RAISE - Radiology AI Safety, an End-to-end lifecycle approach

The integration of AI into radiology introduces opportunities for improved clinical care provision and efficiency but it demands a meticulous approach to mitigate potential risks as with any other new technology. Beginning with rigorous pre-deployment evaluation and validation, the focus should be on ensuring models meet the highest standards of safety, effectiveness and efficacy for their intended applications. Input and output guardrails implemented during production usage act as an additional layer of protection, identifying and addressing individual failures as they occur. Continuous post-deployment monitoring allows for tracking population-level performance (data drift), fairness, and value delivery over time. Scheduling reviews of post-deployment model performance and educating radiologists about new algorithmic-driven findings is critical for AI to be effective in clinical practice. Recognizing that no single AI solution can provide absolute assurance even when limited to its intended use, the synergistic application of quality assurance at multiple levels - regulatory, clinical, technical, and ethical - is emphasized. Collaborative efforts between stakeholders spanning healthcare systems, industry, academia, and government are imperative to address the multifaceted challenges involved. Trust in AI is an earned privilege, contingent on a broad set of goals, among them transparently demonstrating that the AI adheres to the same rigorous safety, effectiveness and efficacy standards as other established medical technologies. By doing so, developers can instil confidence among providers and patients alike, enabling the responsible scaling of AI and the realization of its potential benefits. The roadmap presented herein aims to expedite the achievement of deployable, reliable, and safe AI in radiology.

人工智能融入放射学为改善临床护理服务和提高效率带来了机遇，但与任何其他新技术一样，它需要一种谨慎的方法来降低潜在风险。从严格的部署前评估和验证开始，重点应放在确保模型在其预期应用中符合最高的安全性、有效性和效能标准。在生产使用过程中实施的输入和输出防护措施作为额外的保护层级，在出现个别故障时进行识别和处理。持续的部署后监测能够随着时间推移追踪群体层面的性能（数据漂移）、公平性以及价值交付。安排对部署后模型性能的审查，并对放射科医生进行有关新的算法驱动发现的教育，对于人工智能在临床实践中发挥有效作用至关重要。认识到即使局限于预期用途，也没有单一的人工智能解决方案能够提供绝对保证，因此强调在多个层面——监管、临床、技术和伦理——协同应用质量保证。跨越医疗保健系统、行业、学术界和政府的利益相关者之间的协作努力对于应对所涉及的多方面挑战至关重要。对人工智能的信任是一种赢得的特权，取决于一系列广泛的目标，其中包括透明地证明人工智能遵循与其他已确立的医疗技术相同的严格安全性、有效性和效能标准。通过这样做，开发者能够在医疗服务提供者和患者中树立信心，从而实现人工智能的负责任扩展以及其潜在益处的实现。本文提出的路线图旨在加速在放射学中实现可部署、可靠和安全的人工智能。