The Application of Large Language Models on Major Depressive Disorder Support Based on African Natural Products
Fuente:
arXiv
Saved in:
| Main Author: | |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916825453821952 |
|---|---|
| author | Zou, Linyan |
| author_facet | Zou, Linyan |
| contents | Major depressive disorder represents one of the most significant global health challenges of the 21st century, affecting millions of people worldwide and creating substantial economic and social burdens. While conventional antidepressant therapies have provided relief for many individuals, their limitations including delayed onset of action, significant side effects, and treatment resistance in a substantial portion of patients have prompted researchers and healthcare providers to explore alternative therapeutic approaches (Kasneci et al.). African traditional medicine, with its rich heritage of plant-based remedies developed over millennia, offers a valuable resource for developing novel antidepressant treatments that may address some of these limitations. This paper examines the integration of large language models with African natural products for depression support, combining traditional knowledge with modern artificial intelligence technology to create accessible, evidence-based mental health support systems.
The research presented here encompasses a comprehensive analysis of African medicinal plants with documented antidepressant properties, their pharmacological mechanisms, and the development of an AI-powered support system that leverages DeepSeek's advanced language model capabilities. The system provides evidence-based information about African herbal medicines, their clinical applications, safety considerations, and therapeutic protocols while maintaining scientific rigor and appropriate safety standards. Our findings demonstrate the potential for large language models to serve as bridges between traditional knowledge and modern healthcare, offering personalized, culturally appropriate depression support that honors both traditional wisdom and contemporary medical understanding. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_02947 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Application of Large Language Models on Major Depressive Disorder Support Based on African Natural Products Zou, Linyan Computation and Language Neurons and Cognition Major depressive disorder represents one of the most significant global health challenges of the 21st century, affecting millions of people worldwide and creating substantial economic and social burdens. While conventional antidepressant therapies have provided relief for many individuals, their limitations including delayed onset of action, significant side effects, and treatment resistance in a substantial portion of patients have prompted researchers and healthcare providers to explore alternative therapeutic approaches (Kasneci et al.). African traditional medicine, with its rich heritage of plant-based remedies developed over millennia, offers a valuable resource for developing novel antidepressant treatments that may address some of these limitations. This paper examines the integration of large language models with African natural products for depression support, combining traditional knowledge with modern artificial intelligence technology to create accessible, evidence-based mental health support systems. The research presented here encompasses a comprehensive analysis of African medicinal plants with documented antidepressant properties, their pharmacological mechanisms, and the development of an AI-powered support system that leverages DeepSeek's advanced language model capabilities. The system provides evidence-based information about African herbal medicines, their clinical applications, safety considerations, and therapeutic protocols while maintaining scientific rigor and appropriate safety standards. Our findings demonstrate the potential for large language models to serve as bridges between traditional knowledge and modern healthcare, offering personalized, culturally appropriate depression support that honors both traditional wisdom and contemporary medical understanding. |
| title | The Application of Large Language Models on Major Depressive Disorder Support Based on African Natural Products |
| topic | Computation and Language Neurons and Cognition |
| url | https://arxiv.org/abs/2507.02947 |