Title \u2013 Artificial Intelligence in Healthcare<\/b><\/p>\n
Author –<\/b><\/p>\n
Nikita Vijay Sonawane (50)<\/p>\n
FYMMS 2025-26<\/p>\n
Literature Review –<\/b><\/p>\n
AI-Enabled Customer Experience<\/b><\/p>\n
K., Nayak, Chavadi, and Pai (2025) systematically explores the literature from the last decade (2013-2024) to identify the primary drivers and inhibitors of AI-enabled customer experience in the healthcare industry. K., Nayak, Chavadi, and Pai (2025) shortlist 26 factors that affect patient satisfaction, trust, and service quality, which are broadly categorized into themes such as technology acceptance, AI-powered personalization, risk perception, and psychological factors. In addition, K., Nayak, Chavadi, and Pai (2025) also identify the important inhibitors of patient-centric services, which include algorithmic bias, privacy issues, ethical conflicts, and regulatory hurdles.<\/p>\n
AI Framework from COVID-19 Lessons<\/b><\/p>\n
Balasubramanian et al. (2025) a new, multi-dimensional framework for Artificial Intelligence applications in the healthcare industry, based on the UAE experience during the COVID-19 outbreak. Balasubramanian et al. (2025) divide the environment into technological drivers, such as data types (biological, genomic), processing techniques (deep learning, natural language processing), and applications for different stakeholders, and managerial drivers, which analyze facilitators, performance advantages, and implementation difficulties. Balasubramanian et al. (2025) emphasize that although AI enhances operational speed and public health monitoring, issues like algorithmic bias, data protection, and the absence of standardized datasets need to be overcome to ensure robust healthcare systems.<\/p>\n
Consumer Reactions to GenAI Disclosures<\/b><\/p>\n
Mousavi and Villanova (2025) investigates how consumers assess health care providers based on responses to online reviews generated by Generative AI (GenAI) models such as ChatGPT. The experimental findings indicate that consumers are, on average, unable to distinguish between human-generated and GenAI-generated responses. However, when the response is labeled as being generated by AI, it leads to a substantial decline in consumer satisfaction and their willingness to consider the business in the future. Mousavi and Villanova (2025) also point out the crucial privacy concerns that are exclusive to the healthcare industry, stating that GenAI systems with empathy can, in effect, confirm or disclose patients\u2019 protected health information (PHI), violating HIPAA, and thus emphasizing the need for strict regulatory supervision.<\/p>\n
Editorial on AI in Healthcare Supply Chains<\/b><\/p>\n
Kumar & Goodarzian et al. (2025) the editorial piece for a special issue and presents a comprehensive review of nineteen research articles that explore the application of AI to improve the resilience of healthcare supply chains in disaster situations such as the COVID-19 pandemic. Kumar & Goodarzian et al. (2025) cover a broad range of applications, including predictive capacity planning, risk typologies of medical devices, and optimization of vaccine distribution. Kumar & Goodarzian et al. (2025) conclude by presenting future research avenues, underlining the importance of real-time predictive analytics, decentralized supply chain models, and AI-based decision support systems incorporating sustainable principles.<\/p>\n
Factors Influencing AI Adoption<\/b><\/p>\n
Chalutz-Ben Gal and Margherita (2025) examines 260 research articles from the fields of medicine, computer science, and social sciences to systematically explore the factors that influence technology adoption in the medical field. Chalutz-Ben Gal and Margherita (2025) develop an integrative reference model to classify the factors of AI adoption into three main categories: value assessment, human resource and organizational factors, and policy and health system factors. This model serves as a strategic reference point for healthcare professionals and policymakers to assess their organization’s readiness for successful AI adoption.<\/p>\n
Intellectual Structure of AI in Healthcare<\/b><\/p>\n
Hamsal and Binsar (2025) explores the intellectual structure and current trends of AI in the healthcare industry by analyzing the co-citation and co-word patterns of almost 25,000 publications from 2000 to 2024. Hamsal and Binsar (2025) apply a bibliometric analysis to identify five primary thematic patterns that define the landscape: technology and implementation, health and disease, treatment and care, algorithms and techniques, and social and ethical issues. By pointing out these evolving points of focus, Hamsal and Binsar (2025) provide strategic insights on how to promote balanced AI adoption and address both patient and operational issues worldwide.<\/p>\n
Meta-Analysis of Human-AI Collaboration<\/b><\/p>\n
Ngo (2025) meta-analysis examines the impact of human-AI collaboration in high-stakes decision-making through an aggregation of 146 experiments in the healthcare and public sectors. It reveals the presence of a positive “augmentation effect,” where the use of AI has a positive impact on human performance. However, it also reveals the presence of a negative “synergy effect,” which shows that human-AI collaboration tends to perform poorly compared to the best individual performer, which is often the AI itself in highly structured tasks.<\/p>\n
Mitigating Supply Chain Challenges via Social Media<\/b><\/p>\n
Kumar et al. (2025) work targets disaster-related challenges in the healthcare supply chain by implementing an AI-based multi-step analysis of Twitter data collected in the US and India during the COVID-19 pandemic. By applying Natural Language Processing (NLP) and machine learning algorithms such as K-means clustering for topic modeling and Random Forest classification for crisis classification, Kumar et al. (2025)classify tweets into imperative and non-imperative categories. Moreover, Kumar et al. (2025) effectively apply a Markov chain method to precisely predict the geographical “point-of-crisis” for imperative tweets without geo-location information, thereby proving the potential of social media platforms to guide rescue and relief activities in real-time.<\/p>\n
Multi-Level Supply Chain Collaboration<\/b><\/p>\n
Adhikari et al. (2025) explores collaboration and coordination practices in a three-tier AI-driven healthcare supply chain (consisting of a product producer, a distributor, and a procurement authority) dealing with disaster-related disruptions. Adhikari et al. (2025) use a Stackelberg game-theoretic model to analyse the extent to which supply chain partners coordinate AI innovation activities and pricing agreements in wholesale price contracts and cost-sharing agreements. The findings show that the use of a cost-sharing collaboration tool leads to greater AI innovation activities, retail prices, and total manufacturer profits than in wholesale price contracts alone, thus countering the financial risks involved in the adoption of AI innovation in disaster situations.<\/p>\n
Robust Resource Pre-positioning and Patient Scheduling<\/b><\/p>\n
Liu, Zhang, and Chan (2025) a two-stage distributionally robust optimization (DRO) model to tackle the dual objectives of integrated healthcare resource pre-positioning and dynamic patient scheduling under highly uncertain demand. By modeling the patient allocation problem in the second stage as a Markov Decision Process (MDP), Liu, Zhang, and Chan (2025) employ a Wasserstein distance-based ambiguity set to address the issue of incomplete data distribution information, which is common in disaster scenarios. Liu, Zhang, and Chan (2025) demonstrate that incorporating an AI-powered K-means clustering algorithm for scenario decomposition can significantly lower computational complexity while performing better than conventional sample average approximation (SAA) approaches in minimizing patient waiting penalties.<\/p>\n
Conclusion<\/b><\/p>\n
This reviewed literature concludes that realizing the full potential of AI in healthcare requires much more than technical implementation. It demands strategic financial coordination across supply chains, strictly calibrated human-AI delegation, and transparent yet careful consumer communication. To navigate this technological frontier, policymakers and healthcare leaders must enforce robust governance to protect patient privacy and foster enduring public trust.<\/p>\n
References<\/b><\/p>\n
Adhikari, A., Joshi, R., & Basu, S. (2025). Collaboration and coordination strategies for a multi-level AI-enabled healthcare supply chain under disaster. International Journal of Production Research, 63(2), 497\u2013523.<\/p>\n
Balasubramanian, S., Shukla, V., Islam, N., Upadhyay, A., & Duong, L. (2025). Applying artificial intelligence in healthcare: lessons from the COVID-19 pandemic. International Journal of Production Research, 63(2), 594\u2013627.<\/p>\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. Technology Analysis & Strategic Management, 37(13), 4662\u20134675.<\/p>\n
Hamsal, M., & Binsar, F. (2025). Toward a new era of healthcare services: the role of artificial intelligence in shaping tomorrow’s landscape. Cogent Business & Management, 12(1), 1\u201322.<\/p>\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. Cogent Business & Management, 12(1), 1\u201330.<\/p>\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. International Journal of Production Research, 63(2), 395\u2013403.<\/p>\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. International Journal of Production Research, 63(2), 779\u2013797.<\/p>\n
Liu, Y., Zhang, J., & Chan, F. T. S. (2025). AI-enhanced robust method for integrated healthcare resource pre-positioning and patient scheduling. International Journal of Production Research, 63(2), 729\u2013757.<\/p>\n
Mousavi, J., & Villanova, D. (2025). Consumer Evaluations of Generative Artificial Intelligence Disclosures in Responses to Health Care Reviews. Journal of Public Policy & Marketing, 45(1), 1\u201317.<\/p>\n
Ngo, V. M. (2025). Human\u2013AI collaboration in high-stakes decisions: a meta-analysis of healthcare and public sectors. Applied Economics Letters.<\/p>\n","protected":false},"excerpt":{"rendered":"
Title \u2013 Artificial Intelligence in Healthcare Author – Nikita Vijay Sonawane (50) FYMMS 2025-26 Literature Review – AI-Enabled Customer Experience K., Nayak, Chavadi, and Pai (2025) systematically explores the literature from the last decade (2013-2024) to identify the primary drivers and inhibitors of AI-enabled customer experience in the healthcare industry. K., Nayak, Chavadi, and Pai… 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-24472","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\/24472","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=24472"}],"version-history":[{"count":1,"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=\/wp\/v2\/posts\/24472\/revisions"}],"predecessor-version":[{"id":24473,"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=\/wp\/v2\/posts\/24472\/revisions\/24473"}],"wp:attachment":[{"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=24472"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=24472"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.sachdevajk.in\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=24472"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}