Authors : Manideep Dhar; Ritwik Singh; Sharat Chandra Kumar Manikonda

Volume/Issue : Volume 11 - 2026, Issue 5 - May

Google Scholar : https://tinyurl.com/mt4pd3fe

Scribd : https://tinyurl.com/yc5kxt9a

DOI : https://doi.org/10.38124/ijisrt/26May1651

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Hospitals are rapidly adopting artificial intelligence for triage, imaging, scheduling etc., yet most deployments remain isolated point solutions locked inside departmental silos, resulting in duplicated effort, hidden risks, and unrealised enterprise value. Despite explosive growth of AI in healthcare market and accelerating investment, an estimated 70–80% of healthcare AI pilots fail to scale, largely due to governance gaps, fragmented data, and missing integration blueprints. This research proposes a hospital-specific, compliance-first, Agentic AI architecture with multiple interoperable layers, extending existing hospital AI platform models with: (i) an Agent Orchestration Layer for multi-agent workflows across clinical, operational, and financial domains, (ii) a Compliance and Policy Layer that centralises policy-as-code for HIPAA, GDPR, the EU AI Act, DISHA, India’s DPDP Act, and ISO/IEC security and safety standards, and (iii) a Privacy-Preserving Data Fabric that plugs federated learning, differential privacy, and secure enclaves into real-world Hospital Information Management System (HIMS) flows. Using a synthetic but structurally realistic hospital dataset and an open, ready-to-deploy prototype implementation, this study demonstrates the end-to-end orchestration of triage risk prediction, workflow optimisation, and compliance logging, achieving substantial simulated reductions in task turnaround times and manual documentation effort while maintaining policy-guarded data access. The resulting architecture offers hospital leaders a pragmatic blueprint to move from ad hoc tools to a governed, globally compliant, ROI-focused AI platform that can be tailored to on-premise, hybrid, and cloud-native deployments.

Keywords : Agentic AI in Healthcare, Multi-Agent LLM Orchestration, Hospital Information Management System (HIMS), DISHA Compliance, DPDP Act, Policy-As-Code, AI Governance in Hospitals, Clinical Workflow Automation, Federated Learning in Smart Healthcare, Data Residency in Healthcare, AI-Driven Hospital Operations.

References :

1. Y. Shokrollahi, S. Yarmohammadtoosky, M. M. Nikahd, P. Dong, X. Li, and L. Gu, (2023). A comprehensive review of generative AI in healthcare. arXiv. https://arxiv.org/abs/2310.00795
2. Y. L. E. Thompson, G. M. Levine, W. Chen, B. Sahiner, Q. Li, N. Petrick, et al., “Applying queueing theory to evaluate wait-time-savings of triage algorithms,” Queueing Syst., vol. 108, no. 3-4, pp. 579–610, 2024., doi:10.1007/s11134-024-09927-w
3. D. R. T. Knight, C. A. Aakre, C. V. Anstine, B. Munipalli, P. Biazar, G. Mitri, et al., “Artificial intelligence for patient scheduling in the real-world health care setting: A metanarrative review,” Health Policy Technol., vol. 12, no. 4, p. 100824, 2023.,  doi: 10.1016/j.hlpt.2023.100824
4. A. Tang, R. Tam, A. Cadrin-Chênevert, W. Guest, J. Chong, J. Barfett, et al., Applications of artificial intelligence in the radiology workflow: Process streamlining and optimization. Radiographics / Radiology. ScienceDirect, 2023., doi:10.1053/j.ro.2023.02.003
5. World Health Organization, (2020). Global strategy on human resources for health: Workforce 2030. https://www.who.int/publications/i/item/9789241511131
6. T. D. Shanafelt, L. N. Dyrbye, C. Sinsky, O. Hasan, D. Satele, J. Sloan, et al., “Relationship between clerical burden and characteristics of the electronic environment with physician burnout and professional satisfaction,” Mayo Clin. Proc., vol. 91, no. 7, pp. 836–848, Jul. 2016., doi:10.1016/j.mayocp.2016.05.007
7. McKinsey & Company, (2022). The future of US healthcare: What’s next for the industry post-COVID-19. https://www.mckinsey.com/industries/healthcare  
8. Deloitte, (2023). 2023 Global health care outlook. https://www2.deloitte.com
9.  E. J. Topol, “High-performance medicine: The convergence of human and artificial intelligence,” Nat. Med., vol. 25, no. 1, pp. 44–56, Jan. 2019., doi:10.1038/s41591-018-0300-7
10. D. Kreuzberger, N. Kühl, and S. Hirschl, “Machine learning operations (MLOps): Overview, definition, and architecture,” IEEE Access, vol. 11, pp. 31866–31879, 2023., doi:10.1109/ACCESS.2023.3262138
11. Health Level Seven International, (2019). FHIR Release 4 (R4). https://www.hl7.org/fhir/
12. H. K. Huang, PACS and imaging informatics: Basic principles and applications, 2nd ed., Wiley-Blackwell, 2010, https://openlibrary.org/works/OL1901151W/PACS_and_imaging_informatics
13. World Health Organization, (2019). WHO guideline: Recommendations on digital interventions for health system strengthening. https://www.who.int/publications/i/item/9789241550505  
14. McKinsey & Company, (2021). The future of healthcare: Finding the opportunities that lie beneath the uncertainty. https://www.mckinsey.com/industries/healthcare
15.  J. He, S. L. Baxter, J. Xu, J. Xu, X. Zhou, and K. Zhang, “The practical implementation of artificial intelligence technologies in medicine,” Nat. Med., vol. 25, no. 1, pp. 30–36, Jan. 2019., doi:10.1038/s41591-018-0307-0
16.  A. L. Kellermann and S. S. Jones, “What it will take to achieve the as-yet-unfulfilled promises of health information technology,” Health Aff. (Millwood), vol. 32, no. 1, pp. 63–68, Jan. 2013., doi:10.1377/hlthaff.2012.0693
17. J. Adler-Milstein, A. J. Holmgren, P. Kralovec, C. Worzala, T. Searcy, and V. Patel, “Electronic health record adoption and hospital performance: Time-related effects,” Health Aff. (Millwood), vol. 36, no. 8, pp. 1417–1425, 2017., doi:10.1377/hlthaff.2017.0430
18. World Health Organization, (2021). Ethics and governance of artificial intelligence for health. https://www.who.int/publications/i/item/9789240029200
19. Deloitte, (2022). Scaling AI in healthcare: From pilot to enterprise value. https://www2.deloitte.com
20. J. He, S. L. Baxter, J. Xu, J. Xu, X. Zhou, and K. Zhang, “The practical implementation of artificial intelligence technologies in medicine,” Nat. Med., vol. 25, no. 1, pp. 30–36, Jan. 2019., doi:10.1038/s41591-018-0307-0
21.  M. Nagendran, Y. Chen, C. A. Lovejoy, A. C. Gordon, M. Komorowski, H. Harvey, et al., “Artificial intelligence versus clinicians: Systematic review of design, reporting standards, and claims of deep learning studies,” BMJ, vol. 368, p. m689, Mar. 25 2020., doi:10.1136/bmj.m689
22. C. J. Kelly, A. Karthikesalingam, M. Suleyman, G. Corrado, and D. King, “Key challenges for delivering clinical impact with artificial intelligence,” BMC Med., vol. 17, no. 1, p. 195, Oct. 29 2019., doi:10.1186/s12916-019-1426-2
23. J. Adler-Milstein and A. K. Jha, “HITECH Act drove large gains in hospital electronic health record adoption,” Health Aff. (Millwood), vol. 36, no. 8, pp. 1416–1422, Aug. 1 2017., doi:10.1377/hlthaff.2016.1651
24.  International Organization for Standardization, (2019). ISO 14971: Medical devices—Application of risk management to medical devices. https://www.iso.org/cms/%20render/live/en/sites/isoorg/contents/data/standard/07/27/72704.html
25. D. Sculley, G. Holt, D. Golovin, E. Davydov, T. Phillips, D. Ebner, et al., (2015). Hidden technical debt in machine learning systems. In Advances in Neural Information Processing Systems (NeurIPS). https://papers.nips.cc/paper/5656-hidden-technical-debt-in-machine-learning-systems
26. European Commission, (2024). Regulation (EU) 2024/1689 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). https://eur-lex.europa.eu/eli/reg/2024/1689/oj
27.  T. D. Shanafelt, L. N. Dyrbye, C. Sinsky, O. Hasan, D. Satele, J. Sloan, et al., “Relationship between clerical burden and characteristics of the electronic environment with physician burnout,” Mayo Clin. Proc., vol. 91, no. 7, pp. 836–848, Jul. 2016., doi:10.1016/j.mayocp.2016.05.007
28. Fortune Business Insights, (2026). Artificial intelligence (AI) in healthcare market size, share & industry analysis, by platform, application, end-user, and regional forecast, 2026–2034. https://www.fortunebusinessinsights.com/industry-reports/artificial-intelligence-in-healthcare-market-100534
29. McKinsey & Company, (2021). Scaling AI: It’s not just about the technology. https://www.mckinsey.com/capabilities/quantumblack/our-insights/scaling-ai-its-not-just-about-the-technology
30. Gartner. (2020). Gartner AI in organizations survey. https://www.gartner.com/en/newsroom/press-releases/2020-02-25-gartner-says-37-percent-of-organizations-have-implemented-ai-in-some-form
31. S. Srinivasan and J. Swaminathan, (2022). A layered architecture for artificial intelligence in healthcare. IEEE Engineering in Medicine and Biology Magazine. https://ieeexplore.ieee.org/document/9777261
32. McKinsey & Company, (2021). The state of AI in 2021. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2021
33. Q. Wu, G. Bansal, J. Zhang, Y. Wu, B. Li, E. Zhu, et al., (2023). AutoGen: Enabling next-gen LLM applications via multi-agent conversation frameworks. https://arxiv.org/abs/2308.08155
34. J. S. Park, J. O’Brien, C. J. Cai, M. R. Morris, P. Liang, and M. S. Bernstein, (2023). Generative agents: Interactive simulacra of human behavior. ACM UIST 2023. https://arxiv.org/abs/2304.03442
35. Nuance Communications, Inc., & Ignetica. (2022). The burden of clinical documentation in NHS trusts. https://www.nuance.com/content/dam/nuance/en_uk/collateral/healthcare/white-paper/wp-the-burden-of-clinical-documentation-in-nhs-trusts-en-gb.pdf
36. Freedom of Speech Online, (2015). Accuracy and completeness of clinical documentation.http://www.freedom-speech.co.uk/s/Accuracy_and_completeness_of_clinical_documentation_2015_FOS.pdf
37. C. Sinsky, L. Colligan, L. Li, M. Prgomet, S. Reynolds, L. Goeders, et al., “Allocation of physician time in ambulatory practice: A time and motion study in 4 specialties,” Ann. Intern. Med., vol. 165, no. 11, pp. 753–760, Dec. 6 2016., doi:10.7326/M16-0961
38. T. D. Shanafelt, L. N. Dyrbye, C. Sinsky, O. Hasan, D. Satele, J. Sloan, et al., “Relationship between clerical burden and characteristics of the electronic environment with physician burnout,” Mayo Clin. Proc., vol. 91, no. 7, pp. 836–848, Jul. 2016., doi:10.1016/j.mayocp.2016.05.007
39. European Commission, (2021). Proposal for a regulation laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52021PC0206
40. U.S. Department of Health & Human Services, (2013). Summary of the HIPAA Security Rule. https://www.hhs.gov/hipaa/for-professionals/security/laws-regulations/index.html
41. European Union. (2016). Regulation (EU) 2016/679 (General Data Protection Regulation). https://eur-lex.europa.eu/eli/reg/2016/679/oj
42. Government of India, (2023). Digital Personal Data Protection Act, 2023. https://www.meity.gov.in/writereaddata/files/Digital_Personal_Data_Protection_Act_2023.pdf
43. Ministry of Electronic and Information Technology, (2023). Explanatory note on the Digital Personal Data Protection Act, 2023. https://www.meity.gov.in
44. Ministry of Health and Family Welfare, (2018). Digital Information Security in Healthcare Act (DISHA) – Draft. https://main.mohfw.gov.in
45. N. I. T. I. Aayog, (2020). National Digital Health Blueprint. https://www.niti.gov.in
46. European Data Protection Board, “Guidelines on consent under,” Regulation, vol. 2016, no. 679, 2020. Available: https://edpb.europa.eu
47. U.S. Department of Health & Human Services, (2013). Breach notification rule. https://www.hhs.gov/hipaa/for-professionals/breach-notification/index.html
48. E. Rojas, J. Munoz-Gama, M. Sepúlveda, and D. Capurro, “Process mining in healthcare: A literature review,” J. Biomed. Inform., vol. 61, pp. 224–236, Jun. 2016., doi:10.1016/j.jbi.2016.04.007
49. European Union. (2024). Regulation (EU) 2024/1689 (Artificial Intelligence Act). https://eur-lex.europa.eu/eli/reg/2024/1689/oj
50. Health Level Seven International, (2019). HL7 Version 2 product suite. https://www.hl7.org/implement/standards/product_brief.cfm?product_id=185
51. National Electrical Manufacturers Association, (2023). Digital Imaging and Communications in Medicine (DICOM) standard. https://www.dicomstandard.org/current
52. Health Level Seven International, (2019). FHIR Release 4 (R4). https://www.hl7.org/fhir/
53. J. C. Mandel, D. A. Kreda, K. D. Mandl, I. S. Kohane, and R. B. Ramoni, “SMART on FHIR: A standards-based, interoperable apps platform for electronic health records,” J. Am. Med. Inform. Assoc., vol. 23, no. 5, pp. 899–908, Sep. 2016., doi: 10.1093/jamia/ocv189
54. T. Benson and G. Grieve, Principles of health interoperability: SNOMED CT, HL7 and FHIR, 4th ed., Springer, 2021., doi:10.1007/978-3-030-56883-5
55. M. G. Kahn, J. S. Brown, A. T. Chun, B. N. Davidson, D. Meeker, P. B. Ryan, et al., “Transparent reporting of data quality in distributed data networks,” EGEMS (Wash. DC), vol. 4, no. 1, p. 1232, 2016., doi:10.13063/2327-9214.1232
56.  CrowdFlower Inc, (2016). The data scientist report: 2016. https://visit.figure-eight.com/rs/416-ZBE-142/images/CrowdFlower_DataScienceReport_2016.pdf
57. T. Dasu and T. Johnson, Exploratory data mining and data cleaning. Wiley, 2003., doi: 10.1002/0471448354
58.  D. Sculley, G. Holt, D. Golovin, E. Davydov, T. Phillips, D. Ebner, et al., (2015). Hidden technical debt in machine learning systems. In Advances in Neural Information Processing Systems (NeurIPS).https://papers.nips.cc/paper/5656-hidden-technical-debt-in-machine-learning-systems
59. IBM, (2021). Data fabric architecture: A unified approach to data integration.https://www.ibm.com/cloud/learn/data-fabric
60. National Institute of Standards and Technology, (2020). NIST Privacy Framework: A tool for improving privacy through enterprise risk management (Version 1.0). https://www.nist.gov/privacy-framework
61. R. Bommasani, D. A. Hudson, E. Adeli, R. Altman, S. Arora, S. von Arx, et al., (2021). On the opportunities and risks of foundation models. https://arxiv.org/abs/2108.07258
62. Q. Wu, G. Bansal, J. Zhang, Y. Wu, B. Li, E. Zhu, et al., (2023). AutoGen: Enabling next-gen LLM applications via multi-agent conversation frameworks. https://arxiv.org/abs/2308.08155
63. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. Aguera y Arcas, (2017). Communication-efficient learning of deep networks from decentralized data. https://arxiv.org/abs/1602.05629
64. Amazon Web Services, (2022). What is event-driven architecture?  https://aws.amazon.com/event-driven-architecture/
65. Cloud Native Computing Foundation, (2020). Policy as code (OPA and cloud-native governance). https://www.cncf.io
66. J. He, S. L. Baxter, J. Xu, J. Xu, X. Zhou, and K. Zhang, “The practical implementation of artificial intelligence technologies in medicine,” Nat. Med., vol. 25, no. 1, pp. 30–36, Jan. 2019., doi:10.1038/s41591-018-0307-0
67. U.S. Department of Health & Human Services, (n.d.). Security standards for the protection of electronic protected health information (45 CFR Part 164 Subpart C). https://www.ecfr.gov/current/title-45/subtitle-A/subchapter-C/part-164/subpart-C
68. National Institute of Standards and Technology, (2008). An introductory resource guide for implementing the HIPAA Security Rule (NIST SP 800-66 Rev. 1). https://csrc.nist.gov/publications/detail/sp/800-66/rev-1/final
69. D. McGraw, “Building public trust in uses of Health Insurance Portability and Accountability Act de-identified data,” J. Am. Med. Inform. Assoc., vol. 20, no. 1, pp. 29–34, Jan. 1 2013., doi:10.1136/amiajnl-2012-001028
70. U.S. Food and Drug Association. (2021). Artificial intelligence/machine learning (AI/ML)-based software as a medical device (SaMD) action plan. https://www.fda.gov/media/145022/download
71. National Health Authority, (2021). Health Data Management Policy. https://abdm.gov.in:8081/uploads/health_data_management_policy.pdf
72.  International Organization for Standardization, (2022). ISO/IEC 27001:2022—Information security, cybersecurity and privacy protection—Information security management systems—Requirements. https://www.iso.org/standard/82875.html
73. International Organization for Standardization, (2022). ISO/IEC 27002:2022—Information security, cybersecurity and privacy protection—Information security controls. https://www.iso.org/standard/75652.html
74. National Institute of Standards and Technology. (2018). Framework for improving critical infrastructure cybersecurity (Version 1.1)., doi:10.6028/NIST.CSWP.04162018
75. International Organization for Standardization, (2016). ISO 13485:2016—Medical devices—Quality management systems—Requirements for regulatory purposes. https://www.iso.org/standard/59752.html
76. International Electrotechnical Commission, (2006). IEC 62304:2006—Medical device software—Software lifecycle processes. https://www.iec.ch/dyn/www/f?p=103:85:0::::FSP_LANG_ID:25
77. International Medical Device Regulators Forum, (2013). Software as a medical device (SaMD): Key definitions. https://www.imdrf.org/documents/software-medical-device-samd-key-definitions
78. International Organization for Standardization, (2023). ISO/IEC 42001:2023—Artificial intelligence—Management system. https://www.iso.org/standard/81230.html
79. National Institute of Standards and Technology, (2023). AI risk management framework (AI RMF 1.0). https://www.nist.gov/itl/ai-risk-management-framework
80. T. Schick, J. Dwivedi-Yu, R. Dessì, R. Raileanu, M. Lomeli, E. Hambro, et al., (2023). Toolformer: Language models can teach themselves to use tools. https://arxiv.org/abs/2302.04761
81. S. Yao, J. Zhao, D. Yu, N. Du, I. Shafran, K. Narasimhan, et al., (2023). ReAct: Synergizing reasoning and acting in language models. https://arxiv.org/abs/2210.03629
82. American Medical Association, (2022). Augmented intelligence in health care. https://www.ama-assn.org/practice-management/digital/augmented-intelligence-health-care
83. National Academies of Science Engineering and Medicine. (2019). Integrating social care into the delivery of health care: Moving upstream to improve the nation’s health., doi:10.17226/25467
84. World Health Organization, (2009). WHO guidelines for safe surgery 2009. https://www.who.int/publications/i/item/9789241598552
85. Y. Du, S. Li, A. Torralba, J. B. Tenenbaum, and I. Mordatch, (2024). Improving factuality and reasoning in language models through multiagent debate.https://arxiv.org/abs/2305.14325
86. Microsoft, (2023). AutoGen: Multi-agent conversation framework (technical report & blog). https://www.microsoft.com/en-us/research/project/autogen/
87. S. Secinaro, D. Calandra, A. Secinaro, V. Muthurangu, and P. Biancone, “The role of artificial intelligence in healthcare: A structured literature review,” BMC Med. Inform. Decis. Mak., vol. 21, no. 1, p. 125, Apr. 10 2021., doi:10.1186/s12911-021-01488-9
88. A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, et al., “Dermatologist-level classification of skin cancer with deep neural networks,” Nature, vol. 542, no. 7639, pp. 115–118, Feb. 2 2017., doi:10.1038/nature21056
89. M. Matheny, S. T. Israni, M. Ahmed, and D. Whicher, Eds. Artificial intelligence in health care: The hope, the hype, the promise, the peril. National Academy of Medicine, 2019., doi: 10.17226/25563
90. M. Wooldridge, An introduction to multiagent systems, 2nd ed., Wiley, 2009., doi: 10.1002/9780470519462
91.  P. N. Srinivasu, G. L. Aruna Kumari, S. Ahmed, and A. Alhumam, “Exploring Agentic AI in Healthcare: A study on its working mechanism,” Front. Med. (Lausanne), vol. 12, p. 1753443, Jan. 28 2026., doi:10.3389/fmed.2025.1753443
92. L. Petersson, I. Larsson, J. M. Nygren, P. Nilsen, M. Neher, J. E. Reed, et al., “Challenges to implementing artificial intelligence in healthcare: A qualitative interview study with healthcare leaders in Sweden,” BMC Health Serv. Res., vol. 22, no. 1, p. 850, Jul. 1 2022., doi:10.1186/s12913-022-08215-8
93. M. Amini, et al., “Artificial intelligence ethics in healthcare: Challenges and opportunities under GDPR,” J. Med. Syst., vol. 47, p. 102, 2023., doi:10.1007/s10916-023-01934-6
94. World Economic Forum, (2022). Shaping the future of artificial intelligence governance. https://www.weforum.org/reports/shaping-the-future-of-artificial-intelligence-governance
95. Organisation for Economic Co-operation and Development, (2024). Scaling artificial intelligence in health: Lessons for policy and practice.https://www.oecd.org/health/scaling-artificial-intelligence-in-health.htm
96. N. Rieke, J. Hancox, W. Li, F. Milletarì, H. R. Roth, S. Albarqouni, et al., “The future of digital health with federated learning,” NPJ Digit. Med., vol. 3, no. 119, p. 119, Sep. 14 2020., doi:10.1038/s41746-020-00323-1
97. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. Aguera y Arcas, (2017). Communication-efficient learning of deep networks from decentralized data. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS 2017) (pp. 1273–1282). https://proceedings.mlr.press/v54/mcmahan17a.html
98. Dwork, C., & Roth, A. (2014). The algorithmic foundations of differential privacy. Foundations and Trends® in Theoretical Computer Science, 9(3–4), 211–407., doi:10.1561/0400000042
99. M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, et al., (2016). Deep learning with differential privacy. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (pp. 308–318)., doi:10.1145/2976749.2978318
100. K. Bonawitz, V. Ivanov, B. Kreuter, A. Marcedone, H. B. McMahan, S. Patel, et al., (2017). Practical secure aggregation for privacy-preserving machine learning. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security (pp. 1175–1191)., doi:10.1145/3133956.3133982
101. A. C. Yao, (1982). Protocols for secure computations. In 23rd Annual Symposium on  Foundations of Computer Science (SFCS 1982) (pp. 160–164).,   doi:10.1109/SFCS.1982.38
102. N. Rieke, J. Hancox, W. Li, F. Milletarì, H. R. Roth, S. Albarqouni, et al., “The future of digital health with federated learning,” NPJ Digit. Med., vol. 3, no. 119, p. 119, Sep. 14 2020., doi:10.1038/s41746-020-00323-1
103. T. Li, A. K. Sahu, A. Talwalkar, and V. Smith, “Federated learning: Challenges, methods, and future directions,” IEEE Signal Process. Mag., vol. 37, no. 3, pp. 50–60, 2020., doi:10.1109/MSP.2020.2975749
104. Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., D’Orazio, V., Eichner, H., El Rouayheb, S., Evans, D., Gardner, J., et al. (2021). Advances and open problems in federated learning. Foundations and Trends® in Machine Learning, 14(1–2), 1–210., doi:10.1561/2200000083
105. L. Wang, C. Ma, X. Feng, Z. Zhang, H. Yang, J. Gao, et al., (2023). A survey of large language model based autonomous agents. https://arxiv.org/abs/2308.11432
106. J. S. Park, J. O’Brien, C. J. Cai, M. R. Morris, P. Liang, and M. S. Bernstein, (2023). Generative agents: Interactive simulacra of human behavior. In Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology (UIST ’23)., doi:10.1145/3586183.3606763
107. A. Singh, V. Patel, and N. Kumar, “Challenges and opportunities in integrating large language models with healthcare information systems: A review,” J. Biomed. Inform., vol. 157, p. 104728, 2024., doi:10.1016/j.jbi.2024.104728
108. U. M. Borghoff, A. Heinzl, and R. Pareschi, “Human oversight and accountability in agentic AI systems for healthcare,” AI Soc.; Advance online publication, 2025., doi:10.1007/s00146-025-01987-4
109. H. Qiu, et al., “Towards artificial general intelligence via a multimodal foundation model,” Nat. Mach. Intell., vol. 6, no. 1, pp. 15–28, 2024., doi:10.1038/s42256-023-00765-7
110. World Health Organization, (2016). Framework on integrated, people-centred 
        health services. https://www.who.int/publications/i/item/WHO-HIS-SDS-2016.19
111. M. Abou Ali, F. Dornaika, and J. Charafeddine, “Agentic AI: A comprehensive survey of architectures, applications, and future directions,” Artif. Intell. Rev., vol. 59, no. 1, p. 11, 2026., doi:10.1007/s10462-025-11422-4
112. B. Shneiderman, “Human-centered artificial intelligence: Reliable, safe & trustworthy,” Int. J. Hum. Comput. Interact., vol. 36, no. 6, pp. 495–504, 2020., doi:10.1080/10447318.2020.1741118
113. K. W. Johnson, J. Torres Soto, B. S. Glicksberg, K. Shameer, R. Miotto, M. Ali, et al., “Artificial intelligence in healthcare: Frameworks for governance, validation, and operational oversight,” NPJ Digit. Med., vol. 7, no. 41, 2024., doi:10.1038/s41746-024-01041-2
114. A. Rajkomar, J. Dean, and I. Kohane, “Machine learning in medicine,” N. Engl. J. Med., vol. 380, no. 14, pp. 1347–1358, Apr. 4 2019., doi:10.1056/NEJMra1814259
115. J. Lamb, (2024, July 25). Generative AI in healthcare: Adoption trends and what’s next. McKinsey & Company. https://www.mckinsey.com/industries/healthcare/our-insights/generative-ai-in-healthcare-adoption-trends-and-whats-next
116. A. Krishna, D. Friend, N. Gohad, and P. Reddy, (2025, November 18). The coming evolution of healthcare AI toward a modular architecture. McKinsey & Company. https://www.mckinsey.com/industries/healthcare/our-insights/the-coming-evolution-of-healthcare-ai-toward-a-modular-architecture
117. V. I. Madai and D. C. Higgins, (2021, July 28). Artificial intelligence in healthcare: lost in translation? arXiv.org. https://arxiv.org/abs/2107.13454
118. M. Hassan, A. Kushniruk, and E. Borycki, “Barriers to and Facilitators of Artificial intelligence adoption in Health Care: Scoping review,” JMIR Hum. Factors, vol. 11, p. e48633, Aug. 29 2024., doi:10.2196/48633
119. M. F. Wibowo, A. Pyle, E. Lim, J. W. Ohde, N. Liu, and J. Karlström, “Insights into the current and future state of AI adoption within health systems in Southeast Asia: Cross-Sectional Qualitative Study,” J. Med. Internet Res., vol. 27, p. e71591, Jun. 16 2025., doi:10.2196/71591
120. K. Peffers, T. Tuunanen, M. A. Rothenberger, and S. Chatterjee, “A design science research methodology for information systems research,” J. Manage. Inf. Syst., vol. 24, no. 3, pp. 45–77, 2007., doi:10.2753/MIS0742-1222240302
121. W . Jeanne, Ross, Weill, P., & Robertson, D. C. (2006). Enterprise architecture as strategy: Creating a foundation for business execution. Harvard Business School Press. https://store.hbr.org/product/enterprise-architecture-as-strategy-creating-a-foundation-for-business-execution/8398
122. Synthea. https://synthetichealth.github.io/synthea/
123. J. Walonoski, M. Kramer, J. Nichols, A. Quina, C. Moesel, D. Hall, et al., “Synthea: An approach, method, and software mechanism for generating synthetic patients and the synthetic electronic health care record,” J. Am. Med. Inform. Assoc., vol. 25, no. 3, pp. 230–238, Mar. 1 2018., doi 10.1093/jamia/ocx079
124. F. Perez and I. Ribeiro, (2022). Ignore previous prompt: Attack techniques for language models. https://arxiv.org/abs/2211.09527
125. Open Worldwide Application Security Project, (2024). OWASP Top 10 for Large Language Model Applications 2024. https://owasp.org/www-project-top-10-for-large-language-model-applications/
126. National Institute of Standards and Technology, (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0). https://www.nist.gov/itl/ai-risk-management-framework  
127. R. S. Sandhu, E. J. Coyne, H. L. Feinstein, and C. E. Youman, “Role-based access control models,” Computer, vol. 29, no. 2, pp. 38–47, 1996., doi:10.1109/2.485845
128. M. Maimaitiaili, C. Weng, and Y. Luo, “Artificial intelligence architecture frameworks for smart hospitals: A layered platform perspective,” J. Biomed. Inform., vol. 154, p. 104702, 2024., doi:10.1016/j.jbi.2024.104702
129. McKinsey & Company, (2025). The economic potential of generative AI in healthcare and hospital operations. https://www.mckinsey.com/industries/healthcare/our-insights/the-economic-potential-of-generative-ai-in-healthcare
130. P. Dasaradharami Reddy and T. R. Gadekallu, “Federated learning for healthcare informatics: Concepts, applications, and privacy preservation challenges,” IEEE Access, vol. 11, pp. 41265–41289, 2023., doi:10.1109/ACCESS.2023.3268447
131. T. Li, A. K. Sahu, A. Talwalkar, and V. Smith, “Federated learning: Challenges, methods, and future directions,” IEEE Signal Process. Mag., vol. 37, no. 3, pp. 50–60, 2020., doi:10.1109/MSP.2020.2975749
132. S. Banabilah, M. Aloqaily, and Y. Jararweh, “Privacy-preserving healthcare data sharing using federated learning: A survey,” Sensors (Basel), vol. 22, no. 2, p. 612, 2022., doi:10.3390/s22020612
133. IBM Healthcare Insights, (2024). AI in healthcare: Operational efficiency, workflow transformation, and enterprise value. https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/ai-healthcare
134. T. D. Shanafelt, L. N. Dyrbye, C. Sinsky, O. Hasan, D. Satele, J. Sloan, et al., “Relationship between clerical burden and characteristics of the electronic environment with physician burnout and professional satisfaction,” Mayo Clin. Proc., vol. 91, no. 7, pp. 836–848, Jul. 2016., doi:10.1016/j.mayocp.2016.05.007
135. D. S. Char, N. H. Shah, and D. Magnus, “Implementing machine learning in health care — Addressing ethical challenges,” N. Engl. J. Med., vol. 378, no. 11, pp. 981–983, Mar. 15 2018., doi:10.1056/NEJMp1714229
136. Z. Obermeyer, B. Powers, C. Vogeli, and S. Mullainathan, “Dissecting racial bias in an algorithm used to manage the health of populations,” Science, vol. 366, no. 6464, pp. 447–453, Oct. 25 2019., doi:10.1126/science.aax2342
137. W. N. Price, II and I. G. Cohen, “Privacy in the age of medical big data,” Nat. Med., vol. 25, no. 1, pp. 37–43, Jan. 2019., doi:10.1038/s41591-018-0272-7