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的激进解决方案，该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AI产品，这是当前评估框架的资源成本的一小部分。增加和老龄化的人口，具有更长期的健康状况，使医疗保健系统的压力增加。如果安全有效地采用\ [1 \]，AI可以减轻这种情况。但是，目前检查商业AI产品的可信度和性能的途径既不是标准化也不可扩展的。80％的医院途径涉及放射科医生的成像及其专家解释\ [11 \]。然而，仅英格兰的短缺为1,453名临床放射科医生顾问（2021），临床放射学主管报告不足以进行安全有效的患者护理。 \ [11 \]。临床放射学中的AI和机器学习工具有望提高检测率，简化临床工作流程并提高患者安全性。但是，尽管早期热情，但对常规实践的采用却很慢。但是，许多评估放射学AI产品的研究和试验受到偏见，缺乏盲目和小数据集以及人群数量\ [5 \]的阻碍。此外，AI产品在所谓的“黑盒”中运行，因此，外行用户不清楚产品运行的方法，围绕信誉和可信赖性造成问题。这对希望部署AI的机构构成了巨大的成本和潜在的临床风险，从而创造了广泛采用的主要障碍。明确的未满足需要开发较少的资源密集型流程，该组织可以通过这些过程来证实医疗设备制造商的主张并确定为医疗保健提供者提供明显临床利益的产品。我们建议创建一个标准化的系统，以验证在采购前在当地临床场所在当地临床场所验证商业保健成像。我们的解决方案评估了黑盒产品的性能，公平性，鲁棒性和解释性。这可以由非技术临床经理使用来快速评估和比较AI产品，从而确保其当地人口和工作计划的适用性。