Home / Current Issue / Paper 1712903
Ethical Considerations in AI-Enabled Big Data Predictive Healthcare Analytics and Marketing Innovation
Subject area: Science,Engineering and Technology · Area of research: Data Science
Abstract
Artificial Intelligence (AI) and Big Data are reshaping the healthcare ecosystem by enabling unprecedented advancements in predictive analytics, diagnostics, treatment personalization, operational efficiency, and patient engagement. This research examines the transformative potential of AI innovations across clinical, administrative, and marketing domains. It explores current applications such as AI-powered medical imaging for early disease detection, machine learning?driven personalized treatment planning, natural language processing (NLP) for automated clinical documentation, and predictive modeling for disease outbreaks and hospital resource optimization. Emerging trends?including AI-driven drug discovery, robotic-assisted surgeries, virtual assistants, telemedicine enhancement, and remote patient monitoring?further illustrate AI?s capacity to improve patient outcomes and system-wide efficiency. However, as AI increasingly influences patient decision-making, healthcare marketing strategies, and consumer interactions, ethical considerations become paramount. The study evaluates critical issues such as data privacy, algorithmic bias, fairness, transparency, and compliance with regulatory frameworks like HIPAA and GDPR. It also investigates the ethical implications of using patient data for targeted healthcare marketing, the responsibilities associated with AI-enabled outreach, and the impact of marketing ethics on patient trust and acceptance of predictive analytics technologies. Ultimately, this research provides a comprehensive and integrative perspective on how AI and Big Data can responsibly transform healthcare delivery and marketing practices. It underscores the need for robust ethical frameworks, responsible AI governance, and human?AI collaboration to ensure that predictive healthcare analytics remain accurate, equitable, and aligned with patient-centric values.
Keywords
Healthcare, AI in healthcare, Artificial Intelligence, Machine learning, Social Media Marketing, Marketing ethics, Big Data analytics, Predictive Healthcare, Natural Language Processing, Disease Prediction, Healthcare innovation.
References
[1] Yu, K.-H., Beam, A. L. & Kohane, I. S. Artificial intelligence in healthcare. Nat. Biomed. Eng. 2, 719–731 (2018).
[2] Bertsimas, D., Bjarnadótir, M. V., Kane, M. A., Kryder, J. C., Pandey, R., Vempala, S., Wang, G.: Algorithmic Prediction of Health-Care Costs, Operayions research, vol. 56, no. 6, 6-18, (2008)
[3] Sethi, Suresh P., Houmin Yan, and Hanqin Zhang. Inventory and supply chain management with forecast updates. 2006.
[4] Bandyopadhyay, Paulami. (2024). Optimization of Class Scheduling Problem: AMulti- Constraint Approach for Effective Resource Allocation and Space Utilization. International Journal of Computer Trends and Technology. 72. 36-42. 10.14445/22312803/IJCTT- V72I10P107.
[5] Bandyopadhyay, Paulami. (2024). Scaling Data Engineering with Advanced Data Management Architecture:AComparativeAnalysis of Traditional ETLToolsAgainst the Latest Unified Platform. International Journal of Computer Trends and Technology. 72. 22-30. 10.14445/22312803/IJCTT-V72I10P105.
[6] Bandyopadhyay, Paulami. "Toward Smarter Healthcare: Machine Learning-Driven Analysis of Electronic Health Records (EHR's)." Available at SSRN 5241384 (2025).
[7] Bandyopadhyay, Paulami. "Leveraging machine learning and AI in healthcare: Aparadigm shift from the traditional approaches." (2023).
How to cite this paper
@article{1712903,
author = {Paulami Bandyopadhyay},
title = {Ethical Considerations in AI-Enabled Big Data Predictive Healthcare Analytics and Marketing Innovation},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {9},
number = {6},
pages = {1382-1388},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1712903.pdf},
abstract = {Artificial Intelligence (AI) and Big Data are reshaping the healthcare ecosystem by enabling unprecedented advancements in predictive analytics, diagnostics, treatment personalization, operational efficiency, and patient engagement. This research examines the transformative potential of AI innovations across clinical, administrative, and marketing domains. It explores current applications such as AI-powered medical imaging for early disease detection, machine learning?driven personalized treatment planning, natural language processing (NLP) for automated clinical documentation, and predictive modeling for disease outbreaks and hospital resource optimization. Emerging trends?including AI-driven drug discovery, robotic-assisted surgeries, virtual assistants, telemedicine enhancement, and remote patient monitoring?further illustrate AI?s capacity to improve patient outcomes and system-wide efficiency. However, as AI increasingly influences patient decision-making, healthcare marketing strategies, and consumer interactions, ethical considerations become paramount. The study evaluates critical issues such as data privacy, algorithmic bias, fairness, transparency, and compliance with regulatory frameworks like HIPAA and GDPR. It also investigates the ethical implications of using patient data for targeted healthcare marketing, the responsibilities associated with AI-enabled outreach, and the impact of marketing ethics on patient trust and acceptance of predictive analytics technologies. Ultimately, this research provides a comprehensive and integrative perspective on how AI and Big Data can responsibly transform healthcare delivery and marketing practices. It underscores the need for robust ethical frameworks, responsible AI governance, and human?AI collaboration to ensure that predictive healthcare analytics remain accurate, equitable, and aligned with patient-centric values.},
keywords = {Healthcare, AI in healthcare, Artificial Intelligence, Machine learning, Social Media Marketing, Marketing ethics, Big Data analytics, Predictive Healthcare, Natural Language Processing, Disease Prediction, Healthcare innovation.},
month = {December},
doi = {https://doi.org/10.64388/IREV9I6-1712903}
}