Perceptions of AI-Enabled Public Health Among Healthcare Professionals: A Cross-Sectional Survey on Telemedicine Access, Disease Surveillance, and Operational Efficiency

Background: Artificial intelligence (AI) is reshaping how healthcare systems function — from how patients access services to how diseases are tracked and how hospitals manage their day-to-day operations. Yet despite growing enthusiasm, empirical evidence on how frontline professionals actually perceive these changes remains limited, particularly in settings where digital health infrastructure is still maturing.Methods: This cross-sectional survey enrolled 185 participants drawn purposively from healthcare, research, hospital administration, and technology sectors. A structured, pilot-tested questionnaire captured perceptions across four domains: healthcare accessibility, disease prediction efficiency, operational efficiency, and future AI priorities. Descriptive statistics summarized response distributions, and Pearson correlation analysis examined inter-domain relationships.Results: Telemedicine-based rural access garnered the strongest agreement (81.4%), followed by general healthcare accessibility improvement (78.2%). Epidemiological monitoring (73.0%) and outbreak prediction (71.9%) were the most endorsed disease surveillance functions. Operationally, reducing administrative burden was cited most frequently (17.3%). Correlation analysis revealed strong positive associations between healthcare accessibility and operational efficiency (r = 0.724), and between disease prediction efficiency and operational efficiency (r = 0.695). Data privacy (76.2%), ethical governance (76.8%), and the need for digital infrastructure investment (78.4%) emerged as the leading concerns and future priorities.Conclusion: Healthcare professionals broadly view AI as a meaningful tool for extending service reach, improving surveillance capacity, and streamlining clinical workflows. However, concerns around data governance, infrastructure gaps, and training deficits suggest that optimism alone will not drive adoption — structured investment and policy attention are equally essential.Keywords: Artificial intelligence, public health, healthcare accessibility, disease prediction, operational efficiency