DIGITAL TRANSFORMATION IN PHARMACY: ARTIFICIAL INTELLIGENCE AND THE FUTURE
Artificial intelligence (AI) holds the potential for a profound transformation in healthcare services. By leveraging technologies such as big data analytics, machine learning, and natural language processing, AI can significantly improve clinical decision-making processes through more accurate predictions, early interventions, and personalized approaches to diagnostics and treatments.
In pharmacy, AI also offers transformative potential. From drug development to patient counseling, inventory management, and the personalization of treatment strategies, AI technologies present opportunities to enhance efficiency, reliability, and patient-focused approaches in the sector.
Across the globe, AI is rapidly being integrated into healthcare and is becoming increasingly prevalent in the field of pharmacy.
What is artificial intelligence?
Artificial Intelligence (AI) is a branch of computer science and engineering aimed at replicating certain aspects of human intelligence through machines. This discipline enables computer systems to perceive their environment, make decisions based on those perceptions, and solve previously unprogrammed problems through reasoning or learning from past experiences. In simpler terms, AI involves designing methods and technologies that allow computers to understand complex tasks, reason, make predictions, and learn new information, mimicking human cognitive abilities.
Key components of AI include: Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), Computer Vision, Robotics and Recommendation Systems.
How does artificial intelligence work?
AI systems analyze large volumes of data statistically, identifying patterns to make predictions, decisions, or inferences. During the training phase, the system corrects its errors using data samples, enabling it to recognize similar patterns in new situations and produce appropriate responses. In essence, AI establishes meaningful connections from data, solves problems, and improves performance over time.
Different AI approaches are applied based on the problem’s nature, such as:
Machine Learning: Models that work with labeled data (supervised learning), unlabeled data (unsupervised learning), or reinforcement-based learning.
Deep Learning: Models based on artificial neural networks, particularly effective for handling complex data structures.
The choice of model or architecture depends on the problem type and requirements.
Digital transformation in pharmacy
Digital transformation is critical in healthcare for streamlining service delivery and reducing error rates. In pharmacy, digitalization promises immense potential. This extends beyond traditional practices, offering operational improvements via automation, real-time data analytics for better decisions, and AI-based disease models to optimize clinical research. These advancements can lead to significant improvements in patient experiences, pharmacy operations, and drug development processes.
Smart inventory management
Large pharmacy chains like Walgreens in the U.S. utilize AI-based inventory management systems to predict the demand for specific medications during certain periods. These systems prevent overstocking, optimize supply chains, and reduce costs. They can also predict when patients will run out of their regular medications and automatically suggest refills.
AI technologies track product shelf life and report this data to pharmacists. For instance, systems that identify near-expiry products can notify pharmacists, enabling strategies like promotions to minimize waste, reduce inventory costs, and shape future purchases.
Patient safety
Patient safety is a top priority in pharmacy. Ensuring that medications are taken in the correct dose, at the right time, and in the appropriate form is critical for effective treatment, quality of life, and health economics. AI has the potential to analyze prescription and medication history data multidimensionally, identifying potential drug interactions, inappropriate dosages, or treatment options unsuitable for a patient's medical history early on.
Implementing such technologies can minimize human error risks, allowing healthcare professionals to focus on more complex cases. For instance, AI systems like MedEye, developed in Europe, aim to enhance safety and efficiency in hospital medication management. MedEye tracks medication administration in real time, identifies risky drug interactions, and alerts healthcare professionals in case of discrepancies.
Molecular discoveries
AI’s significant contribution to drug discovery lies in deeply understanding disease mechanisms and identifying optimal drug targets. Traditional methods for disease etiology discovery involve lengthy, costly, and complex laboratory experiments and clinical trials. In contrast, AI-based approaches enable holistic analysis of genetic, proteomic, metabolomic, and even epigenetic data, providing faster and more accurate insights into the molecular structure of diseases.
By scanning vast datasets, mapping molecular interaction networks, and integrating diverse biological markers, AI highlights critical molecules associated with diseases. Researchers can then focus on these “target molecules,” identify new drug candidates earlier, and conduct more targeted laboratory studies, reducing R&D costs and accelerating clinical readiness.
Platforms like Insilico Medicine utilize deep learning techniques to identify new drug targets and significantly shorten preliminary research phases, processing billions of data points across various types of information.
Automation and robotic technologies
,AI-powered pharmacy robots have the potential to revolutionize daily medication preparation processes. Traditional methods of preparing prescriptions and ensuring the correct dose, brand, and formulation are time-consuming and prone to error. Automation-based systems retrieve electronic prescription data and efficiently package the correct medications in precise quantities.
Since these tasks are completed in minimal time, pharmacists can dedicate more attention to patient interaction, medication consultation, clinical advising, or assessing drug interactions.
Artificial intelligence in Türkiye
With the "National Artificial Intelligence Strategy 2021-2025" Turkey has planned significant investments to integrate AI into healthcare. However, for widespread adoption, regulations must be implemented swiftly, with heightened sensitivity toward personal data security.
Regulation and data security
For AI to be effectively utilized, protecting personal data and establishing ethical guidelines are crucial. Faulty datasets can trigger incorrect decisions, potentially causing unforeseen problems in healthcare. Thus, regulatory measures should adopt solutions aligned with scientific and ethical principles.
Misconceptions
-AI always makes the right decision: AI can make mistakes. Like human learning, AI improves by learning from its errors. Therefore, healthcare professionals’ oversight and final decision-making are vital.
"If a machine is expected to be infallible, it cannot also be intelligent." – Alan Turing
-AI will make pharmacists unnecessary: AI is designed to support humans, not replace them. Instead of fearing AI, we should focus on how it can accelerate and enhance our work.
Looking ahead
AI offers the potential to shape the future of pharmacy with its capabilities. With emerging technologies, personalized treatments and smart systems will become widespread, improving patients' quality of life. New standards will emerge in patient monitoring, inventory tracking, and automated prescription management. In the future, both patients and pharmacists will benefit from a more effective and efficient healthcare system.
Conclusion
AI in pharmacy encompasses a wide range of applications, from molecular drug discovery to inventory management. Correct implementation of these technologies reduces error rates and delivers safer and more effective healthcare services. The future is clear: AI-supported systems will make the sector significantly more innovative.
Yarının Teknolojik Vizyonu ve Yapay Zekâ, Demirezen, M. Umut, Editor, Nobel Kitap Yayınevi. (2023).
A Closer Look at Walgreens’ AI-Powered Demand Planning Transformation (2023). https://www.zebra.com/gb/en/blog/posts/2023/closer-look-at-walgreens-ai-powered-demand-planning-transformation.html
Tergooi Hospital has chosen MedEye to strengthen medication safety (2018). https://medeye.com/news/2018/7/5/tergooi-has-chosen-medeye-to-strengthen-medication-safety
Quicker Cures: How Insilico Medicine Uses Generative AI to Accelerate Drug Discovery (2023). https://blogs.nvidia.com/blog/insilico-medicine-uses-generative-ai-to-accelerate-drug-discovery/
Drug dispensing goes digital (2021). https://www.pharmaceutical-technology.com/features/robotic-drug-dispensing-digital-pharmacy/
National Artificial Intelligence Strategy 2021 - 2025 - Türkiye (2021) https://cbddo.gov.tr/SharedFolderServer/Genel/File/TRNationalAIStrategy2021-2025.pdf
