Pauling Cato, Kanber Baris, Arthurs Owen J, Shelmerdine Susan C
UCL Great Ormond Street Institute of Child Health, University College London, London WC1E 6BT, United Kingdom.
Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, University College London (UCL) Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London WC1N 3BG, United Kingdom.
BJR Open. 2023 Dec 12;6(1):tzad005. doi: 10.1093/bjro/tzad005. eCollection 2024 Jan.
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.
漏诊骨折是一个代价高昂的医疗保健问题，不仅会对患者的生活产生负面影响，导致潜在的长期残疾和误工，还会造成高额的医疗法律费用，而这些费用原本可用于改善其他医疗服务。当儿童骨折被忽视时，情况尤为令人担忧，因为可能会错过保障儿童安全的机会。人工智能（AI）在解读医学影像方面提供的帮助或许可为改善患者护理提供一种可能的解决方案，目前已有多款商业AI工具可用于放射学工作流程。然而，关于这些工具的开发情况、性能和验证证据以及目标人群等信息并不总是清晰明了，但在评估潜在的AI实施解决方案时却至关重要。在本文中，我们回顾了利用AI进行骨折检测（包括成人和儿童）的现有产品范围，并总结其性能背后的证据或缺乏证据的情况。这将使其他人在为其特定临床需求决定采购哪种产品时能够做出更明智的决策。
