This paper is published in Volume-11, Issue-6, 2025
Area
Artificial Intelligence In Healthcare , Natural Language Processing , Human-Computer Interaction
Author
Mansi Yadav, Mohammad Faizan, Nitin Singh Thakur, Neha Kumari
Org/Univ
Oriental Institute of Science and Technology, Madhya Pradesh, India
Pub. Date
26 November, 2025
Paper ID
V11I6-1197
Publisher
Keywords
Healthcare Access, Rural Healthcare, Doctor Recommendation System, Distil BERT, Natural Language Processing, Symptom Classification, Real-time Alert System, Digital Prescription.

Citationsacebook

IEEE
Mansi Yadav, Mohammad Faizan, Nitin Singh Thakur, Neha Kumari. Tackling Rural Healthcare Gaps: Intelligent Doctor Recommendation by Distil BERT with Coordinated Medicine Delivery, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Mansi Yadav, Mohammad Faizan, Nitin Singh Thakur, Neha Kumari (2025). Tackling Rural Healthcare Gaps: Intelligent Doctor Recommendation by Distil BERT with Coordinated Medicine Delivery. International Journal of Advance Research, Ideas and Innovations in Technology, 11(6) www.IJARIIT.com.

MLA
Mansi Yadav, Mohammad Faizan, Nitin Singh Thakur, Neha Kumari. "Tackling Rural Healthcare Gaps: Intelligent Doctor Recommendation by Distil BERT with Coordinated Medicine Delivery." International Journal of Advance Research, Ideas and Innovations in Technology 11.6 (2025). www.IJARIIT.com.

Abstract

In rural healthcare, patients often struggle to navigate complex medical systems, leading to misdirected consultations and treatment delays. Traditional paper-based clinics further exacerbate inefficiencies with crowded queues and poor emergency handling. Current digital solutions typically offer only basic appointment booking or generic disease prediction, failing to provide personalized guidance or integrate the complete patient journey. Our AI-powered platform addresses these gaps using a fine-tuned Distil BERT model that analyses patient-described symptoms and recommends appropriate medical specialities with 83.8% accuracy. The system seamlessly integrates intelligent doctor matching with a token-based queue management system, real-time emergency SOS alerts, and digital prescription generation. This creates a comprehensive, patient-centric workflow from initial symptom assessment through treatment completion. Future enhancements will incorporate multi-lingual support, voice-input capabilities, pharmacy integration, and telemedicine modules to further expand accessibility and create a complete healthcare ecosystem for underserved communities.