Journal of Primeasia
Integrative Disciplinary Research | Online ISSN 3064-9870
  1. You are here:
  2. Home
  3. Journals
  4. Biological Sciences
REVIEWS   (Open Access)
Contents Vol 6 (1)

Digital Marketing and Artificial Intelligence in Healthcare: Revolutionizing Patient Engagement and Service Delivery

Md Zillul Karim1*, Reduanul Hasan2, Mohammad Shoeb Abdullah2, Khadiza Tasnim2

+ Author Affiliations

Journal of Primeasia 6 (1) 1-10 https://doi.org/10.25163/primeasia.6110231

Submitted: 25 February 2025 Revised: 18 April 2025  Published: 22 April 2025 

Abstract

The healthcare industry has seen a major transformation in patient engagement and service delivery as a result of the integration of digital marketing and artificial intelligence (AI). Healthcare accessibility and efficiency are increased through individualized experiences provided by AI-driven marketing techniques that improve communication between patients and healthcare providers. Digital marketing optimizes patient encounters, boosts engagement, and offers real-time assistance by utilizing automation, machine learning, and data analytics. Moreover, AI facilitates precision marketing, allowing healthcare organizations to analyze vast amounts of patient data to tailor communication strategies that enhance preventive care and treatment adherence. AI algorithms also support healthcare decision-making by predicting patient behaviors and medical needs, leading to proactive interventions. Healthcare organizations must navigate regulatory frameworks to ensure patient data security and compliance with medical ethics. Additionally, AI models require continuous updates to maintain accuracy and relevance in medical marketing campaigns. Despite these challenges, the potential of AI in healthcare marketing remains unparalleled. The convergence of AI with telemedicine, wearable technology, and electronic health records (EHRs) fosters a holistic patient-centered approach, revolutionizing service delivery. As healthcare providers continue to adopt AI-driven marketing strategies, the industry is poised for a paradigm shift, ensuring enhanced patient engagement, personalized treatment plans, and improved health outcomes. Future research should focus on refining AI models for healthcare marketing, addressing ethical concerns, and maximizing patient trust. The synergy between digital marketing and AI is not just a trend but a transformative force shaping the future of healthcare service delivery.

Keywords: Digital Marketing, Artificial Intelligence, Healthcare, Patient Engagement.

References

Adadi, A., & Berrada, M. (2018). Peeking inside the black-box: A survey of interpretability of machine learning. IEEE Access, 6, 52138–52160. https://doi.org/10.1109/ACCESS.2018.2870052

Agustian, K., Mubarok, E. S., Zen, A., Wiwin, W., & Malik, A. J. (2023). The impact of digital transformation on business models and competitive advantage. Technology and Society Perspectives (TACIT), 1(2), 79–93. https://doi.org/10.61100/tacit.v1i2.55

Björklund, G., Bohlin, M., Olander, E., & Au-Yong Oliveira, M. (2022). An Exploratory Study on the Spotify Recommender System. In Information Systems and Technologies. https://doi.org/10.1007/978-3-031-04819-7_36

Bose, R., Ghosh, A., Sarkar, S., & Ghosh, A. (2019). Leveraging artificial intelligence in marketing: A new frontier in customer experience. Journal of Business Research, 100, 556–570. https://doi.org/10.1016/j.jbusres.2018.08.013

Boudet, J., Gregg, B., Rathje, K., Stein, E., & Vollhardt, K. (2019). The future of personalization—and how to get ready for it. McKinsey & Company. https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/the-future-of-personalization-and-how-to-get-ready-for-it

Chakrabarti, A., & Singh, V. (2023). Design in the Era of Industry 4.0, Volume 1. In Smart innovation, systems and technologies. https://doi.org/10.1007/978-981-99-0293-4

Chen, M., Hao, Y., Cai, Y., Wang, Y., & Zhang, Y. (2020). A novel personalized recommendation system based on deep learning and big data. IEEE Transactions on Industrial Informatics, 16(12), 7850–7859. https://doi.org/10.1109/TII.2019.2953815

Choi, E., Schuetz, A., Stewart, W. F., & Miotto, R. (2016). Using recurrent neural networks for early detection of heart failure onset. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 361–370). https://doi.org/10.1145/2939672.2939754

Chung, W., Kim, S., Park, Y., & Kim, D. (2020). A machine learning-based approach to predicting healthcare resource needs. Healthcare Informatics Research, 26(1), 35–43. https://doi.org/10.4258/hir.2020.26.1.35

Çobanogullari, F. (2024). Learning and Teaching with ChatGPT: Potentials and Applications in Foreign Language Education. https://eric.ed.gov/?id=EJ1452159

Davenport, T., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2), 94–98. https://doi.org/10.7861/futurehosp.6-2-94

Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2019). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24–42. https://doi.org/10.1007/s11747-019-00696-0

Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115–118. https://doi.org/10.1038/nature21056

Evangelista, P. N. (2019). Artificial intelligence in fashion: how consumers and the fashion system are being impacted by AI-powered technologies.

Friedman, R., & McCall, K. (2020). Artificial intelligence and machine learning in drug discovery. Nature Reviews Drug Discovery, 19(6), 379–380. https://doi.org/10.1038/d41573-020-00058-1

Ghani, R., Wang, J., & Liu, X. (2019). Predicting healthcare outcomes using machine learning: Applications in personalized medicine. Journal of Biomedical Informatics, 98, 103266. https://doi.org/10.1016/j.jbi.2019.103266

Goel, P. (2012). Assessment of HR development framework. International Research Journal of Management Sociology & Humanities, 3(1), Article A1014348. https://doi.org/10.32804/irjmsh

Goel, P., & Singh, S. P. (2010). Method and process to motivate the employee at performance appraisal system. International Journal of Computer Science & Communication, 1(2), 127–130.

Harshitha, G., Kumar, S., Rani, S., & Jain, A. (2021, November). Cotton disease detection based on deep learning techniques. In 4th Smart Cities Symposium (SCS 2021) (Vol. 2021, pp. 496–501). IET. https://doi.org/10.1049/icp.2021.1934

Interactions. (2022). Pinterest Powers Personalization with AI [Podcast]. https://www.interactions.com/podcasts/pinterest-powers-personalization-with-ai/

Jain, A., Dwivedi, R., Kumar, A., & Sharma, S. (2017). Scalable design and synthesis of 3D mesh network on chip. In Proceedings of International Conference on Intelligent Communication, Control and Devices: ICICCD 2016 (pp. 661–666). Springer Singapore. https://doi.org/10.1007/978-981-10-1708-7_80

Javed, U., Shaukat, K., Hameed, A. I., Iqbal, F., Mahboob Alam, T., & Luo, S. (2021). A review of content-based and context-based recommendation systems. International Journal of Emerging Technologies in Learning (iJET), 16(3), 274–306. https://doi.org/10.3991/ijet.v16i03.18851

Kaya, Ö., & Aytaç, S. (2024). Fashion sector and artificial intelligence applications. Cankiri Karatekin Universitesi Iktisadi Ve Idari Bilimler Fakultesi Dergisi. https://doi.org/10.18074/ckuiibfd.1520744

Krittanawong, C., Rogers, A. J., & Khin, H. (2020). Artificial intelligence and machine learning in cardiovascular medicine. Journal of the American College of Cardiology, 76(16), 2009–2025. https://doi.org/10.1016/j.jacc.2020.08.064

Kumar, A., & Jain, A. (2021). Image smog restoration using oblique gradient profile prior and energy minimization. Frontiers of Computer Science, 15(6), 156706. https://doi.org/10.1007/s11704-020-9043-9

Kumar, S., Jain, A., Rani, S., Ghai, D., Achampeta, S., & Raja, P. (2021, December). Enhanced SBIR based re-ranking and relevance feedback. In 2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART) (pp. 7–12). IEEE. https://doi.org/10.1109/SMART52563.2021.9667152

Latif, A., Rasheed, A., Sajid, U., Ahmed, J., Ali, N., Ratyal, N. I., Zafar, B., Dar, S. H., Sajid, M., & Khalil, T. (2019). Content-Based Image Retrieval and Feature Extraction: A Comprehensive Review. Mathematical Problems in Engineering. https://doi.org/10.1155/2019/9658350

Liao, K. P., & Cai, T. (2018). Comparative effectiveness of different machine learning algorithms for predicting drug response. Journal of Biomedical Informatics, 81, 53–60. https://doi.org/10.1016/j.jbi.2018.03.003

Long, J., & Cheng, J. (2017). Machine learning for healthcare applications. IEEE Transactions on Biomedical Engineering, 64(6), 1366–1373. https://doi.org/10.1109/TBME.2016.2585908

Marku, E. (2023, October). AI-Artificial Intelligence and the Growth of the Creative Potential of Designers in the Fashion Industry. In Forum A+ P (Vol. 27).

Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the future — Big data, machine learning, and clinical medicine. New England Journal of Medicine, 375(13), 1216–1219. https://doi.org/10.1056/NEJMp1606181

Parveen, R., & Varma, N. S. (2021). Friend’s recommendation on social media using different algorithms of machine learning. Global Transitions Proceedings, 2(2), 273–281. https://doi.org/10.1016/j.gltp.2021.08.012

Pires, P. B., & Santos, J. D. (2023). Artificial Intelligence and marketing. In Advances in marketing, customer relationship management, and e-services book series (pp. 95–118). https://doi.org/10.4018/978-1-6684-8958-1.ch005

Potwora, M., Vdovichena, O., Semchuk, D., Lipych, L., & Saienko, V. (2024). The use of artificial intelligence in marketing strategies: Automation, personalization and forecasting. Deleted Journal, 2024(2), 41–49. https://doi.org/10.53935/jomw.v2024i2.275

Rahman, M. H., Aunni, S. A. A., Ahmed, B., Rahman, M. M., Shabuj, M. M. H., Das, D. C., Akter, M. S., Numan, A. A. (2024). "Artificial intelligence for Improved Diagnosis and Treatment of Bacterial Infections", Microbial Bioactives, 7(1),1-18,10036. https://doi.org/10.25163/microbbioacts.7110036

Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347–1358. https://doi.org/10.1056/NEJMra1814259

Singh, S. P., & Goel, P. (2009). Method and process labor resource management system. International Journal of Information Technology, 2(2), 506–512.

Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56. https://doi.org/10.1038/s41591-018-0300-7

Tufael, A. R. S., Hasan, M. M., Hossain, M. T., Satter, M. A., & Rahman, M. M. (2023). Impact and challenges of digital marketing in health care during the COVID-19 pandemic. Journal of Primeasia, 4(1), 1–4. https://doi.org/9756

Wang, F., & Preininger, A. (2019). Artificial intelligence in healthcare: A review. Journal of Healthcare Engineering, 2019, 1–12. https://doi.org/10.1155/2019/1107986

PDF
Abstract
Export Citation

View Dimensions


View Plumx


View Altmetric



0
Save
0
Citation
65
View
3
Share