Abstract:
MedScan is an AI-based mobile application designed to enhance medication safety and dermatological
diagnostics in resource-constrained contexts, particularly in Sri Lanka. It addresses
two critical healthcare challenges: medication errors, mitigated through Optical Character
Recognition (OCR) of drug labels, and timely skin condition detection using Convolutional
Neural Networks (CNNs) with Explainable AI (XAI) techniques, including Grad-CAM. The
application supports multilingual drug information (English, Sinhala, Tamil) and distinguishes
between drug-induced skin reactions and skin cancers. MedScan achieves 93.8% OCR accuracy
and 97.4% classification accuracy via EasyOCR and a fine-tuned DenseNet121. Offline
availability and a user-friendly interface ensure accessibility for elderly and low-literacy users.
User trials showed a 30% reduction in medication errors, 92% user satisfaction, and a 25%
increase in clinician confidence through XAI visualisations. In addition, MedScan is designed
with data privacy in mind; sensitive patient information is processed locally, and any necessary
transmissions are anonymised and encrypted in compliance with GDPR and HIPAA standards.
Despite strong performance across skin tones, initial testing revealed reduced accuracy in darker
skin types. To address this, the dataset was expanded with 500 curated images from Fitzpatrick
IV–VI, resulting in a 4.1% improvement in recall, with ongoing efforts to prioritise skin tone
diversity and minimise algorithmic bias. To the best of our knowledge, MedScan is among
the first multimodal mHealth frameworks that unify OCR for drug safety and CNN-based dermatology
diagnostics within a single application, supported by XAI for transparency. With an
inference time of 0.2 seconds on mobile devices, MedScan provides a scalable, ethical, and
inclusive solution that enhances patient safety, diagnostic equity, and healthcare workflow efficiency.
