Agentic Generative AI Framework for Intelligent Disease

Prediction and Clinical Decision-Making in Smart Healthcare

Surapaneni Phani Praveen1,* Massila Kamalrudin2 Sai Srinivas Vellela3

Deshinta Arrova Dewi4 Dedeepya Pulletikurthy5 Vahiduddin Shariff6

1 Associate Professor, Department of Computer Science and Engineering, Prasad V. Potluri Siddhartha Institute of Technology,

Kanuru, Vijayawada – 520007, Andhra Pradesh, India

2 Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka (UTeM), Melaka, Malaysia

3 Associate Professor, Department of CSE – Data Science, Chalapathi Institute of Technology, Guntur – 522016, Andhra Pradesh,

India

4 Professor, Faculty of Data Science and Information Technology (FDSIT), INTI International University, Malaysia

5 Department of Computer Science & Engineering, SRM University AP, Amaravati, Andhra Pradesh, India

6 Department of CSE, Sir C. R. Reddy College of Engineering, Eluru, Andhra Pradesh, India

Received: January 05, 2026 Revised: February 10 2026 Accepted: March 19, 2026 ⋆ Corresponding author

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

1. INTRODUCTION

The healthcare industry has undergone a revolution in its

use of technology with the introduction of Electronic Health

Records (EHRs), Internet of Medical Things (IoMT) devices,

wearables, medical imaging equipment, and cloud computing

platforms. These systems produce large amounts of health