Open Access
Enhancing Clinical Decision-Making Using Generative AI-Powered Knowledge Retrieval Systems: A Review of Emerging Approaches and Challenges
4
Independent Researcher, USA
Abstract
A wealth of biomedical information and literature, complex electronic health records (EHRs), disjointed guidance, and time pressures associated with the care process are all affecting clinical decision-making. The objective of this review was to discuss the potential of generative AI-driven knowledge retrieval systems for clinical decision-making and to describe some of the technical, compliance, ethical, and implementation challenges and limitations. This purposively selected, 42-source structured narrative review with scoping review elements was conducted based on publications retrieved from PubMed, Scopus, IEEE Xplore, Google Scholar, WHO/FDA, Web of Science, and major clinical informatics journals from 2020-2026. Study results demonstrate that retrieval-augmented generation (RAG) systems can provide accurate and relevant information by grounding content generated by large language models (LLMs) in clinical guidelines, biomedical literature, drug databases, EHR data, and institutional protocols. The results showed that RAG systems yielded better biomedical system performance than the baseline LLM, with an OR of 1.35 (95% CI: 1.19, 1.53). There are potential impacts, however, such as hallucinations, incomplete retrieval, incomplete and comprehensive datasets, privacy breaches, and lack of multilingual validation. The study concludes that evidence-based, auditable, locally adaptable, and supervised by licensed clinician retrieval systems with generative AI can support safer, faster, and more relevant decision-making processes in clinical settings. Future studies should involve prospective multi-site implementation, a clear retrieval pipeline, multilingual datasets, EHR integration, ongoing monitoring, and clinical governance models to ensure safe use.
Keywords
Generative AI in healthcare,
Clinical decision support systems,
Retrieval-augmented generation,
Medical knowledge retrieval,
Electronic health records,
Evidence-based clinical decision-making.
References
N. C. Apathy, A. J. Holmgren, and D. A. Cross, “Physician EHR time and visit volume following adoption of team-based documentation support,” JAMA Internal Medicine, vol. 184, no. 10, pp. 1212–1221, 2024. https://jamanetwork.com/journals/jamainternalmedicine/articlepdf/2822382/jamainternal_apathy_2024_oi_240052_1727970846.4993.pdf
A. Nooruddin, A. Lal, S. M. F. Ahmed, M. H. Ghori, N. Adnan, and F. Umer, “Use of generative artificial intelligence models in healthcare: A scoping review,” BMC Artificial Intelligence, vol. 2, no. 1, p. 11, 2026. https://link.springer.com/content/pdf/10.1186/s44398-026-00029-6.pdf
R. T. Sutton, D. Pincock, D. C. Baumgart, D. C. Sadowski, R. N. Fedorak, and K. I. Kroeker, “An overview of clinical decision support systems: Benefits, risks, and strategies for success,” npj Digital Medicine, vol. 3, no. 1, p. 17, 2020. https://www.nature.com/articles/s41746-020-0221-y.pdf
S. A. Rabbani, M. El-Tanani, S. Sharma, S. S. Rabbani, Y. El-Tanani, R. Kumar, and M. Saini, “Generative artificial intelligence in healthcare: Applications, implementation challenges, and future directions,” BioMedInformatics, vol. 5, no. 3, p. 37, 2025. https://www.mdpi.com/2673-7426/5/3/37
P. K. R. Prakashkumar, “Analytical Study of Vertex–Oracle Cloud Integration in Enterprise ERP Systems for Automated Tax Compliance and Efficient Financial Closings,” International Journal of Accounting and Management Sciences, vol. 4, no. 4, 2025. https://scipubhouse.com/international-journal-of-accounting-and-management-sciences-ijams/content/ijams-vol-4-no-4-october-2025/analytical-study-of-vertex-oracle-cloud-integration-in-enterprise-erp-systems-for-automated-tax-compliance-and-efficient-financial-closings/
P. Lewis, E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, et al., “Retrieval-augmented generation for knowledge-intensive NLP tasks,” in Advances in Neural Information Processing Systems, vol. 33, pp. 9459–9474, 2020. https://proceedings.neurips.cc/paper/2020/file/6b493230205f780e1bc26945df7481e5-Paper.pdf
L. M. Amugongo, P. Mascheroni, S. Brooks, S. Doering, and J. Seidel, “Retrieval augmented generation for large language models in healthcare: A systematic review,” PLOS Digital Health, vol. 4, no. 6, p. e0000877, 2025. https://journals.plos.org/digitalhealth/article/file?id=10.1371/journal.pdig.0000877&type=printable
K. D. Pagana, T. J. Pagana, and T. N. P. Bullen, Mosby’s Manual of Diagnostic and Laboratory Tests. Elsevier Health Sciences, 2021. https://books.google.com/books?hl=en&lr=&id=laQsEAAAQBAJ&oi=fnd&pg=PP1&dq=The+symptoms,+risk+factors,+lab+test+results,+and+comorbidities+are+also+processed,+and+differential+diagnoses+for+conditions+are+provided+in+line+with+the+guidelines+to+support+diagnosis.&ots=oV31_zhTYV&sig=9Ch-gkLZjyx3k0aSlGlak4y-g3Q
P. Fritz, A. Kleinhans, R. Raoufi, A. Sediqi, N. Schmid, S. Schricker, et al., “Evaluation of medical decision support systems (DDX generators) using real medical cases of varying complexity and origin,” BMC Medical Informatics and Decision Making, vol. 22, no. 1, p. 254, 2022. https://link.springer.com/content/pdf/10.1186/s12911-022-01988-2.pdf
F. F. Alu and S. Oluwadare, “An auditable and source-verified framework for clinical AI decision support: Integrating retrieval-augmented generation with data provenance,” Frontiers in Artificial Intelligence, vol. 9, p. 1737532, 2026. https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2026.1737532/pdf
L. Huang, W. Yu, W. Ma, W. Zhong, Z. Feng, H. Wang, et al., “A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions,” ACM Transactions on Information Systems, vol. 43, no. 2, pp. 1–55, 2025. https://dl.acm.org/doi/pdf/10.1145/3703155
S. Pretorius, V. Bartle, S. Bellass, E. R. Blackburn, H. Bold, R. Cooper, et al., “Making clinical guidelines work for people with multiple long term conditions: Analysis and recommendations from review of single condition guidelines,” BMJ Medicine, vol. 5, no. 1, p. e001495, 2026. https://pmc.ncbi.nlm.nih.gov/articles/PMC12933760/
Sardana, K. S. Chadha, and S. Durgam, “Advancing Healthcare Technology Through Secure Data Systems,” 2025. https://doi.org/10.12732/ijam.v38i10s.1138
S. Bedi, Y. Liu, L. Orr-Ewing, D. Dash, S. Koyejo, A. Callahan, et al., “Testing and evaluation of health care applications of large language models: A systematic review,” JAMA, vol. 333, no. 4, pp. 319–328, 2025. https://pmc.ncbi.nlm.nih.gov/articles/PMC11480901/
S. K. Vishwakarma, “AI-driven predictive risk modelling for aerospace supply chains,” International Journal of Innovation in Business & Economics and Applied Journal, 2025. https://www.iibajournal.org/index.php/iibeaj/article/view/64
S. Rangu and K. S. Chadha, “Cloud-powered healthcare & insurance transformation with CRM and advanced analytics,” International Journal of Intelligent Systems and Applications in Engineering, 2025. https://www.ijisae.org/index.php/IJISAE/article/view/7915
G. Lichtner, B. S. Alper, C. Jurth, C. Spies, M. Boeker, J. J. Meerpohl, and F. von Dincklage, “Representation of evidence-based clinical practice guideline recommendations on FHIR,” Journal of Biomedical Informatics, vol. 139, p. 104305, 2023. https://www.sciencedirect.com/science/article/pii/S1532046423000266
S. K. RaoVanama, “AI-Augmented CI/CD Pipeline Optimization for Scalable Cloud-Native Deployment,” International Journal of Artificial Intelligence, Data Science, and Machine Learning, vol. 5, no. 4, pp. 175–187, 2024. https://ijaidsml.org/index.php/ijaidsml/article/download/368/338
N. Khalid, A. Qayyum, M. Bilal, A. Al-Fuqaha, and J. Qadir, “Privacy-preserving artificial intelligence in healthcare: Techniques and applications,” Computers in Biology and Medicine, vol. 158, p. 106848, 2023. https://www.sciencedirect.com/science/article/pii/S001048252300313X
B. J. Wells, H. M. Nguyen, A. McWilliams, M. Pallini, A. Bovi, A. Kuzma, et al., “A practical framework for appropriate implementation and review of artificial intelligence (FAIR-AI) in healthcare,” npj Digital Medicine, vol. 8, no. 1, p. 514, 2025. https://www.nature.com/articles/s41746-025-01900-y.pdf
A. Ubale, “Beyond Telematics: Leveraging Generative AI for Synthetic Accident Reconstruction and Liability Attribution in Autonomous Vehicle Claims,” International Journal of AI, BigData, Computational and Management Studies, vol. 4, no. 4, pp. 119–124, 2023. https://ijaibdcms.org/index.php/ijaibdcms/article/download/356/352
Y. Lu, X. Zhao, and J. Wang, “ClinicalRAG: Enhancing clinical decision support through heterogeneous knowledge retrieval,” in Proceedings of the 1st Workshop on Towards Knowledgeable Language Models (KnowLLM 2024), Aug. 2024, pp. 64–68. https://aclanthology.org/2024.knowllm-1.6.pdf
G. Mallardi, F. Calefato, L. Quaranta, and F. Lanubile, “Integrating AI into healthcare systems: A multivocal literature review,” Smart Health, vol. 39, p. 100631, 2026. https://www.sciencedirect.com/science/article/pii/S2352648325000923
K. S. Chadha, P. R. Vennamaneni, and J. Sardana, “Leveraging Advanced Analytics and AI-Powered Solutions for Transforming Healthcare and Retail E-Commerce: Enhancing Data Security, Personalization, and Compliance,” International Journal of Applied Mathematics, vol. 38, no. 9s, pp. 1310–1334, 2025. https://doi.org/10.12732/ijam.v38i9s.862
D. Van Veen, C. Van Uden, L. Blankemeier, J. B. Delbrouck, A. Aali, C. Bluethgen, et al., “Adapted large language models can outperform medical experts in clinical text summarization,” Nature Medicine, vol. 30, no. 4, pp. 1134–1142, 2024. https://pmc.ncbi.nlm.nih.gov/articles/PMC11479659/
A. J. Holmgren, C. A. Sinsky, L. Rotenstein, and N. C. Apathy, “National comparison of ambulatory physician electronic health record use across specialties,” Journal of General Internal Medicine, vol. 39, no. 14, pp. 2868–2870, 2024. https://link.springer.com/content/pdf/10.1007/s11606-024-08930-4.pdf
S. Rangu, “Analyzing the Impact of AI-Powered Call Center Automation on Operational Efficiency in Healthcare,” JISEM Journal, 2025. https://www.jisem-journal.com/index.php/journal/article/view/8901
J. Sohn, Y. Park, C. Yoon, S. Park, H. Hwang, M. Sung, et al., “Rationale-guided retrieval augmented generation for medical question answering,” in Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1, Apr. 2025, pp. 12739–12753. https://aclanthology.org/2025.naacl-long.635.pdf
C. Wu, P. Qiu, J. Liu, H. Gu, N. Li, Y. Zhang, et al., “Towards evaluating and building versatile large language models for medicine,” npj Digital Medicine, vol. 8, no. 1, p. 58, 2025. https://www.nature.com/articles/s41746-024-01390-4
A. Ramaswamy, A. Tyagi, H. Hugo, J. Jiang, P. Jayaraman, M. Jangda, et al., “Chatgpt Health performance in a structured test of triage recommendations,” Nature Medicine, vol. 32, no. 5, pp. 1671–1675, 2026. https://www.nature.com/articles/s41591-026-04297-7_reference.pdf
K. S. Chadha, “Zero-Trust Data Architecture for Multi-Hospital Research: HIPAA-Compliant Unification of EHRs, Wearable Streams, and Clinical Trial Analytics,” IJCESEN, 2025. https://ijcesen.com/index.php/ijcesen/article/view/3477
S. Liu, A. B. McCoy, and A. Wright, “Improving large language model applications in biomedicine with retrieval-augmented generation: A systematic review, meta-analysis, and clinical development guidelines,” Journal of the American Medical Informatics Association, vol. 32, no. 4, pp. 605–615, 2025. https://academic.oup.com/jamia/article-pdf/32/4/605/61442713/ocaf008.pdf
V. Mehta, A. Komanduri, R. S. Bhadouriya, V. Mehta, M. D. Johnson, P. Shrestha, et al., “Evaluating transparency in AI/ML model characteristics for FDA-reviewed medical devices,” npj Digital Medicine, vol. 8, no. 1, p. 673, 2025. https://www.nature.com/articles/s41746-025-02052-9.pdf
J. G. You, T. Hernandez-Boussard, M. A. Pfeffer, A. Landman, and R. G. Mishuris, “Clinical trials informed framework for real world clinical implementation and deployment of artificial intelligence applications,” npj Digital Medicine, vol. 8, no. 1, p. 107, 2025. https://www.nature.com/articles/s41746-025-01506-4.pdf
N. Kumar, “Edge Computing Patterns for Real-Time Order Flow Optimization,” International Journal of Applied Mathematics, 2025. https://doi.org/10.12732/ijam.v38i12s.1494
T. D. Azad, H. M. Krumholz, and S. Saria, “Principles to guide clinical AI readiness and move from benchmarks to real-world evaluation,” Nature Medicine, pp. 1–3, 2026. https://www.nature.com/articles/s41591-025-04198-1
J. C. Kwong, L. Erdman, A. Khondker, M. Skreta, A. Goldenberg, M. D. McCradden, et al., “The silent trial: The bridge between bench-to-bedside clinical AI applications,” Frontiers in Digital Health, vol. 4, p. 929508, 2022. https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2022.929508/pdf
G. Xiong, Q. Jin, Z. Lu, and A. Zhang, “Benchmarking retrieval-augmented generation for medicine,” in Findings of the Association for Computational Linguistics: ACL 2024, Aug. 2024, pp. 6233–6251. https://aclanthology.org/2024.findings-acl.372.pdf
H. Smith, “Clinical AI: Opacity, accountability, responsibility and liability,” AI & Society, vol. 36, no. 2, pp. 535–545, 2021. https://link.springer.com/content/pdf/10.1007/s00146-020-01019-6.pdf
R. Hariharan, “Zero Trust Security in Multi-Tenant Cloud Environments,” JISEM Journal, 2025. https://www.jisem-journal.com/index.php/journal/article/view/8899
K. Lekadir, A. F. Frangi, A. R. Porras, B. Glocker, C. Cintas, C. P. Langlotz, et al., “FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare,” BMJ, vol. 388, p. e081554, 2025. https://www.bmj.com/content/bmj/388/bmj-2024-081554.full.pdf
S. Samala, “Architecting Multi-Instance Jira Deployments: Scalability Challenges for Global Enterprises,” International Journal of Engineering Applications, 2025. https://gprjournals.org/journals/index.php/ijea/article/view/353
Most read articles by the same author(s)
- Mohammed Imran Choudhary, AI-Augmented Network-Forensics: Leveraging LLMs for Real-Time Threat Detection and Automated Response in Enterprise Environments , International Journal of Advanced Artificial Intelligence Research: Vol. 3 No. 09 (2026): Volume 03 Issue 09
- Sashank Siwakoti, Bhaskar Chaganti, Human-in-the-Loop Control Planes for Cortex Agents: Policy-Driven Escalation, Approval, and Evidence Capture , International Journal of Advanced Artificial Intelligence Research: Vol. 3 No. 09 (2026): Volume 03 Issue 09
- Priya Sharma, A Data-Centric Approach to Transforming Digital Retail Through Artificial Intelligence-Based Shopping Systems , International Journal of Advanced Artificial Intelligence Research: Vol. 3 No. 09 (2026): Volume 03 Issue 09
- Sonam Kumari, Enhancing Clinical Decision-Making Using Generative AI-Powered Knowledge Retrieval Systems: A Review of Emerging Approaches and Challenges , International Journal of Advanced Artificial Intelligence Research: Vol. 3 No. 08 (2026): Volume 03 Issue 08
- Suprajyotsna Dasari , Automated Testing Techniques for Enterprise Software Systems with GenAI Integration , International Journal of Advanced Artificial Intelligence Research: Vol. 3 No. 09 (2026): Volume 03 Issue 09
- Nguyen Minh Anh, Tran Quoc Bao, Unsupervised Learning Framework for Country Clustering Based on Agricultural Import Patterns , International Journal of Advanced Artificial Intelligence Research: Vol. 3 No. 09 (2026): Volume 03 Issue 09
- Amit Kumar Dhariwal, Comprehensive Study on the Use of Artificial Intelligence to Minimize Bias in Healthcare Succession Management , International Journal of Advanced Artificial Intelligence Research: Vol. 3 No. 08 (2026): Volume 03 Issue 08
- Severov Arseni Vasilievich, Artyom V. Smirnov, Architecting Real-Time Risk Stratification in the Insurance Sector: A Deep Convolutional and Recurrent Neural Network Framework for Dynamic Predictive Modeling , International Journal of Advanced Artificial Intelligence Research: Vol. 2 No. 10 (2025): Volume 02 Issue 10
- Dr. Amit Jain, A Comprehensive Survey of Recent Advances Artificial Intelligence for Insurance Fraud Detection , International Journal of Advanced Artificial Intelligence Research: Vol. 3 No. 08 (2026): Volume 03 Issue 08
- Nimal Perera, Anjali Fernando, Robust Browser Fingerprinting Under Adversarial Conditions: An AI-Driven Detection and Defense Architecture , International Journal of Advanced Artificial Intelligence Research: Vol. 3 No. 09 (2026): Volume 03 Issue 09
Similar Articles
- Dr. Khalid Al-Harbi, Dr. Noor Al-Mazrouei, Analyzing Transparency in Prediction Approaches for Power Regulation Trading Systems , International Journal of Advanced Artificial Intelligence Research: Vol. 3 No. 04 (2026): Volume 03 Issue 04
- Dr. Amir Hosseini, A Intelligent Edge-Cloud Integration for Resilient and Real-Time AI Decision Systems , International Journal of Advanced Artificial Intelligence Research: Vol. 3 No. 08 (2026): Volume 03 Issue 08
- Marko Petrovic, Intelligent Failure Prediction Techniques for Modern Electricity Distribution Infrastructure , International Journal of Advanced Artificial Intelligence Research: Vol. 3 No. 03 (2026): Volume 03 Issue 03
- Dr. Ethan Michael Laurent, Next Generation Resource Scheduling Architecture via Neural Computing Based Forecast Models , International Journal of Advanced Artificial Intelligence Research: Vol. 3 No. 01 (2026): Volume 03 Issue 01
- Takumi Suzuki, Mio Tanaka, Scalability Constraints in AI-Driven Construction Management: Opportunities for Robotics and LLM Integration , International Journal of Advanced Artificial Intelligence Research: Vol. 3 No. 08 (2026): Volume 03 Issue 08
- John M. Davenport, AI-AUGMENTED FRAMEWORKS FOR DATA QUALITY VALIDATION: INTEGRATING RULE-BASED ENGINES, SEMANTIC DEDUPLICATION, AND GOVERNANCE TOOLS FOR ROBUST LARGE-SCALE DATA PIPELINES , International Journal of Advanced Artificial Intelligence Research: Vol. 2 No. 08 (2025): Volume 02 Issue 08
- Dr. Sunita Dixit, Early Warning Systems for Traffic Accidents Using Predictive Machine Learning Models , International Journal of Advanced Artificial Intelligence Research: Vol. 3 No. 07 (2026): Volume 03 Issue 07
- Dr. Eleni Markou, Narrative Intelligence In The Age Of Generative Ai: Integrating Computational Storytelling, Transformer Architectures, Ethical Governance, And Consumer Impact , International Journal of Advanced Artificial Intelligence Research: Vol. 3 No. 03 (2026): Volume 03 Issue 03
- Dr. Ali Hosseini, Deep Convolutional Neural Network-Based Adaptive Chatbot Framework for Personalized Educational Support in Autism Spectrum Disorder , International Journal of Advanced Artificial Intelligence Research: Vol. 3 No. 06 (2026): Volume 03 Issue 06
- Nethika Perera, Kavindu Jayasinghe, Dynamic Risk-Based Access Control for Autonomous Agentic AI Systems: Architecture, Policy Enforcement, and Security Evaluation , International Journal of Advanced Artificial Intelligence Research: Vol. 3 No. 09 (2026): Volume 03 Issue 09
You may also start an advanced similarity search for this article.