ASPG Menu
search

American Scientific Publishing Group

verified Journal

International Journal of Advances in Applied Computational Intelligence

ISSN
Online: 2833-5600
Frequency

Continuous publication

Publication Model

Open access journal. All articles are freely available online with no APC.

Submit Manuscript send
International Journal of Advances in Applied Computational Intelligence
Full Length Article

Volume 8 • Issue 1 • PP: 33–41 • 2026

Agentic Generative AI Framework for Intelligent Disease Prediction and Clinical Decision-Making in Smart Healthcare

S. Phani Praveen 1*
mail
,
Massila Kamalrudin 2
mail
,
Sai Vellela 3
mail
,
Deshinta Arrova Dewi 4
mail
,
Dedeepya Pulletikurthy 5
mail
,
Vahiduddin Shariff 6
mail
1Associate Professor, Department of Computer Science and Engineering, Prasad V. Potluri Siddhartha Institute of Technology, Kanuru, Vijayawada – 520007, Andhra Pradesh, India
2Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka (UTeM), Melaka, Malaysia
3Associate Professor, Department of CSE – Data Science, Chalapathi Institute of Technology, Guntur – 522016, Andhra Pradesh, India
4Professor, Faculty of Data Science and Information Technology (FDSIT), INTI International University, Malaysia
5Department of Computer Science & Engineering, SRM University AP, Amaravati, Andhra Pradesh, India
6Department of CSE, Sir C. R. Reddy College of Engineering, Eluru, Andhra Pradesh, India
* Corresponding Author.
DOI https://doi.org/10.54216/IJAACI.080105
format_quote Cite this article
Received: January 05, 2026 Revised: February 10 2026 Accepted: March 19, 2026
PDF Icon View PDF open_in_new

Abstract

Rapid growth in the adoption of Electronic Health Records (EHRs), Internet of Medical Things (IoMT) devices, wearable sensor technology, and digital healthcare systems offers immense scope for intelligent healthcare decision support. However, most AI-enabled healthcare systems in use today still lack explainability, contextual reasoning capabilities, and effective decision-making. For these reasons, this research develops an Agentic Generative AI Framework for Intelligent Disease Prediction and Decision-Making in smart healthcare. The framework incorporates predictive analytics, Generative AI-based clinical reasoning, and autonomous intelligent agents into a coherent healthcare framework. Six specific agents are used for data gathering, data analysis, disease prediction, clinical reasoning, treatment recommendations, and patient monitoring. The combined functionality of these agents supports disease prediction, clinical reasoning, and personalized treatment plans. Evaluation was performed on healthcare datasets related to heart disease, diabetes, chronic kidney disease, and breast cancer. Experimental results show high efficiency, stable accuracy across diseases, reliable recommendation generation, and enhanced healthcare intelligence compared with traditional ML, DL, and LLM methods. Results show that combining Agentic AI with Generative AI increases explainability, adaptability, and efficiency in medical decision support. The proposed model represents an encouraging path toward intelligent, patient-centered, and explainable smart healthcare systems.

Keywords

Agentic Artificial Intelligence Generative Artificial Intelligence Disease Prediction Clinical Decision Support Explainable AI Smart Healthcare

References

[1] Singhal, K., Azizi, S., Tu, T., Mahdavi, S. S., Wei, J., Chung, H. W., et al. (2023). Large language models encode clinical knowledge. Nature, 620(7972), 172– 180.

[2] Singhal, K., Tu, T., Gottweis, J., Sayres, R., Wulczyn, E., Amin, M., et al. (2025). Toward expert-level medical question answering with large language models. Nature Medicine, 31(3), 943–950.

[3] Maity, S., and Saikia, M. J. (2025). Large language models in healthcare and medical applications: a review. Bioengineering, 12(6), 631.

[4] Scott, I. A., Reddy, S., Kelly, T., Miller, T., and Van Der Vegt, A. (2025). Using generative artificial intelligence in clinical practice: a narrative review and proposed agenda for implementation. Medical Journal of Australia, 223(11), 664–672.

[5] Rabbani, S. A., El-Tanani, M., Sharma, S., Rabbani, S. S., El-Tanani, Y., Kumar, R., and Saini, M. (2025). Generative artificial intelligence in healthcare: applications, implementation challenges, and future directions. BioMedInformatics, 5(3), 37.

[6] Gaber, F., Shaik, M., Allega, F., Bilecz, A. J., Busch, F., Goon, K., et al. (2025). Evaluating large language model workflows in clinical decision support for triage, referral, and diagnosis. NPJ Digital Medicine, 8(1), 263.

[7] Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56.

[8] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., et al. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24–29.

[9] Rajpurkar, P., Chen, E., Banerjee, O., and Topol, E. J. (2022). AI in health and medicine. Nature Medicine, 28(1), 31–38.

[10] Beam, A. L., and Kohane, I. S. (2018). Big data and machine learning in health care. JAMA, 319(13), 1317 1318.

[11] Miotto, R., Wang, F., Wang, S., Jiang, X., and Dudley, J. T. (2018). Deep learning for healthcare: review, opportunities and challenges. Briefings in Bioinformatics, 19(6), 1236–1246.

[12] Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., et al. (2017). Artificial intelligence in healthcare: past, present and future. Stroke and Vascular Neurology, 2(4).

[13] Yu, K. H., Beam, A. L., and Kohane, I. S. (2018). Artificial intelligence in healthcare. Nature Biomedical Engineering, 2(10), 719–731.

[14] Shortliffe, E. H., and Sepulveda, M. J. (2018). Clinical decision support in the era of artificial intelligence. JAMA, 320(21), 2199–2200.

[15] Sutton, R. T., Pincock, D., Baumgart, D. C., Sadowski, D. C., Fedorak, R. N., and Kroeker, K. I. (2020). An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ Digital Medicine, 3(1), 17.

[16] Holzinger, A., Langs, G., Denk, H., Zatloukal, K., and Muller, H. (2019). Causability and explainability of artificial intelligence in medicine. WIREs Data Mining and Knowledge Discovery, 9(4), e1312.

[17] Samek, W., Montavon, G., Vedaldi, A., Hansen, L. K., and Muller, K. R. (Eds.). (2019). Explainable AI: Interpreting, Explaining and Visualizing Deep Learning. Springer Nature.

[18] Helen, D., and Suresh, N. V. (2024). Generative AI in healthcare: opportunities, challenges, and future perspectives. In Revolutionizing the Healthcare Sector with AI (pp. 79–90).

[19] Nori, H., King, N., McKinney, S. M., Carignan, S., and Horvitz, E. (2023). Capabilities of GPT-4 on medical challenge problems. arXiv preprint, arXiv:2303.13375.

[20] Chen, Y., Huang, D., Shen, Y., Xu, H., Sheng, L., Zhou, Q., andWang, K. (2025). MedCT: a clinical terminology graph for generative AI applications in healthcare. In International Conference on Computational Science (pp. 24–38). Cham: Springer Nature Switzerland.

[21] Shimgekar, S. R., Vassef, S., Goyal, A., Saha, K., Zonooz, P., and Kumar, N. (2025). Agentic AI framework for end-to-end medical data inference. In 2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 7807–7810). IEEE.

[22] Srinivasu, P. N., Aruna Kumari, G. L., Ahmed, S., and Alhumam, A. (2026). Exploring agentic AI in healthcare: a study on its working mechanism. Frontiers in Medicine, 12, 1753443.

[23] Zhao, L., Liu, S., Xin, T., Tan, J., Wang, X., Li, Y., et al. (2026). AI agent in healthcare: applications, evaluations, and future directions. NPJ Artificial Intelligence, 2(1), 31.

[24] Liu, Y., Carrero, Z. I., Jiang, X., Ferber, D., Wolflein, G., Zhang, L., et al. (2026). Benchmarking large language model-based agent systems for clinical decision tasks. NPJ Digital Medicine.

[25] Garza, L., Kotal, A., Grasso, M. A., and Umucu, E. (2025). Retrieval-Augmented Framework for LLMBased Clinical Decision Support. arXiv preprint, arXiv:2510.01363.

[26] Sidhu, R. S., Farwaha, H. S., Kumar, R., Bains, P. S., Prakash, C., Kumar, R., Chowdhury, S., and Chan, C. K. (2024). Comparative study of metallic foil friction stir welded and conventional friction stir welded AZ61 Mg alloy butt joints: biomedical and engineering applications. AIP Advances, 14(4), 045209.

[27] Alolayyan, M. N., et al. (2025). The mediating role of medical service geographical availability between healthcare service quality and medical insurance. In A. Hannoon and A. Mahmood (Eds.), Intelligence-Driven Circular Economy (Studies in Computational Intelligence, Vol. 1174). Cham: Springer.

[28] Mohammad, A. A. S., et al. (2026). Medical power electronics: innovations, applications, and challenges. In M. Alsyasneh and J. Masih (Eds.), Artificial Intelligence for Agile Business Solutions (Studies in Systems, Decision and Control, Vol. 633). Cham: Springer.

[29] Praveen, S. P., Hasan, M. K., Abdullah, S. N. H. S., Sirisha, U., Tirumanadham, N. S. K. M. K., Islam, S., et al. (2024). Enhanced feature selection and ensemble learning for cardiovascular disease prediction: hybrid GOL2-2T and adaptive boosted decision fusion with babysitting refinement. Frontiers in Medicine, 11, 1407376.

[30] Praveen, S. P., Sandeep, K., Sai, N. R., Sharma, A., Pandey, J., and Chouhan, V. (2024). Outlier management and its impact on diabetes prediction: a voting ensemble study. Journal of Intelligent Systems and Internet of Things, 12(1), 08–19.

[31] Praveen, S. P., Ghasempoor, H., Shahabi, N., and Izanloo, F. (2023). A hybrid gravitational emulation local search-based algorithm for task scheduling in cloud computing. Mathematical Problems in Engineering, 2023, 6516482.

[32] Praveen, S. P., Sidharth, S. R., Priya, T. K., Kavuri, Y. S., Sindhura, S. M., and Donepudi, S. (2023). ResNet and ResNeXt-powered kidney tumor detection: a robust approach on a subset of the KAUH dataset. In 2023 2nd International Conference on Automation, Computing and Renewable Systems (ICACRS) (pp. 749–757). IEEE.

[33] Chowdary, N. S., Kadiyala, S., Jyothi, V. E., Srinandan, P., Praveen, S. P., and Prakash, P. B. (2025). Identity and proxy orientation based remote data integration checking and uploading in public clouds. In 2025 3rd International Conference on Communication, Security, and Artificial Intelligence (ICCSAI) (pp. 1–5). IEEE.

Cite This Article

Choose your preferred format

format_quote
Praveen, S. Phani, Kamalrudin, Massila, Vellela, Sai , Dewi, Deshinta Arrova, Pulletikurthy, Dedeepya , Shariff, Vahiduddin. "Agentic Generative AI Framework for Intelligent Disease Prediction and Clinical Decision-Making in Smart Healthcare." International Journal of Advances in Applied Computational Intelligence, vol. Volume 8 , no. Issue 1, 2026, pp. 33–41. DOI: https://doi.org/10.54216/IJAACI.080105
Praveen, S., Kamalrudin, M., Vellela, S., Dewi, D., Pulletikurthy, D., Shariff, V. (2026). Agentic Generative AI Framework for Intelligent Disease Prediction and Clinical Decision-Making in Smart Healthcare. International Journal of Advances in Applied Computational Intelligence, Volume 8 (Issue 1), 33–41. DOI: https://doi.org/10.54216/IJAACI.080105
Praveen, S. Phani, Kamalrudin, Massila, Vellela, Sai , Dewi, Deshinta Arrova, Pulletikurthy, Dedeepya , Shariff, Vahiduddin. "Agentic Generative AI Framework for Intelligent Disease Prediction and Clinical Decision-Making in Smart Healthcare." International Journal of Advances in Applied Computational Intelligence Volume 8 , no. Issue 1 (2026): 33–41. DOI: https://doi.org/10.54216/IJAACI.080105
Praveen, S., Kamalrudin, M., Vellela, S., Dewi, D., Pulletikurthy, D., Shariff, V. (2026) 'Agentic Generative AI Framework for Intelligent Disease Prediction and Clinical Decision-Making in Smart Healthcare', International Journal of Advances in Applied Computational Intelligence, Volume 8 (Issue 1), pp. 33–41. DOI: https://doi.org/10.54216/IJAACI.080105
Praveen S, Kamalrudin M, Vellela S, Dewi D, Pulletikurthy D, Shariff V. Agentic Generative AI Framework for Intelligent Disease Prediction and Clinical Decision-Making in Smart Healthcare. International Journal of Advances in Applied Computational Intelligence. 2026;Volume 8 (Issue 1):33–41. DOI: https://doi.org/10.54216/IJAACI.080105
S. Praveen, M. Kamalrudin, S. Vellela, D. Dewi, D. Pulletikurthy, V. Shariff, "Agentic Generative AI Framework for Intelligent Disease Prediction and Clinical Decision-Making in Smart Healthcare," International Journal of Advances in Applied Computational Intelligence, vol. Volume 8 , no. Issue 1, pp. 33–41, 2026. DOI: https://doi.org/10.54216/IJAACI.080105
Digital Archive Ready