EasyPill: AI-Powered Pill Identification for Enhancing Medication Safety and Reducing Errors by Using Mobilenet CNNs
Year: 2025
Type: article
Abstract: Medication errors are a significant concern in healthcare, often arising from difficulties in identifying unlabeled pills. Patients, caregivers, and healthcare professionals need reliable tools for accurate pill identification to ensure safe medication practices. EasyPill aims to simplify pharmaceutical pill identification with high accuracy and precision by leveraging advanced AI technologies. The system addresses issues such as medication errors, unlabeled pills, and the need for efficient drug verification. EasyPill employs MobileNet, a Convolutional Neural Networks (CNNs) architecture, to analyze pill images uploaded by users based on attributes like color, shape, and size. These features are then matched against a comprehensive drug database. Additionally, the platform includes a search engine for direct pill name queries and offers user authentication options—such as account signup and login—to ensure secure access. The system provides detailed information about pills, making it a valuable tool for patients, caregivers, and healthcare professionals. EasyPill promotes health awareness and encourages responsible medication practices by serving as both an identification tool and an educational resource. Through its user-friendly design and reliable identification capabilities, EasyPill enhances public health by addressing key challenges in medication safety. It supports safe medication use while fostering greater health awareness among users.
Source: Journal of Productive Discourse
Authors: Phul Babu Jha, Anjay Kumar Mishra, Raheem Ansari, Suvash Khadka, Binisha Dahal, Shreeja Aryal
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4410497740