AI-Enabled Pill Inspection in Medication Packaging
Client
Leading Pharmacy Automation Provider
Region
North America
Industry
Healthcare & Pharma
Completed
25 Nov 2024
Executive Summary
Project overview and client background
The client, a leading player in pharmacy automation, partnered with Finarb to address the persistent challenge of detecting broken or damaged pills prior to the packaging process. Finarb developed and deployed a deep learning-based image classification model that accurately distinguishes between intact and broken pills.
The solution achieved over 99.8% classification accuracy and operated with minimal latency, automating what was previously a manual, error-prone step. By catching issues upstream—before the pills were even packaged—the solution minimized downstream disruptions, prevented ripple effects such as batch recalls or manual rechecks, strengthened regulatory compliance, and elevated patient safety.
Challenge
The critical problem we needed to solve
Patient Safety Concerns
Pill breakage or damage prior to the packaging process can compromise dosage accuracy, introduce contamination risks, and ultimately affect patient safety. Despite using advanced robotics in pharmacy automation, physical damage to pills—such as chipping, cracking, or fragmentation—remained a persistent challenge.
Operational Challenges
Manual inspection burden: Identifying broken pills required visual scrutiny by pharmacists or technicians, adding to their workload.
False alerts: Existing rule-based systems often flagged pills incorrectly due to lighting issues or pill orientation, increasing review time.
Scalability limitations: As prescription volumes increased, consistent manual inspection was unsustainable.
Results
Measurable impact and outcomes
AI-Powered Innovation
This AI-powered enhancement transformed a previously manual and error-prone process into an automated, scalable, and efficient solution for damaged pill detection and classification. The solution featured a binary image classification model, high-resolution training dataset with thousands of labeled pill images, real-time inference engine designed to detect pill damage under 2 seconds per pouch image, and advanced augmentation strategies to ensure model reliability across real-world conditions. The model was optimized for edge compatibility to run on the client's existing hardware infrastructure.
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