Authors:
1. Dr. Omkar Shinde 2. Sneha Naresh Yada 3. Calvin K. Makok 4. Karandeep Singh Sandh 5. Deeksha Ramesh Banger 6. Saniya Khan 7. Dr. Devraj Bikash Das 8. Shraddha Jain
ABSTRACT
Background: Artificial intelligence (AI)–enabled disease surveillance systems have become increasingly prominent in public health, particularly during recent global health emergencies. While these technologies offer potential benefits for early outbreak detection and response, their widespread deployment raises complex ethical and legal questions that remain insufficiently synthesised.
Objective: This scoping review aimed to map the existing legal and ethical frameworks governing AI-powered disease surveillance systems, identify dominant themes across jurisdictions, and highlight gaps relevant to policy and governance.
Methods: A qualitative scoping review was conducted following the Arksey and O’Malley framework and reported in accordance with the PRISMA-ScR guidelines. Searches were performed across major biomedical and legal databases, supplemented by authoritative policy and regulatory documents. The included sources were synthesised using thematic analysis to capture recurring ethical and legal concerns.
Results: The reviewed literature demonstrated substantial heterogeneity in governance approaches across regions. Privacy and data protection emerged as the most frequently discussed ethical concerns, alongside challenges related to informed consent, accountability, transparency, and equity. Legal frameworks varied widely, with comprehensive data protection regimes in some jurisdictions contrasted by fragmented or outdated regulatory structures elsewhere. Across settings, a consistent gap was observed between high-level ethical principles and their operationalisation in enforceable governance mechanisms.
Conclusions: AI-powered disease surveillance is governed by diverse and evolving legal and ethical frameworks, yet significant governance gaps persist. Addressing these gaps through context-sensitive, enforceable, and equity-oriented regulatory approaches will be essential to ensure responsible and trustworthy use of AI in public health surveillance.
