A Clinically Viable Deep Learning System for Pneumonia Screening in Resource-Constrained Settings: Balancing Sensitivity, Specificity, and Deployability (Al-Farooq Journal of Sciences, 2026)
Pneumonia remains a leading cause of mortality worldwide, particularly in low- and middle-income countries where access to radiological expertise is limited. We present a clinically viable deep learning system for automated pneumonia screening from chest X-rays, explicitly designed for deployment in resource-constrained settings. Our approach balances high sensitivity—critical for minimizing missed diagnoses—with sufficient specificity to avoid overburdening fragile health systems. The model leverages a lightweight convolutional architecture optimized for edge-device inference, operates offline, and integrates seamlessly into minimal-infrastructure clinical workflows. Trained on diverse, multi-source datasets and validated on real-world data from rural clinics, the system achieves a sensitivity of 94.2% and specificity of 86.5%, with an AUC of 0.93. Crucially, it maintains robust performance across varying image qualities and demographic groups, demonstrating strong generalizability. Built-in explainability features (e.g., attention heatmaps) support clinician trust and facilitate human-in-the-loop triage. This work demonstrates that rigorously engineered AI tools can deliver both clinical utility and practical deployability—bridging the gap between algorithmic innovation and real-world impact in global health.
Keywords: pneumonia screening, deep learning, chest X-ray, resource-constrained settings, point-of-care diagnostics, lightweight neural networks, medical AI deployment, sensitivity-specificity trade-off, global health, explainable AI