The study investigates the juridico-technological architecture of international public health instruments, focusing on their implementation across India, the European Union, the United States and low- and middle-income countries (LMICs), particularly in Sub-Saharan Africa. It addresses a research lacuna: the insufficient harmonisation between normative health law and algorithmic public health infrastructures in resource-constrained jurisdictions. The principal objective is to assess how artificial intelligence augments implementation of instruments grounded in IHR 2005 and the WHO FCTC while identifying doctrinal and infrastructural bottlenecks. Using comparative doctrinal analysis and legal-normative mapping, the study triangulates legislative instruments, WHO monitoring frameworks, AI systems including BlueDot, Aarogya Setu and EIOS, and compliance metrics. Preliminary results show that AI has improved early detection, surveillance precision and responsiveness in high-capacity jurisdictions, whereas LMICs face infrastructural deficits, data privacy gaps and fragmented legal scaffolding. The findings highlight the relevance of the EU Artificial Intelligence Act and GDPR as regulatory prototypes for health-oriented algorithmic governance and contrast them with embryonic AI integration and limited internet penetration in many LMICs. The study argues for embedding AI within a rights-compliant, supranationally coordinated regulatory framework to secure equitable health outcomes and stronger compliance. It proposes a model for algorithmic treaty-making inspired by FCTC architecture and calls for WHO-led compliance mechanisms modelled on the WTO Dispute Settlement Body to enhance pandemic preparedness, surveillance equity and transnational governance resilience.

翻译：本研究探讨国际公共卫生工具的法律-技术架构，重点关注其在印度、欧盟、美国以及中低收入国家（特别是撒哈拉以南非洲地区）的实施情况。研究针对一个学术空白：在资源受限的司法管辖区，规范性卫生法与算法化公共卫生基础设施之间的协调不足。主要目标是评估人工智能如何增强基于《国际卫生条例（2005）》和《世界卫生组织烟草控制框架公约》的公共卫生工具的实施，同时识别理论性和基础设施性瓶颈。通过比较性理论分析和法律规范映射，本研究将立法工具、世卫组织监测框架、BlueDot、Aarogya Setu及EIOS等人工智能系统以及合规指标进行三角验证。初步结果表明，在高能力司法管辖区，人工智能提升了早期检测、监测精度和响应能力；而中低收入国家则面临基础设施不足、数据隐私保护缺失和法律框架碎片化等问题。研究结果凸显了欧盟《人工智能法案》和《通用数据保护条例》作为健康导向算法治理监管范本的相关性，并与许多中低收入国家尚处萌芽阶段的人工智能整合及有限的互联网普及率形成对比。研究主张将人工智能嵌入符合权利规范、超国家协调的监管框架，以确保公平的健康成果并强化合规性。研究提出受《烟草控制框架公约》架构启发的算法化条约制定模型，并呼吁建立以世贸组织争端解决机构为范本、由世卫组织主导的合规机制，以增强大流行防范、监测公平性和跨国治理韧性。