
International Journal For Multidisciplinary Research
E-ISSN: 2582-2160
•
Impact Factor: 9.24
A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal
Home
Research Paper
Submit Research Paper
Publication Guidelines
Publication Charges
Upload Documents
Track Status / Pay Fees / Download Publication Certi.
Editors & Reviewers
View All
Join as a Reviewer
Get Membership Certificate
Current Issue
Publication Archive
Conference
Publishing Conf. with IJFMR
Upcoming Conference(s) ↓
WSMCDD-2025
GSMCDD-2025
Conferences Published ↓
RBS:RH-COVID-19 (2023)
ICMRS'23
PIPRDA-2023
Contact Us
Plagiarism is checked by the leading plagiarism checker
Call for Paper
Volume 7 Issue 2
March-April 2025
Indexing Partners



















Genai-Powered Digital Twins For Chronic Disease Management
Author(s) | Nirup Kumar Reddy Pothireddy |
---|---|
Country | United States |
Abstract | Real-time simulations for chronic disease management can be achieved through digital twin technology when combined with generative AI. A research project investigates the development of AI-created physiological and behavioral patterns replicating digital twins for chronic disease patients which predict their disease progression under different treatment approaches. Laboratory and predictive modeling with machine learning allows physicians to assess in advance how different medication adjustments along with lifestyle changes and surgical interventions will affect heart failure cases as well as lung ailment patients. Virtual models that use AI and are designed for individual needs help medical staff make better choices and design improved treatment plans together with decreased hospital visits because the models detect medical problems before they happen. This research evaluates the possible difficulties associated with digital twins driven by GenAI technology which relate to data safety and calculating power requirements and ethical considerations. The research reveals that digital twins controlled by AI lead to superior medical care because they supply dynamic adaptive treatments supported by evidence. The research ends by recognizing that General Artificial Intelligence-generated digital twin systems show enormous potential for chronic disease care reform but additional research must address both precision in modeling and privacy protection and ease of clinical implementation. |
Keywords | Generative AI, Digital Twin Technology, Chronic Disease Management, Predictive Healthcare Analytics, Personalized Medicine |
Field | Computer > Artificial Intelligence / Simulation / Virtual Reality |
Published In | Volume 7, Issue 2, March-April 2025 |
Published On | 2025-04-07 |
DOI | https://doi.org/10.36948/ijfmr.2025.v07i02.40878 |
Short DOI | https://doi.org/g9dm9w |
Share this

E-ISSN 2582-2160

CrossRef DOI is assigned to each research paper published in our journal.
IJFMR DOI prefix is
10.36948/ijfmr
Downloads
All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.
