Performance evaluation of a prescription medication image classification model: an observational cohort

Abstract

Technology assistance of pharmacist verification tasks through the use of machine intelligence has the potential to detect dangerous and costly pharmacy dispensing errors. National Drug Codes (NDC) are unique numeric identifiers of prescription drug products for the United States Food and Drug Administration. The physical form of the medication, often tablets and capsules, captures the unique features of the NDC product to help ensure patients receive the same medication product inside their prescription bottle as is found on the label from a pharmacy. We report and evaluate using an automated check to predict the shape, color, and NDC for images showing a pile of pills inside a prescription bottle. In a test set containing 65,274 images of 345 NDC classes, overall macro-average precision was 98.5%. Patterns of incorrect NDC predictions based on similar colors, shapes, and imprints of pills were identified and recommendations to improve the model are provided.

Clinical Decision Support System Based on Hybrid Knowledge Modeling: A Case Study of Chronic Kidney Disease-Mineral and Bone Disorder Treatment

“…Deep learning-based approaches are generally employed for processing unstructured data, such as medication images and clinical texts, for the purpose of correctly identifying medication information. To reduce medication identification errors by the patients, deep learning-based techniques are leveraged that aid in prescription pill identification from mobile images [ 21 , 22 , 23 , 24 , 25 ]. Similarly, deep learning techniques are also successfully applied to the task of medication and dosage extraction from clinical texts, such as clinical notes [ 26 , 27 , 28 ] and social media texts [ 29 , 30 , 31 ].…”

Automated medication verification system (AMVS): System based on edge detection and CNN classification drug on embedded systems

“…Hence, the development of Automated Dispensing Cabinets (ADCs) has been proposed. These cabinets autonomously dispense medications or medical supplies, opening specific compartments to retrieve the correct items, thereby reducing the likelihood of medication errors [ [4] , [5] , [6] ]. Jaw-Horng Liou et al implemented ADCs at the Taichung Veterans General Hospital in Taiwan, where pharmacists place medications in the cabinets upon receiving physician medication orders.…”

Pharmacists’ Trust in Automated Pill Recognition Technology: The Role of Presenting AI Uncertainty Information (Preprint)

“…The advent of deep learning has further enhanced the capabilities of automated pill recognition systems [4,23]. For instance, Larios Delgado et al Despite the impressive strides in model accuracy, realizing the potential of these technologies is only possible if people establish appropriate trust in them.…”