Journal of Ai ML DL
Journal of Ai ML DL | Online ISSN 3070-2143
0
Citations
24.5k
Views
16
Articles
Volume 2 Number 1 2026
RESEARCH ARTICLE (Open Access)
Perceptions of AI-Enabled Public Health Among Healthcare Professionals: A Cross-Sectional Survey on Telemedicine Access, Disease Surveillance, and Operational Efficiency
Md Shihab Rahman1*, Safiul Islam1, Md Jahidul Islam Ridoy2
Journal of Ai ML DL 2 (1) 1-11 https://doi.org/10.25163/ai.2110797
Submitted: 04 January 2026 Revised: 16 March 2026 Accepted: 23 March 2026 Published: 24 March 2026
Abstract
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
References
Allen, B., Seltzer, S. E., Langlotz, C. P., Dreyer, K. P., Summers, R. M., Petrick, N., Marinac-Dabic, D., Cruz, M., Alkasab, T. K., Hanisch, R. J., Nilsen, W. J., Burleson, J., Lyman, K., & Kandarpa, K. (2019). A road map for translational research on artificial intelligence in medical imaging: From the 2018 National Institutes of Health/RSNA/ACR/The Academy Workshop. Journal of the American College of Radiology, 16(9), 1179–1189. https://doi.org/10.1016/j.jacr.2019.04.014
Alowais, S. A., Alghamdi, S. S., Alsuhebany, N., Alqahtani, T., Alshaya, A. I., Almohareb, S. N., Aldairem, A., Alrashed, M., Saleh, K. B., Badreldin, H. A., Yami, M. S. A., Harbi, S. A., & Albekairy, A. M. (2023). Revolutionizing healthcare: The role of artificial intelligence in clinical practice. BMC Medical Education, 23(1), 689. https://doi.org/10.1186/s12909-023-04698-z
Badidi, E. (2023). Edge AI for early detection of chronic diseases and the spread of infectious diseases: Opportunities, challenges, and future directions. Future Internet, 15(11), 370. https://doi.org/10.3390/fi15110370
Bekbolatova, M., Mayer, J., Ong, C. W., & Toma, M. (2024). Transformative potential of AI in healthcare: Definitions, applications, and navigating the ethical landscape and public perspectives. Healthcare, 12(2), 125. https://doi.org/10.3390/healthcare12020125
Berger, M. L., Lipset, C., Gutteridge, A., Axelsen, K., Subedi, P., & Madigan, D. (2014). Optimizing the leveraging of real-world data to improve the development and use of medicines. Value in Health, 18(1), 127–130. https://doi.org/10.1016/j.jval.2014.10.009
Da Silva, R. G. L. (2024). The advancement of artificial intelligence in biomedical research and health innovation: Challenges and opportunities in emerging economies. Globalization and Health, 20(1), 44. https://doi.org/10.1186/s12992-024-01049-5
Giuffrè, M., & Shung, D. L. (2023). Harnessing the power of synthetic data in healthcare: Innovation, application, and privacy. npj Digital Medicine, 6(1), 186. https://doi.org/10.1038/s41746-023-00927-3
Hirani, R., Noruzi, K., Khuram, H., Hussaini, A. S., Aifuwa, E. I., Ely, K. E., Lewis, J. M., Gabr, A. E., Smiley, A., Tiwari, R. K., & Etienne, M. (2024). Artificial intelligence and healthcare: A journey through history, present innovations, and future possibilities. Life, 14(5), 557. https://doi.org/10.3390/life14050557
Johnson, K. B., Wei, W., Weeraratne, D., Frisse, M. E., Misulis, K., Rhee, K., Zhao, J., & Snowdon, J. L. (2020). Precision medicine, AI, and the future of personalized health care. Clinical and Translational Science, 14(1), 86–93. https://doi.org/10.1111/cts.12884
Jungwirth, D., & Haluza, D. (2023). Artificial intelligence and public health: An exploratory study. International Journal of Environmental Research and Public Health, 20(5), 4541. https://doi.org/10.3390/ijerph20054541
Kong, J. D., Akpudo, U. E., Effoduh, J. O., & Bragazzi, N. L. (2023). Leveraging responsible, explainable, and local artificial intelligence solutions for clinical public health in the global South. Healthcare, 11(4), 457. https://doi.org/10.3390/healthcare11040457
Lee, D., & Yoon, S. N. (2021). Application of artificial intelligence-based technologies in the healthcare industry: Opportunities and challenges. International Journal of Environmental Research and Public Health, 18(1), 271. https://doi.org/10.3390/ijerph18010271
Mandal, A., & Ghosh, A. R. (2023). Role of artificial intelligence (AI) in fish growth and health status monitoring: A review on sustainable aquaculture. Aquaculture International, 32(3), 2791–2820. https://doi.org/10.1007/s10499-023-01297-z
Marques, L., Costa, B., Pereira, M., Silva, A., Santos, J., Saldanha, L., Silva, I., Magalhães, P., Schmidt, S., & Vale, N. (2024). Advancing precision medicine: A review of innovative in silico approaches for drug development, clinical pharmacology and personalized healthcare. Pharmaceutics, 16(3), 332. https://doi.org/10.3390/pharmaceutics16030332
Mhasawade, V., Zhao, Y., & Chunara, R. (2021). Machine learning and algorithmic fairness in public and population health. Nature Machine Intelligence, 3(8), 659–666. https://doi.org/10.1038/s42256-021-00373-4
Morgenstern, J. D., Rosella, L. C., Daley, M. J., Goel, V., Schünemann, H. J., & Piggott, T. (2021). "AI's gonna have an impact on everything in society, so it has to have an impact on public health": A fundamental qualitative descriptive study of the implications of artificial intelligence for public health. BMC Public Health, 21(1), 40. https://doi.org/10.1186/s12889-020-10030-x
Olawade, D. B., David-Olawade, A. C., Wada, O. Z., Asaolu, A. J., Adereni, T., & Ling, J. (2024). Artificial intelligence in healthcare delivery: Prospects and pitfalls. Journal of Medicine, Surgery, and Public Health, 3, 100108. https://doi.org/10.1016/j.glmedi.2024.100108
Prosperi, M., Min, J. S., Bian, J., & Modave, F. (2018). Big data hurdles in precision medicine and precision public health. BMC Medical Informatics and Decision Making, 18(1), 139. https://doi.org/10.1186/s12911-018-0719-2
Rehman, A., Naz, S., & Razzak, I. (2021). Leveraging big data analytics in healthcare enhancement: Trends, challenges and opportunities. Multimedia Systems, 28(4), 1339–1371. https://doi.org/10.1007/s00530-020-00736-8
Shuaib, A. (2024). Transforming healthcare with AI: Promises, pitfalls, and pathways forward. International Journal of General Medicine, 17, 1765–1771. https://doi.org/10.2147/IJGM.S449598
Stasevych, M., & Zvarych, V. (2023). Innovative robotic technologies and artificial intelligence in pharmacy and medicine: Paving the way for the future of health care — A review. Big Data and Cognitive Computing, 7(3), 147. https://doi.org/10.3390/bdcc7030147
Syrowatka, A., Kuznetsova, M., Alsubai, A., Beckman, A. L., Bain, P. A., Craig, K. J. T., Hu, J., Jackson, G. P., Rhee, K., & Bates, D. W. (2021). Leveraging artificial intelligence for pandemic preparedness and response: A scoping review to identify key use cases. npj Digital Medicine, 4(1), 96. https://doi.org/10.1038/s41746-021-00459-8
Varnosfaderani, S. M., & Forouzanfar, M. (2024). The role of AI in hospitals and clinics: Transforming healthcare in the 21st century. Bioengineering, 11(4), 337. https://doi.org/10.3390/bioengineering11040337
Williamson, S. M., & Prybutok, V. (2024). Balancing privacy and progress: A review of privacy challenges, systemic oversight, and patient perceptions in AI-driven healthcare. Applied Sciences, 14(2), 675. https://doi.org/10.3390/app14020675
Recommended articles
Health Risks and Industrial Significance of Filamentous Fungi: Pathogenicity, Mycotoxicity, and Environmental Determinants
Comprehensive Review of Foundation Toxicity Models Integrating In Vivo, In Vitro, and Chemical Knowledge for Unified Risk Prediction
0
Save
Save
0
Citation
Citation
38
View
View
0
Share
Share