卫生系统中算法的社会技术审视：技术、成本与以人为本的考量

中文摘要 AI 辅助

人工智能（AI）在医疗保健领域的应用日益普遍，旨在协助卫生系统、服务提供者和患者完成决策、风险预测和诊断等任务。这种日益增长的计算潜力将AI应用推到了工作场所决策的前沿，但往往未充分考虑随之而来的计算、组织和社会成本。这些应用被用于降低医疗成本并提高日常任务的效率，而模型相关成本则在不同的粒度层面上被考量。为了理解这些趋势，我们批判性地分析了114篇论文，以考察针对卫生系统开发的成本感知型AI模型。我们探讨了这些模型的数据、方法和结果选择，以及它们与成本和以人为本的关注点的交集，强调了严格的社会技术模型设计中的空白。基于这些趋势，我们定义了模型成本及其后续维度，并对那些报告了财务、计算、组织和/或社会指标的研究提供了见解。此外，我们批判性地评估了在为卫生系统开发AI模型时，评估模型相关成本和可持续性问题的益处与挑战。

英文摘要

Artificial intelligence (AI) applications in healthcare are becoming increasingly prevalent, to assist health systems, providers, and patients with tasks such as decision-making, risk prediction, and diagnosis. This increasing computational potential brings AI applications to the forefront of workplace decision making, often without full consideration of subsequent computational, organizational, and social costs. These applications are leveraged to reduce healthcare costs and increase efficiency of daily tasks, with model-related costs being considered at varying levels of granularity. To understand these trends, we critically analyze 114 papers to examine how cost-aware AI models have been developed for health systems. We explore the data, method, and outcome choices of these models, as well as their intersection with cost and human-centered concerns, highlighting the gaps in rigorous sociotechnical model design. From these trends, we define model costs and subsequent dimensions, presenting insight into those studies reporting financial, computational, organizational and/or social measures. Further, we critique the benefits and challenges of evaluating model-related costs and sustainability concerns when developing AI models for health systems.

发表机构

• University of Toronto（多伦多大学）

机构由 AI 辅助整理，请以论文原文为准。