All models are local: time to replace external validation with recurrent local validation
Stanford University（斯坦福大学）；University of Oxford（牛津大学）；Duke University School of Medicine（杜克大学医学院）；Oxford-Suzhou Centre for Advanced Research（牛津-苏州高等研究院）；Stanford University School of Medicine（斯坦福大学医学院）；Stanford Medicine（斯坦福医学院）；Clinical Excellence Research Center（临床卓越研究中心）
Alex Youssef, Michael Pencina, Anshul Thakur, Tingting Zhu, David Clifton, Nigam H. Shah
External validation is often recommended to ensure the generalizability of ML models. However, it neither guarantees generalizability nor equates to a model's clinical usefulness (the ultimate goal of any clinical decision-support tool). External validation is misaligned with current healthcare ML needs. First, patient data changes across time, geography, and facilities. These changes create significant volatility in the performance of a single fixed model (especially for deep learning models, which dominate clinical ML). Second, newer ML techniques, current market forces, and updated regulatory frameworks are enabling frequent updating and monitoring of individual deployed model instances. We submit that external validation is insufficient to establish ML models' safety or utility. Proposals to fix the external validation paradigm do not go far enough. Continued reliance on it as the ultimate test is likely to lead us astray. We propose the MLOps-inspired paradigm of recurring local validation as an alternative that ensures the validity of models while protecting against performance-disruptive data variability. This paradigm relies on site-specific reliability tests before every deployment, followed by regular and recurrent checks throughout the life cycle of the deployed algorithm. Initial and recurrent reliability tests protect against performance-disruptive distribution shifts, and concept drifts that jeopardize patient safety.
Centro de Informática, Universidade Federal de Pernambuco（伯南布哥联邦大学信息中心）；Montreal Institute for Learning Algorithms, University of Montreal（蒙特利尔大学蒙特利尔学习算法研究所）；Northwestern University（西北大学）
Residual GRU+MHSA: A Lightweight Hybrid Recurrent Attention Model for Cardiovascular Disease Detection
George Mason University（乔治·马歇尔大学）；University of Southern Queensland（南方昆士兰大学）；University of Aveiro（阿维罗大学）；Edinburgh Napier University（爱丁堡纳皮尔大学）；Northwood High School（北伍德高中）
Prediction of the Position of External Markers Using a Recurrent Neural Network Trained With Unbiased Online Recurrent Optimization for Safe Lung Cancer Radiotherapy
The University of Tokyo（东京大学）；Japan Atomic Energy Commission（日本原子能委员会）；National Institutes for Quantum and Radiological Science and Technology（日本量子科学技术研究开发机构）
