Accelerating adoption of trustworthy AI in radiology: scalable software for non-technical clinical users to independently validate commercial products at local sites

加速在放射学中采用值得信赖的人工智能：为非技术临床用户提供可扩展的软件，以在本地站点独立验证商业产品

• 批准号: 10064189
• 金额: $ 6.06万
• 依托单位: METALYNX LTD
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=10064189
• 关键词: Accelerating; adoption; trustworthy; AI; radiology

We propose a novel and radical solution that allows healthcare providers and hospitals to rapidly evaluate and test radiology AI products in an independent manner, at a fraction of the resource cost of current evaluation frameworks.An increasing and aging population, with more long-term health conditions, is putting increasing strain on the healthcare system. AI can alleviate this if adopted safely and effectively \[1\]. However, current pathways to examine the trustworthiness and performance of commercial AI products are neither standardised nor scalable.Over 80% of hospital pathways involve imaging and its expert interpretation by radiologists \[11\]. However, England alone had a shortfall of 1,453 clinical radiologist consultants (2021), with clinical radiology directors reporting insufficient numbers for safe and effective patient care. \[11\]. AI and machine learning tools in clinical radiology promise to improve detection rates, streamline clinical workflow and improve patient safety. However, despite early enthusiasm, adoption into routine practice has been slow \[2\].A key reason for this is that, prior to deployment, each product must be externally and independently validated on local data to ensure reproducibility, generalisability and trustworthiness, along with operational performance and points of failure \[3\]. However, many studies and trials evaluating radiology AI products are hampered by bias, lack of blinding and small datasets and population numbers \[5\]. Furthermore, AI products operate in so-called "black-boxes" whereby the method in which the product operates is unclear to the lay user, creating issues around credibility and trustworthiness.This poses significant cost and potential clinical risk to institutions looking to deploy AI, creating a major barrier to widespread adoption. There is a clear unmet need to develop less resource intensive processes by which organizations can substantiate medical device manufacturer claims and identify products that offer clear clinical benefit to healthcare providers.We propose to create a standardised system to validate commercial healthcare imaging AI products at local clinical sites before procurement. Our solution assesses the performance, fairness, robustness and explainability of a black-box product. This can be used by non-technical clinical managers to quickly evaluate and compare AI products, ensuring suitability for their local populations and work schemes.

我们提出了一种新颖而激进的解决方案，使医疗保健提供者和医院能够以独立的方式快速评估和测试放射学人工智能产品，其资源成本仅为当前评估框架的一小部分。人口的不断增长和老龄化，以及更多的长期健康状况，正在给医疗保健系统带来越来越大的压力。如果安全有效地采用人工智能可以缓解这种情况\[1\]。然而，目前检验商业人工智能产品可信度和性能的途径既没有标准化，也没有可扩展性。80岁以上% of hospital pathways involve imaging and its expert interpretation by radiologists \[11\]. However, England alone had a shortfall of 1,453 clinical radiologist consultants (2021), with clinical radiology directors reporting insufficient numbers for safe and effective patient care. \[11\]. AI and machine learning tools in clinical radiology promise to improve detection rates, streamline clinical workflow and improve patient safety. However, despite early enthusiasm, adoption into routine practice has been slow \[2\].A key reason for this is that, prior to deployment, each product must be externally and independently validated on local data to ensure reproducibility, generalisability and trustworthiness, along with operational performance and points of failure \[3\]. However, many studies and trials evaluating radiology AI products are hampered by bias, lack of blinding and small datasets and population numbers \[5\]. Furthermore, AI products operate in so-called "black-boxes" whereby the method in which the product operates is unclear to the lay user, creating issues around credibility and trustworthiness.This poses significant cost and potential clinical risk to institutions looking to deploy AI, creating a major barrier to widespread adoption. There is a clear unmet need to develop less resource intensive processes by which organizations can substantiate medical device manufacturer claims and identify products that offer clear clinical benefit to healthcare providers.We propose to create a standardised system to validate commercial healthcare imaging AI products at local clinical sites before procurement. Our solution assesses the performance, fairness, robustness and explainability of a black-box product. This can be used by non-technical clinical managers to quickly evaluate and compare AI products, ensuring suitability for their local populations and work schemes.