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Possibilities of artificial intelligence in the development of antihypertensive herbal preparations

https://doi.org/10.59598/ME-2305-6053-2025-114-1-137-140

Abstract

In the modern pharmaceutical industry, the application of artificial intelligence significantly enhances the process of drug development. Recent studies have revealed that artificial intelligence can notably improve the effectiveness of hypertension treatment by optimizing the compositions of herbal blends.

This study proposes three unique formulations of blends developed using the ChatGPT neural network. The three devised blend compositions are formulated based on the analysis of plant data, their interactions, and treatment effectiveness. Each composition is meticulously selected to ensure maximum efficacy and minimal side effects. These innovative approaches have the potential to significantly enhance patients' quality of life by providing them with more effective and safer means for treating hypertension and other cardiovascular conditions. This progress opens up new perspectives for future medical development, emphasizing the significance of the symbiosis between technology and medical practice.

About the Authors

A. S. Turekhanova
South Kazakhstan Medical Academy
Kazakhstan

Aruzhan Sabitkyzy Turekhanova 

160019, Shymkent, Al-Farabi Square, 1/1



Zh. S. Toksanbaeva
South Kazakhstan Medical Academy
Kazakhstan

160019, Shymkent, Al-Farabi Square, 1/1



A. G. Ibragimova
South Kazakhstan Medical Academy
Kazakhstan

160019, Shymkent, Al-Farabi Square, 1/1



S. A. Syzdykova
South Kazakhstan Medical Academy
Kazakhstan

160019, Shymkent, Al-Farabi Square, 1/1



M. M. Kulbayeva
South Kazakhstan Medical Academy
Kazakhstan

160019, Shymkent, Al-Farabi Square, 1/1



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For citations:


Turekhanova A.S., Toksanbaeva Zh.S., Ibragimova A.G., Syzdykova S.A., Kulbayeva M.M. Possibilities of artificial intelligence in the development of antihypertensive herbal preparations. Medicine and ecology. 2025;(1):137-141. (In Russ.) https://doi.org/10.59598/ME-2305-6053-2025-114-1-137-140

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ISSN 2305-6045 (Print)
ISSN 2305-6053 (Online)