Integrating AI-Driven Triage into Digital Pharmacy Systems for Rational Antibiotic Use in Low-Resource Settings.

Antimicrobial resistance (AMR) is becoming a bigger threat to global health, especially in low and middle-income countries (LMICs) where antibiotics are often given out without a prescription. This study presents a hybrid digital Artificial Intelligence(AI) framework aimed at improving rational antibiotic utilization by integrating pharmacies, telemedicine practitioners, and policymakers via an integrated digital infrastructure. The digital intervention system was implemented in 28 community pharmacies and showed significant improvements in how people used antibiotics. For example, the percentage of people who self-medicated with antibiotics dropped from 4.05% to 0.86%, and the percentage of people who bought antibiotics with a prescription jumped from 27.6% to 43.5%. We propose an Artificial Intelligence(AI) framework that combines a machine learning (ML)-based and a large language model (LLM)-based symptom checker for intelligent triage and clinical decision support, based on this foundation. These models will allow for efficient analysis of both structured and narrative symptom data, which will help pharmacists give advice and doctors refer patients right away. The suggested model demonstrates a scalable, data-driven, and human-in-the-loop approach to antibiotic stewardship, with the potential to significantly enhance AMR mitigation and healthcare accessibility in LMIC settings.