USE OF AI IN PUBLIC HEATH SURVEILLANCE: AWARENESS AND FEASIBILITY AMONG HEALTHCARE PROFESSIONALS OF INDIA: A CROSS-SECTIONAL SURVEY - Radhika Sood; Dr. Surbhi Priyadarshi; Dr. Sumit Singh Phukela; Gouri Nagpal (The Bioscan, 2025)
Background: Artificial Intelligence (AI) offers significant potential to enhance public health surveillance through advanced data analytics, real-time monitoring, and predictive modelling. By using vast amounts of health-related data, AI can improve the detection of disease outbreaks and health trends.But, the adoption of AI in public health remains limited, particularly in resource- restrained settings, due to challenges such as low awareness, inadequate infrastructure, data privacy concerns, and lack of policy support. This study aims to assess the awareness and feasibility of AI integration among public health stakeholders for further investments and strategic planning for its implementation. Objective: This study aimed to assess the awareness, attitudes, and perceived feasibility of AI integration in public health surveillance among healthcare professionals and students in India, and to recognise key challenges and training needs associated with its implementation. Methods: A cross-sectional survey was conducted using a structured questionnaire formed using Google forms, which was distributed electronically via WhatsApp , telegram etc. among 400 healthcare professionals and students (Medical courses and paramedical courses ) . The topics of the analysis in the questionnaire were sections regarding demographics, awareness and experience with AI, perceptions of AI in public health, challenges to implementation, and interest in AI-related training. Results: Around. 85% if the respondents were aware of AI in healthcare, and 70% agreed that AI can improve public health surveillance. But, only 22% had practical experience using AI tools. According to respondents the main areas of AI application included diagnostics, treatment planning, and health records management . Major barriers included lack of technical training (30%), concerns about data privacy (25%), and limited funding (20%). Notably, 80% expressed interest in learning AI, preferring flexible options like online courses and workshops. Conclusion: There is strong awareness and positive perception of AI’s potential in public health among healthcare learners and professionals, but practical application remains low. Closing knowledge gaps in training, infrastructure, policy definition, and funding is critical to facilitate effective utilization of AI in public health surveillance systems. These findings offer key recommendations for policymakers and educators to promote AI readiness in the healthcare field. KEYWORDS: Artificial Intelligence, Public Health, Awareness, Feasibility, Health Technology