A Systematic Review of Agentic AI in Healthcare: An Evidence-Informed Seven-Principle Framework
DOI:
https://doi.org/10.62411/jcta.16171Keywords:
Agentic AI, Autonomous agents, Clinical decision support, Digital health, Explainability, Human-in-the-loop, Multi-agent systems, Patient monitoringAbstract
Agentic artificial intelligence (AI) systems capable of autonomous goal-directed behavior, multi-step planning, tool use, multi-agent coordination, and iterative self-correction represent a transition from passive clinical AI tools toward systems that can participate in complex healthcare workflows. However, empirical evidence remains fragmented across clinical decision support, patient monitoring, and administrative applications, and no systematic synthesis has evaluated which agentic principles have been technically demonstrated and which have accumulated sufficient evidence to support responsible clinical deployment. We conducted a PRISMA-informed systematic review of peer-reviewed empirical studies published between January 2025 and April 2026. Searches across five bibliographic databases and Google Scholar, supplemented by citation tracking, identified 443 unique records for screening, of which 25 met the predefined PICOS and quality appraisal criteria. Evidence was synthesized using an evidence-informed seven-principle framework derived from the integration of agentic AI, clinical AI, and healthcare governance literature. This framework provides a structured lens for examining how agentic principles are evaluated individually and in combination, enabling a deployment-readiness perspective that extends beyond capability-focused assessments alone. The evidence base was concentrated on technical capability principles, whereas human oversight, safety, compliance, and equity-related evaluation received comparatively limited attention. Most studies remained at the laboratory, benchmark, or proof-of-concept stage, and none reported demographic-stratified performance outcomes. Overall, the findings suggest a structural asymmetry in agentic healthcare AI: empirical research is advancing agentic capabilities more rapidly than it is generating evidence for the oversight, safety, equity, and governance mechanisms required for responsible clinical translation.References
S. Balakrishna and V. Kumar Solanki, “A Comprehensive Review on AI-Driven Healthcare Transformation,” Ing. Solidar., vol. 20, no. 2, pp. 1–30, May 2024, doi: 10.16925/2357-6014.2024.02.07.
J. Qiu et al., “LLM-based agentic systems in medicine and healthcare,” Nat. Mach. Intell., vol. 6, no. 12, pp. 1418–1420, Dec. 2024, doi: 10.1038/s42256-024-00944-1.
A. Alnattah, M. Jajroudi, S. A. N. Fadafen, M. N. Manzari, and S. Eslami, “Artificial Intelligence in Clinical Decision-Making: A Scoping Review of Rule-Based Systems and Their Applications in Medicine,” Cureus, Aug. 2025, doi: 10.7759/cureus.91333.
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.
R. Hirani et al., “Artificial Intelligence and Healthcare: A Journey through History, Present Innovations, and Future Possibilities,” Life, vol. 14, no. 5, p. 557, Apr. 2024, doi: 10.3390/life14050557.
M. M. Raza, K. P. Venkatesh, and J. C. Kvedar, “Generative AI and large language models in health care: pathways to implementation,” npj Digit. Med., vol. 7, no. 1, p. 62, Mar. 2024, doi: 10.1038/s41746-023-00988-4.
W. Zhao et al., “An agentic system for rare disease diagnosis with traceable reasoning,” Nature, vol. 651, no. 8106, pp. 775–784, Mar. 2026, doi: 10.1038/s41586-025-10097-9.
H. Eguia, C. L. Sánchez-Bocanegra, F. Vinciarelli, F. Alvarez-Lopez, and F. Saigí-Rubió, “Clinical Decision Support and Natural Language Processing in Medicine: Systematic Literature Review,” J. Med. Internet Res., vol. 26, p. e55315, Sep. 2024, doi: 10.2196/55315.
M. H. Shahin, S. Goswami, S. Lobentanzer, and B. W. Corrigan, “Agents for Change: Artificial Intelligent Workflows for Quantitative Clinical Pharmacology and Translational Sciences,” Clin. Transl. Sci., vol. 18, no. 3, Mar. 2025, doi: 10.1111/cts.70188.
S. Vatsal, H. Dubey, and A. Singh, “Agentic AI in Healthcare and Medicine: A Seven-Dimensional Taxonomy for Empirical Evaluation of LLM-Based Agents,” IEEE Access, vol. 14, pp. 4840–4863, 2026, doi: 10.1109/ACCESS.2026.3651218.
S. Johri et al., “An evaluation framework for clinical use of large language models in patient interaction tasks,” Nat. Med., vol. 31, no. 1, pp. 77–86, Jan. 2025, doi: 10.1038/s41591-024-03328-5.
D. Ferber et al., “Development and validation of an autonomous artificial intelligence agent for clinical decision-making in oncology,” Nat. Cancer, vol. 6, no. 8, pp. 1337–1349, Jun. 2025, doi: 10.1038/s43018-025-00991-6.
Q. Liu et al., “EvoMDT: a self-evolving multi-agent system for structured clinical decision-making in multi-cancer,” npj Digit. Med., vol. 9, no. 1, p. 124, Jan. 2026, doi: 10.1038/s41746-025-02304-8.
S. Bedi et al., “Testing and Evaluation of Health Care Applications of Large Language Models,” JAMA, vol. 333, no. 4, p. 319, Jan. 2025, doi: 10.1001/jama.2024.21700.
B. G. Collaco et al., “The role of agentic artificial intelligence in healthcare: a scoping review,” npj Digit. Med., vol. 9, no. 1, p. 345, Mar. 2026, doi: 10.1038/s41746-026-02517-5.
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, Nov. 2025, doi: 10.1007/s10462-025-11422-4.
B. Njei et al., “Artificial intelligence agents in healthcare research: A scoping review,” PLoS One, vol. 21, no. 2, p. e0342182, Feb. 2026, doi: 10.1371/journal.pone.0342182.
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., vol. 12, Jan. 2026, doi: 10.3389/fmed.2025.1753443.
S. Hosseini and H. Seilani, “The role of agentic AI in shaping a smart future: A systematic review,” Array, vol. 26, p. 100399, Jul. 2025, doi: 10.1016/j.array.2025.100399.
Y. Hao et al., “Personalizing prostate cancer education for patients using an EHR-Integrated LLM agent,” npj Digit. Med., vol. 8, no. 1, p. 770, Dec. 2025, doi: 10.1038/s41746-025-02166-0.
M. J. Page et al., “The PRISMA 2020 statement: an updated guideline for reporting systematic reviews,” BMJ, p. n71, Mar. 2021, doi: 10.1136/bmj.n71.
S. Yao and others, “ReAct: Synergizing Reasoning and Acting in Language Models,” OpenReview.net. 2023. [Online]. Available: https://openreview.net/forum?id=WE_vluYUL-X
S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 4th ed. Pearson, Hoboken, 2021.
T. Schick and others, “Toolformer: Language Models Can Teach Themselves to Use Tools,” in NIPS ’23: Proceedings of the 37th International Conference on Neural Information Processing Systems, 2023. [Online]. Available: https://dl.acm.org/doi/10.5555/3666122.3669119
Y. Du, S. Li, A. Torralba, J. B. Tenenbaum, and I. Mordatch, “Improving Factuality and Reasoning in Language Models through Multiagent Debate,” in ICML’24: Proceedings of the 41st International Conference on Machine Learning, May 2024, pp. 11733–11763. [Online]. Available: http://arxiv.org/abs/2305.14325
European Parliament and Council of the European Union, “Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act).” 2024. [Online]. Available: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32024R1689
WHO guidance and World Health Organization, “Ethics and governance of artificial intelligence for health,” World Health Organization, 2021. https://www.who.int/publications/i/item/9789240029200
G. Joshi, A. Jain, S. R. Araveeti, S. Adhikari, H. Garg, and M. Bhandari, “FDA-Approved Artificial Intelligence and Machine Learning (AI/ML)-Enabled Medical Devices: An Updated Landscape,” Electronics, vol. 13, no. 3, p. 498, Jan. 2024, doi: 10.3390/electronics13030498.
A. F. Markus, J. A. Kors, and P. R. Rijnbeek, “The role of explainability in creating trustworthy artificial intelligence for health care: A comprehensive survey of the terminology, design choices, and evaluation strategies,” J. Biomed. Inform., vol. 113, p. 103655, Jan. 2021, doi: 10.1016/j.jbi.2020.103655.
E. Tjoa and C. Guan, “A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI,” IEEE Trans. Neural Networks Learn. Syst., vol. 32, no. 11, pp. 4793–4813, Nov. 2021, doi: 10.1109/TNNLS.2020.3027314.
S. Mbonihankuye, A. Nkunzimana, and A. Ndagijimana, “Healthcare Data Security Technology: HIPAA Compliance,” Wirel. Commun. Mob. Comput., vol. 2019, pp. 1–7, Oct. 2019, doi: 10.1155/2019/1927495.
M. Wooldridge and N. R. Jennings, “Intelligent Agents: Theory and Practice,” Knowl. Eng. Rev., vol. 10, no. 2, pp. 115–152, 1995.
S. Yao et al., “Tree of Thoughts: Deliberate Problem Solving with Large Language Models,” in Proceedings of the 37th International Conference on Neural Information Processing Systems (NIPS ’23), 2023, pp. 11809–11822. [Online]. Available: https://dl.acm.org/doi/abs/10.5555/3666122.3666639
Z. Xi et al., “The rise and potential of large language model based agents: a survey,” Sci. China Inf. Sci., vol. 68, no. 2, p. 121101, Feb. 2025, doi: 10.1007/s11432-024-4222-0.
L. Wang et al., “A survey on large language model based autonomous agents,” Front. Comput. Sci., vol. 18, no. 6, p. 186345, Dec. 2024, doi: 10.1007/s11704-024-40231-1.
I. Gabriel, “Artificial Intelligence, Values, and Alignment,” Minds Mach., vol. 30, no. 3, pp. 411–437, Sep. 2020, doi: 10.1007/s11023-020-09539-2.
O. Freyer, S. Jayabalan, J. N. Kather, and S. Gilbert, “Overcoming regulatory barriers to the implementation of AI agents in healthcare,” Nat. Med., vol. 31, no. 10, pp. 3239–3243, Oct. 2025, doi: 10.1038/s41591-025-03841-1.
L. Floridi et al., “AI4People—An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations,” Minds Mach., vol. 28, no. 4, pp. 689–707, Dec. 2018, doi: 10.1007/s11023-018-9482-5.
A. Jobin, M. Ienca, and E. Vayena, “The global landscape of AI ethics guidelines,” Nat. Mach. Intell., vol. 1, no. 9, pp. 389–399, Sep. 2019, doi: 10.1038/s42256-019-0088-2.
B. Mohapatra, B. Walia, and S. Dash, “A Modular Multi-Agent Framework for Clinical Documentation and Hospital Operations,” in 2025 8th International Conference on Algorithms, Computing and Artificial Intelligence (ACAI), Dec. 2025, pp. 1–5. doi: 10.1109/ACAI68217.2025.11406271.
S. AlZu’Bi, M. Almiani, and Y. Jararweh, “Agentic AI for Healthcare: Solutions to Intelligent Patient Care,” in 2025 12th International Conference on Information Technology (ICIT), May 2025, pp. 33–38. doi: 10.1109/ICIT64950.2025.11049267.
Q. Li, K. R. Amat, and J. Li, “From Knowledge to Action: An Agentic AI Framework for Diabetes Management,” in 2025 IEEE 16th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), Oct. 2025, pp. 395–401. doi: 10.1109/UEMCON67449.2025.11267605.
L. Murali, A. Thankachan, and D. M. Viswanathan, “Agentic AI for Severity Extraction in Clinical Notes: Enhancing Disease Diagnosis Beyond Rule-Based and Traditional ML Models,” in 2025 International Conference on Power, Instrumentation, Control, and Computing (PICC), Oct. 2025, pp. 1–6. doi: 10.1109/PICC67314.2025.11291255.
Y.-J. Chen, A. Albarqawi, and C.-S. Chen, “Enhancing Clinical Decision-Making: Integrating Multi-Agent Systems with Ethical AI Governance,” in 2025 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), Aug. 2025, pp. 1–7. doi: 10.1109/CIBCB66090.2025.11177136.
X. Qiao et al., “Parallel Nursing: Enhancing Postoperative Nursing With LLM Agent Systems,” IEEE Trans. Comput. Soc. Syst., vol. 13, no. 2, pp. 2002–2011, Apr. 2026, doi: 10.1109/TCSS.2025.3605582.
A. Mukhtar, G. E. Arzu, W. T. Toor, and U. Ali, “Collaborative multi-agent conversational artificial intelligence for clinical support in Parkinson disease,” Parkinsonism Relat. Disord., p. 108292, Mar. 2026, doi: 10.1016/j.parkreldis.2026.108292.
R. R. Pai, “Agentic AI Implementation in Healthcare Insurance Industry: A Comprehensive Framework for Automated Claims Processing and Risk Assessment,” in 2025 IEEE Madhya Pradesh Section Conference (MPCON), Aug. 2025, pp. 812–817. doi: 10.1109/MPCON66082.2025.11256764.
K. Lee et al., “A4SLR: An Agentic Artificial Intelligence-Assisted Systematic Literature Review Framework to Augment Evidence Synthesis for Health Economics and Outcomes Research and Health Technology Assessment,” Value Heal., vol. 28, no. 11, pp. 1655–1664, Nov. 2025, doi: 10.1016/j.jval.2025.08.002.
S. R. Shimgekar, S. Vassef, A. Goyal, K. Saha, P. Zonooz, and N. Kumar, “Agentic AI Framework for End-to-End Medical Data Inference,” in 2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Dec. 2025, pp. 7807–7810. doi: 10.1109/BIBM66473.2025.11356498.
A. Patel, S. Joseph, C. Bailey, A. Sakharpe, and M. Devarapalli, “Transforming electrophysiology workflows with natural language processing and agentic artificial intelligence,” Hear. Rhythm O2, vol. 6, no. 10, pp. 1613–1620, Oct. 2025, doi: 10.1016/j.hroo.2025.07.013.
E. Tzanis and M. E. Klontzas, “mAIstro: An open-source multi-agent system for automated end-to-end development of radiomics and deep learning models for medical imaging,” Eur. J. Radiol. Artif. Intell., vol. 4, p. 100044, Dec. 2025, doi: 10.1016/j.ejrai.2025.100044.
Z. Yi, J. Liu, M. V. Albert, and T. Xiao, “A Multi-Agent System for Complex Reasoning in Radiology Visual Question Answering,” in 2025 ACM/IEEE Joint Conference on Digital Libraries (JCDL), Dec. 2025, pp. 139–147. doi: 10.1109/JCDL67857.2025.00025.
R. Sathya and A. Valaramathi, “Detection and diagnosis of diabetic retinopathy in retinal fundus images using agentic AI approaches,” Sci. Rep., vol. 16, no. 1, p. 3780, Dec. 2025, doi: 10.1038/s41598-025-34016-0.
P. Liu and L. Xiao, “Improving Clinical Decision Support: Architecture Design of a Multi-Agent System based on an Argument Quality Assessment Ontology,” in 2025 IEEE 22nd International Conference on Software Architecture (ICSA), 2025, pp. 313–323. doi: 10.1109/ICSA65012.2025.00039.
A. Loaiza-Bonilla et al., “Transforming oncology clinical trial matching through neuro-symbolic, multi-agent AI and an oncology-specific knowledge graph: a prospective evaluation in 3804 patients,” ESMO Real World Data Digit. Oncol., vol. 12, p. 100706, Jun. 2026, doi: 10.1016/j.esmorw.2026.100706.
K. Linton-Reid, M. Chen, M. B. Martell, J. M. Posma, and E. O. Aboagye, “Radiomics in clinical radiology: advances, challenges, and future directions,” Clin. Radiol., vol. 92, p. 107165, Jan. 2026, doi: 10.1016/j.crad.2025.107165.
M. Omar et al., “Impact of patient communication style on agentic AI-generated clinical advice in E-medicine,” Am. J. Med., vol. 139, no. 4, pp. 437–444, Apr. 2026, doi: 10.1016/j.amjmed.2025.12.027.
J. Wang, K. Arora, D. Swoboda, and A. Nazha, “Artificial intelligence system for delivering interactive and verifiable guideline-based clinical decision support in hematology,” Blood, vol. 146, no. Supplement 1, pp. 2578–2578, Nov. 2025, doi: 10.1182/blood-2025-2578.
O. Freyer et al., “The regulation of artificial intelligence in intensive care units: from narrow tools to generalist systems,” npj Digit. Med., vol. 9, no. 1, p. 246, Mar. 2026, doi: 10.1038/s41746-026-02535-3.
A. J. Goodell, S. N. Chu, D. Rouholiman, and L. F. Chu, “Large language model agents can use tools to perform clinical calculations,” npj Digit. Med., vol. 8, no. 1, p. 163, Mar. 2025, doi: 10.1038/s41746-025-01475-8.
S. Schmidgall et al., “AgentClinic: a multimodal benchmark for tool-using clinical AI agents,” npj Digit. Med., Apr. 2026, doi: 10.1038/s41746-026-02674-7.
B. Babic, I. Glenn Cohen, A. D. Stern, Y. Li, and M. Ouellet, “A general framework for governing marketed AI/ML medical devices,” npj Digit. Med., vol. 8, no. 1, p. 328, May 2025, doi: 10.1038/s41746-025-01717-9.
M. Moritz, E. Topol, and P. Rajpurkar, “Coordinated AI agents for advancing healthcare,” Nat. Biomed. Eng., vol. 9, no. 4, pp. 432–438, Apr. 2025, doi: 10.1038/s41551-025-01363-2.
J. C. L. Ong et al., “Innovating global regulatory frameworks for generative AI in medical devices is an urgent priority,” npj Digit. Med., vol. 9, no. 1, p. 364, Mar. 2026, doi: 10.1038/s41746-026-02552-2.
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