AI-Assisted Multi-Disease Diagnosis System for Remote Rural Clinics

Abstract

Rural communities often face critical challenges in accessing timely and accurate healthcare due to a shortage of medical professionals, limited diagnostic infrastructure, and geographical barriers. This paper presents the design and implementation of an AI-assisted multi-disease diagnosis system tailored for remote rural clinics, enabling frontline health workers to provide preliminary diagnostic support with minimal training.The proposed system integrates machine learning algorithms and rule-based expert systems to analyze symptoms, vital signs, and optional inputs such as medical images.

Introduction

The paper presents an AI-Assisted Multi-Disease Diagnosis System tailored for remote rural clinics to aid frontline health workers in early and accurate disease detection. The system uses machine learning models combining symptom data, vital signs, and optional diagnostic images to provide real-time preliminary diagnoses and referral guidance. It is designed with a lightweight, offline-capable architecture and supports multilingual, voice-based interfaces to accommodate users with limited digital skills.

Key features include:

• Multi-disease detection (e.g., TB, diabetes, malaria, anemia, respiratory infections)

• Hybrid machine learning models (Random Forest, XGBoost for symptoms; CNN for images) trained on diverse public datasets

• Cross-platform mobile/web deployment optimized for low-resource settings

• Offline-first operation with secure local data storage and periodic cloud syncing

Evaluation and field trials showed:

• Overall multi-label diagnostic accuracy of ~87.5% across diseases

• High usability with 94% user satisfaction among rural health workers

• Significant reduction (~40%) in diagnosis and triage time

• Robust performance even with intermittent connectivity

The system effectively bridges the rural-urban healthcare gap by empowering frontline workers with AI tools, improving access and reducing diagnostic delays. It offers advantages over existing solutions by supporting multiple diseases, offline use, and localized interfaces. Limitations include some false positives in overlapping symptom cases, and scope for expanding pediatric/maternal health modules.

Conclusion

This paper presents a novel AI-Assisted Multi-Disease Diagnosis System tailored for remote rural clinics, addressing long-standing challenges in accessibility, diagnostic accuracy, and healthcare delivery in under-resourced regions. By combining machine learning models with a user-friendly, multilingual mobile application, the system empowers frontline health workers to perform preliminary disease assessments with confidence and efficiency.Through comprehensive evaluation using multi-source datasets and real-world field deployment, the system demonstrated an overall diagnostic accuracy of 87.5%, strong user acceptance, and operational effectiveness in offline, low-connectivity environments. The inclusion of both symptom-based inputs and image analysis enhances the system’s versatility and real-world utility.

References

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Copyright

Copyright © 2025 V. Chandrika, V. Satish Kumar, V. Vivek Reddy, V. Prasanna, T. Madhavi Latha, Mrs. M. Rupasri. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.