发表机构
Jagannath University; North South University（贾格纳特大学; 北南大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过定性哲学分析，识别AI在公共卫生监测中实施就绪度的四个维度（认识充分性、分配正义、治理伦理、制度合法性），提出社会技术与规范过程的理论框架，以促进资源受限环境中的可持续AI应用。
AI 中文摘要
尽管人工智能（AI）通过早期检测疫情暴发、流行病预测和基于证据的决策，已成为改善疾病监测的一种新兴可信手段，但在许多低收入和中等收入国家（LMICs）中，由于卫生信息系统碎片化、数字不平等、治理问题和机构能力不足等各种因素，AI工具的使用仍面临挑战。迄今为止，大多数研究集中于AI在预测疫情方面的有效性和准确性，而很少关注其部署所需的其他条件。本研究采用定性问题发现研究设计，将主题分析与哲学分析相结合，考察疾病监测中AI实施所面临的结构性和规范性障碍。分析确定了实施就绪度的四个相互关联的维度：认识充分性、分配正义、治理伦理和制度合法性。这些维度提供了一个框架，用以理解知识整合的局限、数字基础设施的不平等、隐私和问责问题以及制度和公众信任的缺失如何制约AI驱动的监测的实际采用。本研究并非提出另一个预测模型，而是发展了一个理论框架，将疾病监测中的AI实施概念化为一个同时涉及社会技术和规范的过程。该框架为后续实证研究奠定了基础，并为在资源受限的医疗环境中设计更具情境敏感性、伦理基础和制度可持续性的AI驱动的疾病监测系统提供了结构化视角。
英文摘要
Despite being an emerging credible means of using artificial intelligence (AI) for improving disease surveillance via early detection of outbreaks, epidemics prediction and evidence-based decision-making, there continue to be challenges in the use of AI tools in many low- and middle-income countries (LMICs) due to various factors including fragmented health information system, digital inequality, governance problems and institutional incapability. Most of the research conducted so far has concentrated on the efficacy and accuracy of AI in terms of predicting outbreaks, with little focus on other conditions necessary for its deployment. This study adopts a qualitative problem-discovery research design, integrating thematic analysis with philosophical analysis to examine the structural and normative barriers surrounding AI implementation in disease surveillance. The analysis identifies four interrelated dimensions of implementation readiness: epistemic adequacy, distributive justice, ethics of governance, and institutional legitimacy. These dimensions provide a framework for understanding how limitations in knowledge integration, unequal digital infrastructure, privacy and accountability concerns, and deficits in institutional and public trust can constrain the practical adoption of AI-enabled surveillance. Rather than proposing another predictive model, this study develops a theoretical framework that conceptualizes AI implementation in disease surveillance as simultaneously a socio-technical and normative process. The framework provides a foundation for subsequent empirical investigation and offers a structured perspective for designing more context-sensitive, ethically grounded, and institutionally sustainable AI-enabled disease surveillance systems in resource-constrained healthcare settings.
Comments15 pages, 3 figures, 3 tables
