MLHOps: Machine Learning for Healthcare Operations
Vector Institute for Artificial Intelligence（向量人工智能研究所）；Hospital for Sick Children（病童医院）；University of Toronto（多伦多大学）；York University（约克大学）；Dalhousie University（达尔豪斯大学）
机器学习医疗运营（Machine Learning Health Operations，简称MLHOps）是在医疗场景中可靠、高效、易用且合乎伦理地部署与维护机器学习模型的各类流程的结合体。本文既对该领域的相关研究进行了综述，也为开发者和临床人员在临床实践中部署、维护自有模型提供了指南。我们涵盖了通用机器学习运营的基础概念，阐述了MLHOps流水线的初始搭建（包括数据源、数据预处理、特征工程及相关工具），随后介绍了长期监控与更新（包括数据分布偏移和模型更新）以及伦理考量（包括偏差、公平性、可解释性和隐私）。因此，本研究为MLHOps从概念构思到初始部署及持续部署的全流程提供了指导。
英文摘要：
Machine Learning Health Operations (MLHOps) is the combination of processes for reliable, efficient, usable, and ethical deployment and maintenance of machine learning models in healthcare settings. This paper provides both a survey of work in this area and guidelines for developers and clinicians to deploy and maintain their own models in clinical practice. We cover the foundational concepts of general machine learning operations, describe the initial setup of MLHOps pipelines (including data sources, preparation, engineering, and tools). We then describe long-term monitoring and updating (including data distribution shifts and model updating) and ethical considerations (including bias, fairness, interpretability, and privacy). This work therefore provides guidance across the full pipeline of MLHOps from conception to initial and ongoing deployment.
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection
Informatics Institute, University of Amsterdam（阿姆斯特丹大学信息学院）；Department of Computer Science and Technology, SUNY Empire State University（SUNY埃默里州立大学计算机科学与技术系）
As opportunities for AI-assisted healthcare grow steadily, model deployment faces challenges due to the specific characteristics of the industry. The configuration choice for a production device can impact model performance while influencing operational costs. Moreover, in healthcare some situations might require fast, but not real time, inference. We study different configurations and conduct a cost-performance analysis to determine the optimized hardware for the deployment of a model subject to healthcare domain constraints. We observe that a naive performance comparison may not lead to an o…
OPTIC: Optimizing Patient-Provider Triaging & Improving Communications in Clinical Operations using GPT-4 Data Labeling and Model Distillation
Microsoft Health AI（微软健康人工智能）；Johns Hopkins University（约翰斯·霍普金斯大学）；Health Information Technology（健康信息技术）；Johns Hopkins Medicine（约翰斯·霍普金斯医学）；University of Washington（华盛顿大学）；Microsoft（微软）
Parkland Center for Clinical Innovation（帕克兰临床创新中心）；Dallas, TX 75247, USA（美国得克萨斯州达拉斯市75247（注：此条为地址，非机构，按要求不应提取，实际仅需保留Parkland Center for Clinical Innovation））
In recent times, machine learning (ML) and artificial intelligence (AI) based systems have evolved and scaled across different industries such as finance, retail, insurance, energy utilities, etc. Among other things, they have been used to predict patterns of customer behavior, to generate pricing models, and to predict the return on investments. But the successes in deploying machine learning models at scale in those industries have not translated into the healthcare setting. There are multiple reasons why integrating ML models into healthcare has not been widely successful, but from a techni…
Generative Artificial Intelligence in Healthcare: Ethical Considerations and Assessment Checklist
Duke-NUS Medical School（杜克-新加坡国立大学医学院）；National University of Singapore（新加坡国立大学）；University of Oxford（牛津大学）；Imperial College London（帝国理工学院）；University of Antwerp（安特卫普大学）；Singapore Eye Research Institute（新加坡眼科研究所）；Singapore National Eye Centre（新加坡全国眼科中心）；Singapore Health Services（新加坡保健服务）；Singapore General Hospital（新加坡中央医院）；Hangzhou Dianzi University（杭州电子科技大学）；Nanyang Technological University（南洋理工大学）；The Chinese University of Hong Kong（香港中文大学）；Scripps Research（斯克里普斯研究所）
RISED: A Pre-Deployment Evaluation Framework for High-Stakes AI Decision-Support Systems, with Application to Healthcare
Florida International University（佛罗里达国际大学）；Boston University（波士顿大学）；New York University（纽约大学）；University of Maryland（马里兰大学）；Boston University School of Public Health（波士顿大学公共卫生学院）
Many institutions within the healthcare ecosystem are making significant investments in AI technologies to optimize their business operations at lower cost with improved patient outcomes. Despite the hype with AI, the full realization of this potential is seriously hindered by several systemic problems, including data privacy, security, bias, fairness, and explainability. In this paper, we propose a novel canonical architecture for the development of AI models in healthcare that addresses these challenges. This system enables the creation and management of AI predictive models throughout all t…
Beyond One-Time Validation: A Framework for Adaptive Validation of Prognostic and Diagnostic AI-based Medical Devices
x-cardiac GmbH（x-cardiac有限公司）；University of Tübingen（图宾根大学）；Deutsches Herzzentrum der Charité（夏里特德国心脏中心）；German Centre for Cardiovascular Research（德国心血管研究中心）；Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究所）
Generalist Foundation Models Are Not Clinical Enough for Hospital Operations
Courant Institute School of Mathematics, Computing, and Data Science（Courant学院数学、计算与数据科学学院）；New York University（纽约大学）；Department of Neurosurgery（神经外科部）；Global AI Frontier Lab（全球人工智能前沿实验室）；Electrical and Computer Engineering（电气与计算机工程）；Tandon School of Engineering（Tandon工程学院）；Grossman School of Medicine（Grossman医学院）；Department of Medicine（医学部）；Department of Computer Science（计算机科学部）；Department of Surgery（外科部）；Division of Applied AI Technologies（应用人工智能技术部）；Department of Population Health（人口健康部）；ETH Zurich（苏黎世联邦理工学院）
For centuries nursing has been known as a job that requires complex manual operations, that cannot be automated or replaced by any machinery. All the devices and techniques have been invented only to support, but never fully replace, a person with knowledge and expert intuition. With the rise of Artificial Intelligence and continuously increasing digital data flow in healthcare, new tools have arrived to improve patient care and reduce the labour-intensive work conditions of a nurse. This cross-disciplinary review aims to build a bridge over the gap between computer science and nursing. It out…
