range of available products utilizing AI for fracture detection (in both adults and children) and summarize the evidence, or lack thereof, behind their performance. This will allow others to make better informed decisions when deciding which product to procure for their specific clinical requirements.
