{"id":24164,"date":"2026-02-25T10:54:46","date_gmt":"2026-02-25T05:24:46","guid":{"rendered":"http:\/\/www.sachdevajk.in\/?p=24164"},"modified":"2026-02-25T10:54:46","modified_gmt":"2026-02-25T05:24:46","slug":"artificial-intelligence-in-healthcare-2","status":"publish","type":"post","link":"http:\/\/www.sachdevajk.in\/?p=24164","title":{"rendered":"Artificial Intelligence in Healthcare"},"content":{"rendered":"

Title \u2013 Artificial Intelligence in Healthcare<\/span><\/p>\n

Author \u2013 Nikita Vijay Sonawane<\/span>(50)<\/span><\/p>\n

FYMMS 2025-26 <\/span><\/p>\n

Literature Review –<\/span><\/p>\n

1. AI Applications Framework in UAE Healthcare<\/span>\u00a0<\/span><\/p>\n

This article proposes a multi-dimensional framework for artificial intelligence applications in the healthcare sector, evaluating its effectiveness in the United Arab Emirates (UAE) during the COVID-19 pandemic. It outlines computational techniques, data requirements, and managerial factors such as stakeholder collaboration, technology infrastructure, and regulatory environments necessary to accelerate AI adoption and build resilient healthcare systems that can withstand future crises.<\/span><\/p>\n

2. Collaboration in Multi-Level AI Healthcare Supply Chains<\/span>\u00a0<\/span><\/p>\n

This article models collaboration and coordination strategies within a three-level AI-enabled healthcare supply chain (comprising a manufacturer, distributor, and procurement agency) in disaster settings. By analysing wholesale price and cost-sharing contracts using a Stackelberg game-theoretic approach, the authors conclude that cost-sharing models result in higher AI innovation efforts, retail prices, and profitability compared to standard wholesale price contracts.<\/span><\/p>\n

3. Consumer Reactions to GenAI Disclosures in Healthcare<\/span>\u00a0<\/span><\/p>\n

This study investigates how patients evaluate online healthcare reviews when the responses are generated by generative AI (GenAI) models like ChatGPT. The authors found that while consumers generally cannot distinguish between human-written and AI-written responses, explicitly disclosing AI involvement significantly decreases consumer satisfaction and their likelihood of utilizing the healthcare provider. The research stresses the importance of ethical guidelines, data privacy, and transparency regarding AI usage in customer care.<\/span><\/p>\n

4. Determinants of AI-Enabled Customer Experience<\/span><\/p>\n

Based on a systematic review spanning from 2013 to 2024, this article identifies 26 determinants that influence the AI-driven customer experience in healthcare. It highlights key driving factors such as service quality, trust, emotional elements, and technology acceptance, while also pointing out barriers such as algorithmic bias, ethical dilemmas, and privacy concerns that hinder successful AI integration in patient-centered services.<\/span><\/p>\n

5. Editorial on AI in Healthcare Supply Chains<\/span>\u00a0<\/span><\/p>\n

This editorial introduces a special issue focused on the role of AI in improving healthcare supply chain resilience during disaster conditions, such as the COVID-19 pandemic. It summarizes nineteen selected papers covering diverse topics like medical device risk typologies, vaccine distribution networks, predictive capacity planning, and blood donation management, and concludes with directions for future research involving real-time predictive analytics and decentralized framework development.<\/span><\/p>\n

6. Factors Influencing AI Adoption in Healthcare<\/span>\u00a0<\/span><\/p>\n

This study systematically reviews 260 research articles to extract and synthesize factors driving AI adoption in healthcare settings. The authors construct an integrative reference model composed of three main areas: value assessment, human resource and organizational dimensions, and policy\/health system factors, designed to help healthcare organizations evaluate their preparedness for successful AI implementation.<\/span><\/p>\n

7. Intellectual Structure of AI in Healthcare<\/span>\u00a0<\/span><\/p>\n

Through co-citation and co-word analysis of literature published from 2000 to 2024, this paper explores the evolving landscape and intellectual structure of AI in healthcare. It identifies five main thematic clusters: technology implementation, health and disease, treatment and care, algorithms, and social\/ethical aspects which serve to guide practitioners, researchers, and policymakers in equitably integrating AI into global healthcare systems.<\/span><\/p>\n

8. Meta-Analysis of Human-AI Collaboration<\/span>\u00a0<\/span><\/p>\n

Analysing 146 experiments in the healthcare and public sectors, this meta-analysis evaluates the effectiveness of human-AI collaboration. The findings suggest that while AI augmentation reliably improves human performance, true “synergy” effects are often negative because AI alone frequently outperforms human-AI teams in highly structured tasks. The paper recommends selective automation for precision tasks combined with human oversight for highly ambiguous or ethical decisions.<\/span><\/p>\n

9. Robust AI Pre-positioning and Patient Scheduling<\/span>\u00a0<\/span><\/p>\n

This paper addresses the dual challenges of healthcare resource pre-positioning and dynamic patient scheduling during disasters under conditions of stochastic demand. The authors propose a two-stage distributionally robust optimization (DRO) model aided by an AI-based K-means clustering approach to improve computational efficiency. Numerical results demonstrate that this method significantly minimizes patient waiting penalty costs and handles data scarcity better than traditional sample average approximation methods.<\/span><\/p>\n

10. Social Media Analysis for Healthcare Supply Chains<\/span>\u00a0<\/span><\/p>\n

Focusing on the COVID-19 pandemic, this study applies a multi-step AI approach using natural language processing and machine learning to Twitter data to extract real-time healthcare supply chain issues. By analyzing public sentiment and classifying tweets into imperative versus non-imperative needs, the model helps authorities identify precise supply shortages (e.g., oxygen, PPE, beds) and predicts geographical crisis points with high accuracy to assist in disaster response efforts.<\/span><\/p>\n

Conclusion<\/span><\/p>\n

The integration of Artificial Intelligence into healthcare represents a shift toward a more resilient, data-driven, and complex ecosystem that balances technological efficiency with human-centric challenges. While AI-driven frameworks and game-theoretic models significantly enhance supply chain coordination and resource pre-positioning particularly during crises like COVID-19 the transition is hindered by a “transparency paradox” where patients value the quality of GenAI responses but report lower satisfaction upon disclosure. Furthermore, while meta-analyses prove that AI excels at structured, high-precision tasks, true human-AI synergy remains elusive, necessitating a selective automation strategy that preserves human oversight for ethical and ambiguous decisions. Ultimately, the successful global adoption of AI in healthcare depends on addressing twenty-six critical determinants ranging from service quality and trust to algorithmic bias and regulatory alignment to ensure that technological innovation translates into equitable, transparent, and sustainable patient-centered care.<\/span><\/p>\n

References<\/span><\/p>\n

    \n
  1. \n

    Adhikari, A., Joshi, R., & Basu, S. (2025). Collaboration and coordination strategies for a multi-level AI-enabled healthcare supply chain under disaster. <\/span>International Journal of Production Research<\/span>, 63(2), 497\u2013523.<\/span><\/p>\n<\/li>\n

  2. \n

    Balasubramanian, S., Shukla, V., Islam, N., Upadhyay, A., & Duong, L. (2025). Applying artificial intelligence in healthcare: lessons from the COVID-19 pandemic. <\/span>International Journal of Production Research<\/span>, 63(2), 594\u2013627.<\/span><\/p>\n<\/li>\n

  3. \n

    Chalutz-Ben Gal, H., & Margherita, A. (2025). The adoption of Artificial Intelligence (AI) in healthcare: a model of value assessment, human resource and health system factors. <\/span>Technology Analysis & Strategic Management<\/span>, 37(13), 4662\u20134675.<\/span><\/p>\n<\/li>\n

  4. \n

    Hamsal, M., & Binsar, F. (2025). Toward a new era of healthcare services: the role of artificial intelligence in shaping tomorrow’s landscape. <\/span>Cogent Business & Management<\/span>, 12(1), 1\u201322.<\/span><\/p>\n<\/li>\n

  5. \n

    K., P., Nayak, S., Chavadi, C. A., & Pai, Y. P. (2025). Determinants of AI-enabled customer experience across healthcare sectors: a decade in review. <\/span>Cogent Business & Management<\/span>, 12(1), 1\u201330.<\/span><\/p>\n<\/li>\n

  6. \n

    Kumar, V., Goodarzian, F., Ghasemi, P., Chan, F. T. S., & Gupta, N. (2025). Artificial intelligence applications in healthcare supply chain networks under disaster conditions. <\/span>International Journal of Production Research<\/span>, 63(2), 395\u2013403.<\/span><\/p>\n<\/li>\n

  7. \n

    Kumar, V. V., Sahoo, A., Balasubramanian, S. K., & Gholston, S. (2025). Mitigating healthcare supply chain challenges under disaster conditions: a holistic AI-based analysis of social media data. <\/span>International Journal of Production Research<\/span>, 63(2), 779\u2013797.<\/span><\/p>\n<\/li>\n

  8. \n

    Liu, Y., Zhang, J., & Chan, F. T. S. (2025). AI-enhanced robust method for integrated healthcare resource pre-positioning and patient scheduling. <\/span>International Journal of Production Research<\/span>, 63(2), 729\u2013757.<\/span><\/p>\n<\/li>\n

  9. \n

    Mousavi, J., & Villanova, D. (2025). Consumer Evaluations of Generative Artificial Intelligence Disclosures in Responses to Health Care Reviews. <\/span>Journal of Public Policy & Marketing<\/span>, 45(1), 1\u201317.<\/span><\/p>\n<\/li>\n

  10. \n

    Ngo, V. M. (2025). Human\u2013AI collaboration in high-stakes decisions: a meta-analysis of healthcare and public sectors. <\/span>Applied Economics Letters<\/span>.<\/span><\/span><\/p>\n<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

    Title \u2013 Artificial Intelligence in Healthcare Author \u2013 Nikita Vijay Sonawane(50) FYMMS 2025-26 Literature Review – 1. AI Applications Framework in UAE Healthcare\u00a0 This article proposes a multi-dimensional framework for artificial intelligence applications in the healthcare sector, evaluating its effectiveness in the United Arab Emirates (UAE) during the COVID-19 pandemic. It outlines computational techniques, data… Continue reading Artificial Intelligence in Healthcare<\/span><\/a><\/p>\n","protected":false},"author":140110,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-24164","post","type-post","status-publish","format-standard","hentry","category-uncategorized","entry"],"_links":{"self":[{"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=\/wp\/v2\/posts\/24164","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=\/wp\/v2\/users\/140110"}],"replies":[{"embeddable":true,"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=24164"}],"version-history":[{"count":1,"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=\/wp\/v2\/posts\/24164\/revisions"}],"predecessor-version":[{"id":24165,"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=\/wp\/v2\/posts\/24164\/revisions\/24165"}],"wp:attachment":[{"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=24164"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=24164"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=24164"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}