1Professor, Electronics and Communication Engineering, LBS Institute of Technology for Women, Poojappura. Email: resmi@lbsitw.ac.in; ORCID: 0000-0002-0849-5509
2Assistant Professor, Department of Artificial Intelligence and Machine Learning, Nitte Meenakshi Institute of Technology, NITTE (Deemed to be University), Bangalore, Karnataka, India 560064. Email: kruthika.cg@nmit.ac.in
3Assistant Professor, MBA, Velammal Engineering College. ORCID: 0009-0005-7355-982X
4Head & Assistant Professor, BBA, Anna Adarsh College for Women. Email: gayathria@annaadarsh.edu.in
5Associate Professor, Mechanical Engineering, LBS Institute of Technology for Women, Poojappura. Email: sajanjerome@lbsitw.ac.in; ORCID: 0000-0003-4231-5265
6Professor, Mechanical Engineering, LBS Institute of Technology for Women. Email: anilkumar@lbsitw.ac.in; ORCID: 0000-0001-5185-9489
There is a growing use of artificial intelligence in healthcare marketing in the form of chatbots, predictive outreach, recommender systems, segmentation engines, digital front-door workflows, and generative content systems. Although such technologies are meant to enhance personalisation and efficiency in operations, healthcare is not like an average market of consumers due to its vulnerable users, highly sensitive information, and the decision-making process, which can affect access, trust, and behaviour related to health. The present paper will carry out a research study of the ethical AI implementation in healthcare marketing through a management lens, based on secondary data. The discussion uses scholarly works published by peers, policy documents, and regulatory guidelines, and enforcement practices of healthcare organisations to explore the ways in which AI can be used by healthcare organisations in a way that is patient-centric, ethically justifiable, and operationally feasible. The literature review incorporates the literature on patient engagement, interaction via omnichannels, AI ethics, privacy governance, fairness, transparency, and interactions in digital health. The results indicate that predictive models are not always the root cause of the most severe ethical breaches in healthcare marketing, but the entire marketing-technology stack: web trackers, third-party advertising techniques, SDKs, and vendor ecosystems. The conceptual model proposed in the paper suggests a management-based connection between the computer-science capabilities and the marketing capabilities to the patient-centric strategies, mediated by the lifecycle governance principles which are accountability, fair evaluation of consent, transparency, and risk-monitoring. Findings indicate that organisations ought to view ethical AI as an organisational capability, other than a compliance exercise. The factors that are needed to maintain trust and facilitate responsible innovation are strong governance, privacy-safe measurement, well-design consent, human-based oversight, and vendor accountability. The paper ends with practical recommendations to managers and finding limitations and directions of future research.
Keywords: Ethical AI; healthcare marketing; patient-centricity; responsible AI; privacy governance; algorithmic fairness; explainability; digital health; generative AI; healthcare management.
How to cite this article: Resmi R, Kruthika CG, Nafeza E, Gayathri A, Jerome S, Anilkumar EN. Ethical AI Deployment in Healthcare Marketing: A Management Perspective on Computer Science-Enabled Patient-Centric Strategies. Int J Drug Deliv Technol. 2026;16(11s): 297-305; DOI: 10.25258/ijddt.16.11s.28.
Source of support: Nil.
Conflict of interest: Nil