Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States.
Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, United States.
J Am Med Inform Assoc. 2024 Apr 19;31(5):1195-1198. doi: 10.1093/jamia/ocae036.
As the enthusiasm for integrating artificial intelligence (AI) into clinical care grows, so has our understanding of the challenges associated with deploying impactful and sustainable clinical AI models. Complex dataset shifts resulting from evolving clinical environments strain the longevity of AI models as predictive accuracy and associated utility deteriorate over time.
Responsible practice thus necessitates the lifecycle of AI models be extended to include ongoing monitoring and maintenance strategies within health system algorithmovigilance programs. We describe a framework encompassing a 360° continuum of preventive, preemptive, responsive, and reactive approaches to address model monitoring and maintenance from critically different angles.
We describe the complementary advantages and limitations of these four approaches and highlight the importance of such a coordinated strategy to help ensure the promise of clinical AI is not short-lived.
随着将人工智能（AI）融入临床护理的热情不断高涨，我们对部署有影响力和可持续的临床 AI 模型所面临的挑战的理解也在不断加深。由于临床环境的不断变化导致复杂的数据集中转移，随着时间的推移，预测准确性和相关效用会逐渐降低，因此 AI 模型的寿命也受到了影响。
因此，负责任的实践需要将 AI 模型的生命周期扩展到卫生系统算法监测计划中，包括持续监测和维护策略。我们描述了一个框架，涵盖了预防性、先发制人、响应性和反应性方法的 360°连续体，从截然不同的角度解决模型监测和维护问题。
我们描述了这四种方法的互补优势和局限性，并强调了这种协调策略的重要性，以帮助确保临床 AI 的承诺不会昙花一现。
