ArticleBMC health services research2020
A drug identification model developed using deep learning technologies: experience of a medical center in Taiwan.
Article in BMC health services research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Integration of artificial intelligence applications in clinical pharmacy services: A scoping review.Future healthcare journal · 2026Review
- Image-based identification and DEA-based optimization modeling of antibiotic packaging using unsupervised learning techniques.PloS one · 2026Article
- Analysis of the efficiency and effectiveness of technology transformation through the introduction of ADC in the Emergency Department: a Taiwanese case study.BMC health services research · 2025Article
- Application of Generative Artificial Intelligence Models for Accurate Prescription Label Identification and Information Retrieval for the Elderly in Northern East of Thailand.Journal of imaging · 2025Article
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- Risk Management and Patient Safety in the Artificial Intelligence Era: A Systematic Review.Healthcare (Basel, Switzerland) · 2024Review
- Environmental therapy: interface design strategies for color graphics to assist navigational tasks in patients with visuospatial disorders through an analytic hierarchy process based on CIE color perception.Frontiers in psychology · 2024Article
- A Novel Two-Stage Induced Deep Learning System for Classifying Similar Drugs with Diverse Packaging.Sensors (Basel, Switzerland) · 2023Article
- High accurate and explainable multi-pill detection framework with graph neural network-assisted multimodal data fusion.PloS one · 2023Article
- Multispectral Portable Fibre-Optic Reflectometer for the Classification of the Origin of Chicken Eggshells in the Case ofSensors (Basel, Switzerland) · 2022Article
- Hybrid Method Incorporating a Rule-Based Approach and Deep Learning for Prescription Error Prediction.Drug safety · 2022Article
- A Clinical Decision Support System for Diabetes Patients with Deep Learning: Experience of a Taiwan Medical Center.International journal of medical sciences · 2022Article
- Deep Learning for Classifying Physical Activities from Accelerometer Data.Sensors (Basel, Switzerland) · 2021Article
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5 authors.
Funding
Abstract
backgroundIssuing of correct prescriptions is a foundation of patient safety. Medication errors represent one of the most important problems in health care, with 'look-alike and sound-alike' (LASA) being the lead error. Existing solutions to prevent LASA still have their limitations. Deep learning techniques have revolutionized identification classifiers in many fields. In search of better image-based solutions for blister package identification problem, this study using a baseline deep learning drug identification (DLDI) aims to understand how identification confusion of look-alike images by human occurs through the cognitive counterpart of deep learning solutions and thereof to suggest further solutions to approach them.
methodsWe collected images of 250 types of blister-packaged drug from the Out-Patient Department (OPD) of a medical center for identification. The deep learning framework of You Only Look Once (YOLO) was adopted for implementation of the proposed deep learning. The commonly-used F1 score, defined by precision and recall for large numbers of identification tests, was used as the performance criterion. This study trained and compared the proposed models based on images of either the front-side or back-side of blister-packaged drugs.
resultsOur results showed that the total training time for the front-side model and back-side model was 5 h 34 min and 7 h 42 min, respectively. The F1 score of the back-side model (95.99%) was better than that of the front-side model (93.72%).
conclusionsIn conclusion, this study constructed a deep learning-based model for blister-packaged drug identification, with an accuracy greater than 90%. This model outperformed identification using conventional computer vision solutions, and could assist pharmacists in identifying drugs while preventing medication errors caused by look-alike blister packages. By integration into existing prescription systems in hospitals, the results of this study indicated that using this model, drugs dispensed could be verified in order to achieve automated prescription and dispensing.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.