Current - Issue
Original Article
Calibrated Multi-Evidence Fusion Framework for Clinical Decision Support Systems
Addakula Chaithanya1
Dr. Chandra Sekhar Sanaboina2
1 PG Scholar, Department of Computer Science and Engineering, UCEK, JNTUK, Kakinada, Andhra Pradesh, India. 2 Assistant Professor, Department of Computer Science and Engineering, UCEK, JNTUK, Kakinada, Andhra Pradesh, India.
Published Online: May-June 2026
Pages: 59-68
Cite this article
↗ https://www.doi.org/10.59256/ijire.20260703007
References
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using the BioBERT Models,” in Proceedings of the 2022 IEEE 4th Eurasia Conference on Biomedical Engineering, Healthcare and
Sustainability, ECBIOS 2022, Institute of Electrical and Electronics Engineers Inc., 2022, pp. 221–224.
doi:10.1109/ECBIOS54627.2022.9945029.
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13, pp. 36162–36174,2025,doi:10.1109/ACCESS.2025.3544096.
[22]. A. N. Saritha, G. Kalnoor, P. R. Bhat, P. Anantha Rao, P. Prajwal, and P. T. Pradeep, “Alleviate: An AI-Powered Mobile Application
for Personalized Healthcare Management and Doctor Recommendation,” IEEE Access, vol. 13, pp. 215822–215832, 2025, doi:
10.1109/ACCESS.2025.3647089.
[23]. P. Wang, W. Lu, C. Lu, R. Zhou, M. Li, and L. Qin, “Large Language Model for Medical Images: A Survey of Taxonomy, Systematic
Review, and Future Trends,” Big Data Mining and Analytics, vol. 8, no. 2, pp. 496–517, 2025,doi:10.26599/BDMA.2024.9020090.
[24]. M. Nadas, L. Diosan, and A. Tomescu, “Synthetic Data Generation Using Large Language Models: Advances in Text and Code,”
IEEE Access, vol. 13, pp. 134615–134633, 2025, doi: 10.1109/ACCESS.2025.3589503.
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Augmented Generation (RAG) Technologies,” IEEE Access, vol. 13, pp. 201246–201263, 2025, doi:
10.1109/ACCESS.2025.3636437.
[26]. A. Abdulnazar, R. Roller, S. Schulz, and M. Kreuzthaler, “Large Language Models for Clinical Text Cleansing Enhance Medical
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Intensive Care Unit,” IEEE Trans. Eng. Manag., vol. 70, no. 8, pp. 2931–2943, Aug. 2023, doi: 10.1109/TEM.2022.3195813.
[28]. S. Vatsal, H. Dubey, and A. Singh, “Agentic AI in Healthcare & Medicine: A Seven-Dimensional Taxonomy for Empirical Evaluation
of LLM-based Agents,” 2026, Institute of Electrical and Electronics Engineers Inc.doi:10.1109/ACCESS.2026.3651218.
[29]. M. S. Hajar, H. K. Kalutarage, and M. O. Al-Kadri, “3R: A reliable multi agent reinforcement learning based routing protocol for
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and deep learning models for medical imaging,” European Journal of Radiology Artificial Intelligence, vol. 4, p. 100044, Dec. 2025,
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[34]. M. F. Yatchang et al., “High-throughput screening for the identification of dual inhibitors of BRD4 and RIPK3 toward the
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machine learning for critical care units,” IEEE Access, vol. 8, pp. 185676–185687, 2020, doi: 10.1109/ACCESS.2020.3030031.
[2]. M. K. Ogirala, R. Tallapaneni, S. M. Chalamcharla, and A. Chinta, “A Medical Diagnosis and Treatment Recommendation Chatbot
using MLP,” in Proceedings of the 2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023,
Institute of Electrical and Electronics Engineers Inc., 2023, pp. 495–500. doi: 10.1109/ICAAIC56838.2023.10141211.
[3]. G. Rajani and K. Ruparel, “Deep Learning based Chatbot Architecture for Medical Diagnosis and Treatment Recommendation,” in
Proceedings of 3rd International Conference on Advanced Computing Technologies and Applications, ICACTA 2023 , Institute of
Electrical and Electronics Engineers Inc., 2023. doi: 10.1109/ICACTA58201.2023.10392702.
[4]. M. Kilinc, F. Gurcan, and A. Soylu, “LLM-Based Generative AI in Medicine: Analysis of Current Research Trends With BERTopic,”
IEEE Access, vol. 13, pp. 157026–157043, 2025, doi: 10.1109/ACCESS.2025.3606335.
[5]. E. B. M. Salim, T. Anass, and A. I. Abdelouahed, “Advancing Multilingual Retrieval-Augmented Generation for Reliable Medication
Counseling,” IEEE Access, vol. 13, pp. 215550–215564, 2025, doi: 10.1109/ACCESS.2025.3646941.
[6]. S. D. Bhavani Peri, S. Santhanalakshmi, and R. Radha, “Chatbot to chat with medical books using Retrieval-Augmented Generation
Model,” in Proceedings of NKCon 2024 - 3rd Edition of IEEE NKSS’s Flagship International Conference: Digital Transformation:
Unleashing the Power of Information, Institute of Electrical and Electronics Engineers Inc., 2024.
doi:10.1109/NKCon62728.2024.10774900.
[7]. S. Liang, K. Xu, and Z. Dong, “A Multi-Agent Approach to Modeling Task-Oriented Dialog Policy Learning,” IEEE Access, vol. 13,
pp. 11754–11764, 2025, doi: 10.1109/ACCESS.2025.3529469.
[8]. A. Sheikh and E. K. P. Chong, “Advancing AIoMT-Enabled Healthcare System-of-Systems Using Multi-Agent Reinforcement
Learning,” IEEE Access, vol. 13, pp. 142674–142694, 2025, doi: 10.1109/ACCESS.2025.3596921.
[9]. W. Li et al., “CARE: A clinical agentic reasoning engine to enhance real-World diagnostic accuracy via structured medical
reasoning,” Expert Syst. Appl., 2026, doi: 10.1016/j.eswa.2026.131476.
[10]. Z. Walji, R. Badhwar, P. Dave, D. Choi, S. Virani, and J. Park, “Multi-Agent System for Emergent Care (MAS-EC): Focused on
Workload, Efficiency, and User Experience,” Human Factors in Healthcare, p. 100133, Mar. 2026, doi: 10.1016/j.hfh.2026.100133.
[11]. F. Liu et al., “A foundational architecture for AI agents in healthcare,” Oct. 21, 2025. doi: 10.1016/j.xcrm.2025.102374.
[12]. J. Yang, L. Shu, H. Duan, and H. Li, “RDguru: A Conversational Intelligent Agent for Rare Diseases,” IEEE J. Biomed. Health
Inform., vol. 29, no. 9, pp. 6366–6378, 2025, doi: 10.1109/JBHI.2024.3464555.
[13]. S. Saadaoui and E. Alonso, “Coordinated LLM multi-agent systems for collaborative question-answer generation,” Knowl. Based.
Syst., vol. 330, Nov. 2025, doi: 10.1016/j.knosys.2025.114627.
[14]. S. Yoon, “Strategic Learning Under Linguistic and Contextual Constraints: A Theoretical Framework for LLM-Based Multi-Agent
Coordination,” IEEE Access, vol. 13, pp. 197053–197071, 2025, doi: 10.1109/ACCESS.2025.3628927.
[15]. S. Ouali and S. El Garouani, “MedQA-MA: A Moroccan Arabic medical question-answering dataset for virtual healthcare assistants
and large language models,” Data Brief, vol. 65, Apr. 2026, doi: 10.1016/j.dib.2026.112537.
[16]. M. A. Zuluaga, I. Išgum, and M. B. Cuadra, “Trustworthy AI in Medical Image Analysis: A Unified Perspective Built on Robustness
and Layers of Trust,” Curr. Opin. Biomed. Eng., p. 100649, Mar. 2026, doi: 10.1016/j.cobme.2026.100649.[17]. Ç. U. Öğdü, K. Arslanoğlu, and M. Karaköse, “An Adaptive Multi-Agent LLM-Based Clinical Decision Support System Integrating
Biomedical RAG and Web Intelligence,” IEEE Access, vol. 13, pp. 167390–167404, 2025, doi: 10.1109/ACCESS.2025.3613340.
[18]. N. B. Mahiddin, Z. A. Othman, A. A. Bakar, and N. A. A. Rahim, “An Interrelated Decision-Making Model for an Intelligent Decision
Support System in Healthcare,” IEEE Access, vol. 10, pp. 31660–31676, 2022, doi: 10.1109/ACCESS.2022.3160725.
[19]. S. Sharif and M. R. Akbarzadeh-T, “Distributed Probabilistic Fuzzy Rule Mining for Clinical Decision Making,” Fuzzy Information
and Engineering, vol. 13, no. 4, pp. 436–459,2021,doi:10.1080/16168658.2021.1978803.
[20]. N. Y. Tung, H. W. Hu, T. W. Chang, Y. M. Hu, and H. M. Lin, “Multi-model Comparison for Classification of Medical Records
using the BioBERT Models,” in Proceedings of the 2022 IEEE 4th Eurasia Conference on Biomedical Engineering, Healthcare and
Sustainability, ECBIOS 2022, Institute of Electrical and Electronics Engineers Inc., 2022, pp. 221–224.
doi:10.1109/ECBIOS54627.2022.9945029.
[21]. K. Saleem et al., “Multi-Agent-Based Cognitive Intelligence in Non-Linear Mental Healthcare-Based Situations,” IEEE Access, vol.
13, pp. 36162–36174,2025,doi:10.1109/ACCESS.2025.3544096.
[22]. A. N. Saritha, G. Kalnoor, P. R. Bhat, P. Anantha Rao, P. Prajwal, and P. T. Pradeep, “Alleviate: An AI-Powered Mobile Application
for Personalized Healthcare Management and Doctor Recommendation,” IEEE Access, vol. 13, pp. 215822–215832, 2025, doi:
10.1109/ACCESS.2025.3647089.
[23]. P. Wang, W. Lu, C. Lu, R. Zhou, M. Li, and L. Qin, “Large Language Model for Medical Images: A Survey of Taxonomy, Systematic
Review, and Future Trends,” Big Data Mining and Analytics, vol. 8, no. 2, pp. 496–517, 2025,doi:10.26599/BDMA.2024.9020090.
[24]. M. Nadas, L. Diosan, and A. Tomescu, “Synthetic Data Generation Using Large Language Models: Advances in Text and Code,”
IEEE Access, vol. 13, pp. 134615–134633, 2025, doi: 10.1109/ACCESS.2025.3589503.
[25]. E. A. Olca, “Professor X: Diagnosis and Treatment of Dermatological Diseases by Integration of Visual Diagnosis and Retrieval-
Augmented Generation (RAG) Technologies,” IEEE Access, vol. 13, pp. 201246–201263, 2025, doi:
10.1109/ACCESS.2025.3636437.
[26]. A. Abdulnazar, R. Roller, S. Schulz, and M. Kreuzthaler, “Large Language Models for Clinical Text Cleansing Enhance Medical
Concept Normalization,” IEEE Access, 2024, doi: 10.1109/ACCESS.2024.3472500.
[27]. J. Possik et al., “An Agent-Based Modeling and Virtual Reality Application Using Distributed Simulation: Case of a COVID-19
Intensive Care Unit,” IEEE Trans. Eng. Manag., vol. 70, no. 8, pp. 2931–2943, Aug. 2023, doi: 10.1109/TEM.2022.3195813.
[28]. S. Vatsal, H. Dubey, and A. Singh, “Agentic AI in Healthcare & Medicine: A Seven-Dimensional Taxonomy for Empirical Evaluation
of LLM-based Agents,” 2026, Institute of Electrical and Electronics Engineers Inc.doi:10.1109/ACCESS.2026.3651218.
[29]. M. S. Hajar, H. K. Kalutarage, and M. O. Al-Kadri, “3R: A reliable multi agent reinforcement learning based routing protocol for
wireless medical sensor networks,” Computer Networks, vol. 237, Dec. 2023, doi: 10.1016/j.comnet.2023.110073.
[30]. R. Vashistha et al., “Agent-MIRA: AI-orchestrated medical imaging agent for PET image retrieval and assistance,” Comput. Med.
Imaging Graph., vol. 129, p. 102725, Mar. 2026,doi:10.1016/j.compmedimag.2026.102725.
[31]. Z. Jiang and S. Feng, “UsmleGPT: An AI application for developing MCQs via multi-agent system,” Software Impacts, vol. 23, Mar.
2025, doi: 10.1016/j.simpa.2025.100742.
[32]. 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,” European Journal of Radiology Artificial Intelligence, vol. 4, p. 100044, Dec. 2025,
doi: 10.1016/j.ejrai.2025.100044.
[33]. A. Stranjak and S. Campagna, “Decentralised Agent-Based Medical Image Reconstruction,” in Procedia Computer Science, Elsevier
B.V., 2022, pp. 2106–2115. doi: 10.1016/j.procs.2022.09.270.
[34]. M. F. Yatchang et al., “High-throughput screening for the identification of dual inhibitors of BRD4 and RIPK3 toward the
development of small-molecule medical countermeasure agents against arsenicals,” SLAS Discovery, vol. 35, Sep. 2025, doi:
10.1016/j.slasd.2025.100247
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