ARCHIVES
AI-Driven Treatment Response Prediction for Chronic Disease Management Using Generative Artificial Intelligence and Large Language Models
Published Online: March-April 2026
Pages: 58-65
Cite this article
↗ https://www.doi.org/10.59256/ijrtmr.20260602009Abstract
In the case of chronic management diseases like diabetes, cardiovascular disorders, and cancer, there is a significant challenge of predicting the reaction of patients to medical treatment. Patient health status, medical history, genetic and treatment arrangements create difficulty in the health practitioners determining the most effective treatment to be offered to each individual. The latest developments in Artificial Intelligence (AI) can open new possibilities in terms of using the large amounts of medical data and helping in the clinical decision-making. The proposed project aims at the creation of a smart system where Generative Artificial Intelligence and Large Language Models (LLMs) are applied to forecast patient treatment reactions and help in customized care. The suggested system will challenge structured and unstructured healthcare data, such as establishment health records, lab reports, treatment history, and clinical notes. Identifying treatment outcome patterns based on patient characteristics is done using machine learning and deep learning techniques. Large Language Models are also integrated to analyze and comprehend written medical data including doctor notes and patient reports so that clinical data could be interpreted more accurately. Python is used to implement the system along with deep learning systems such as PyTorch or TensorFlow, and healthcare datasets made publicly available are used in training and evaluation. Some of the standard metrics that are used to evaluate the performance of the system are accuracy, precision, recall, and F1-score in order to determine the effectiveness of prediction. The last system gives insights on whether a patient is likely to respond a given treatment in a positive way to assist medical practitioners give more effective and personalized treatment. The proposed system helps to reveal the potential of intelligent healthcare technologies by allowing them to enhance the planning of the treatment and supporting the personalized medicine through the combination of Generative AI, predictive analytics, and natural language processing.
Related Articles
2026
A Strategic Framework for Depth-Dependent Hydroelectric Conversion along the Indian Coastline
2026
Reimagining Development in India: A Critical Analysis of the Viksit Bharat Vision
2026
AI-Enabled Image Description: Bridging the Gap for the Visually Impaired
2026
Perceived Occupational Risks of Emergency Medical Services Personnel
2026
Origin, Growth and recent Development of Integrated Reporting (IR): A theoretical Review
2026