Introduction
Pharmacies play a vital role in public health and well-being, serving as accessible hubs where healthcare meets everyday life. Healthcare advancements over the past century have significantly increased the production and consumption of medicines by the general public. Coupled with population growth and lifestyle changes, this trend has created an ever-growing need for pharmacies to enhance their efficiency in dispensing both prescribed medications and over-the-counter products like pain relievers, cold remedies, and vitamin supplements.
The Evolution of Pharmacy Automation
As pressure on pharmacies has intensified, the relevance of technology-driven pharmacy automation has grown significantly. Automated systems can streamline tasks, reduce manual errors, and ultimately improve overall efficiency. While pharmacy automation has existed since the 1980s, with early adopters using digital tablet counters to mitigate risks and reduce labor costs[1], these initial solutions were often prohibitively expensive for independent pharmacists.
However, with the advent of computers, the internet, and smartphones in the 21st century, and significant advancements in AI in the last decade, pharmacy automation has entered a new era. These developments have made solutions for applications such as pill counting not only viable but highly effective. AI-driven systems streamline the pill-counting process while improving accuracy and efficiency over time by learning from data. Additionally, they can be enhanced with capabilities like defect detection and pill classification, making them indispensable tools for modern pharmacies.
AI and Computer Vision in Pill Counting
Pill counting and dispensing constitute a significant portion of a pharmacy’s workflow. Traditionally, this task has been performed manually using a pill counting tray and spatula. While these tools are simple and straightforward, the repetitive nature of the task demands sustained attention, making it prone to errors. Moreover, the manual process can lead to fatigue among pharmacy staff, further increasing the risk of mistakes.
A computer vision-based pill counting system offers a streamlined alternative, requiring only a camera and a computational device such as a computer or smartphone. In this setup, the camera captures either still images or a video feed of the pills. An AI algorithm then processes this visual data on the computational device, swiftly analyzing the content to provide the desired output—typically the total number of pills present.
This approach significantly reduces the need for manual counting, leveraging technology to enhance both speed and accuracy in pharmacy operations. The benefits of such systems include:
- Increased accuracy: AI systems can achieve higher accuracy rates than manual counting, reducing dispensing errors. This is crucial for patient safety and inventory management. A recent study[2] found that hand counting was associated with an error rate of 12.6% inaccurate prescriptions, compared to just 4.8% when using automated methods.
- Time savings: Automated counting is significantly faster than manual methods, allowing pharmacists to focus on patient care. The same study [2] revealed that the traditional hand-counting method was 42.3% slower than even a rudimentary handheld counting device.
- Reduced fatigue: By automating repetitive tasks, staff fatigue is minimized, leading to improved overall performance. Research conducted in 2020 [3] found that employees involved in manual pill counting experienced distractions, fatigue, and felt rushed to complete tasks.
- Scalability: These systems can handle varying volumes of pills without a proportional increase in time or effort.
How AI-Based Pill Counters Work
The implementation of AI-based pill counting systems leverages several advanced learning algorithms, particularly deep learning techniques. Here’s a breakdown of the key components:
- Object Detection: Convolutional Neural Networks (CNNs) based architectures like YOLO (You Only Look Once) are often employed for image recognition and object detection. These allow the system to accurately localize individual pills regardless of their shape, orientation, or overlap.
- Segmentation: Methods like the Segment Anything Model (SAM) can be utilized for precise pill segmentation, separating each pill from the background and from other pills.
- Tracking: For video-based counting, object-tracking algorithms such as ByteTrack can be implemented to follow pills as they move through the frame.
- Counting: The process typically involves detecting and segmenting each pill, then employing instance segmentation techniques to distinguish between individual pills, even when they are in contact with each other.
- Visualization: The results are often visualized in real-time, with a dot mark highlighting each detected pill on the video feed or image. A running count is usually displayed prominently, updating in real-time as pills are added or removed from view.
Our in-house AI-powered pill counter in action
Future Scope
The future of AI in pharmacy automation is promising and extends beyond pill counting. Some potential developments include:
- Integration with pharmacy management systems for seamless inventory tracking and order management. Expansion to other pharmacy tasks, such as prescription verification and drug interaction checking.
- Application of AI in personalized medicine, potentially assisting in tailoring medications to individual patient needs.
- Analysis of Pill Quality: Beyond counting and classification, systems can detect inconsistencies or defects in pills ensuring only high-quality medication is dispensed.
Conclusion
As technology continues to evolve, embracing innovations like AI-assisted pill counters will be crucial for pharmacies to stay competitive and provide better service. This trend signals a future where AI becomes an indispensable assistant in various aspects of pharmacy operations, from inventory management to personalized medication recommendations. By adopting these technologies, pharmacies can position themselves at the forefront of healthcare innovation, ultimately leading to improved patient outcomes, reduced errors, and more efficient operations. The integration of AI in pharmacy practices is not just a temporary shift, but a glimpse into the future of healthcare where technology and human expertise work in harmony to provide superior pharmaceutical care.
As a pharmacist or healthcare professional, how do you envision AI and automation shaping the future of pharmacy practice? The transition to these advanced systems may seem daunting, but the potential benefits in terms of accuracy, efficiency, and patient safety make them a compelling consideration for modern pharmacies.
[1] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3304454/
[2] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6892485/
[3] https://academic.oup.com/heapol/article-abstract/35/4/452/5740606
About the authors
Hrishikesh P S is a seasoned Machine Learning professional at Founding Minds with deep roots in the field, having been involved since the early days of deep learning. His expertise in Computer Vision and Deep Learning has grown alongside the rapid evolution of these technologies over the past five years. Hrishikesh specializes in advanced computer vision applications and cutting-edge deep learning techniques, staying at the forefront of artificial intelligence developments. His long-standing experience and adaptability make him a valuable expert in this dynamic and fast-paced field.
Akhil K A is a Machine Learning Engineer at Founding Minds who focuses on AI and ML platform development. His expertise spans vision-based machine learning and Natural Language Processing. Leveraging frameworks like TensorFlow and PyTorch, Akhil creates advanced models and actively participates in ML competitions to remain at the forefront of artificial intelligence advancements. His work combines technical prowess with a commitment to pushing the boundaries of AI technology.
