AI-Driven Healthcare Delivery in Pakistan: A Framework for Systemic Improvement

Imama Zahoor, Shiza Ihtsham, Muhammad Umar Ramzan, Agha Ali Raza, Basmaa Ali

Abstract: In Low- and Middle-Income Countries (LMICs), poor health outcomes come from a high burden of disease, a shortage of healthcare professionals, and inefficient health information exchange leading to substantial economic losses. In this paper, we highlight critical gaps in healthcare delivery in Pakistan and propose solutions to improve patient outcomes in resource-constrained environments. We have built Darcheeni, an AI-driven healthcare framework that leverages artificial intelligence to assist and supplement physicians, streamline healthcare processes, and prioritize patient-centered care. Darcheeni analyzes doctor-patient interactions in real-time, integrates lab and imaging data, generates and distributes care plans customized to the patient’s needs, and sends them directly to patients’ smartphones. We also discuss the challenges and limitations associated with sustainable AI integration by centering our learnings from the pilot deployment of Darcheeni. By focusing on Pakistan as a case study, this work offers practical insights and strategies for deploying AI-driven technologies sustainably in similar resource-constrained environments and contributes to the broader discourse on the role of AI in global health improvement.