Abstract: Identifying prescribed medication accurately remains a challenge for many people, particularly older individuals who may experience medication errors due to impaired vision, lack of English proficiency, or other disabilities. This problem is more prevalent in healthcare settings where pills are often distributed in strips rather than in traditional packaging, increasing the risk of dangerous consequences. To address this issue, a mobile application has been developed using Computer Vision and Artificial Intelligence to accurately recognize pills and provide relevant information through text and speech formats. The approach integrates the GPT-4 API for imprint extraction and YOLOv8 for image detection, significantly enhancing the application's accuracy. The goal is to improve medication management for vulnerable populations facing unique accessibility challenges. The application has achieved an overall accuracy of 90.89%, demonstrating its effectiveness in assisting users to identify and manage their medication.

Taif Alahmadi, Rana Alsaedi, Ameera Alfadli, Ohoud Alzubaidi and Afnan Aldhahri. “A Computer Vision-Based Pill Recognition Application: Bridging Gaps in Medication Understanding for the Elderly”. International Journal of Advanced Computer Science and Applications (IJACSA) 15.7 (2024). http://dx.doi.org/10.14569/IJACSA.2024.0150768

@article{Alahmadi2024,
title = {A Computer Vision-Based Pill Recognition Application: Bridging Gaps in Medication Understanding for the Elderly},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150768},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150768},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {7},
author = {Taif Alahmadi and Rana Alsaedi and Ameera Alfadli and Ohoud Alzubaidi and Afnan Aldhahri}
}