I, pharmacist? AI and robot-powered prescriptions 

The growing presence of Artificial Intelligence, especially generative AI, in our personal tasks and routines mirrors its growing adoption and transformative potential in healthcare, particularly within pharmacy. Like most applied AI, irrespective of industry, AI in pharmacy remains in an early stage with incredible promise already taking shape in two areas: medication management and drug development.

Medication management

While AI is relatively new in pharmacy, production automation and robotic processing have been in use for some time. Walgreens currently operates 19 micro-fulfillment centers across the US, 11 of which have been operating since 2022. These robotic facilities can process up to 50,000 orders a day, allowing pharmacists to focus on the more complex components of their work and reduce pharmacy overhead and human error.

While robotic systems might be filling your prescriptions now, it won’t be long before AI systems are also thinking about your medication as well. In many cases, these same AI systems can more accurately dispense and label medications, predict demand or supply fluctuations and even identify potential drug interactions and adverse events from a patient’s medical history or condition. They can sort through data to identify members needing medication refills, and after identification, an AI system might initiate a conversation with a member via reminder texts, calls or emails and edit medical records based on the response.

Drug development

Drug development is a time-consuming process that requires the filtering of enormous amounts of raw data through known scientific and biological testing – just the scenario that AI models can help with. Earlier this year, the FDA released a comprehensive discussion paper to facilitate engagement with stakeholders on the use of AI and machine learning in drug development. These technologies, the paper notes, have the potential to enhance drug development in many ways, such as by speeding the delivery of safe and effective drugs, improving drug safety, broadening access to drugs and increasing the quality of manufacturing.

Initial drug development requires the creation of a suitable biological target (i.e. the ideal patient in whom the medication would achieve acceptable results against a tolerable probability of side effects). A GenAI model would not only analyze traditional data but could also take into account images, audio and related (or unrelated) medical literature. Unsurprisingly, the list of companies currently utilizing or integrating AI in drug development includes familiar names such as Johnson & Johnson, AbbVie, Pfizer and Eli Lilly.

The big picture

Much like AI models, the prescription drug industry is deeply layered and very complex. While some processes within the prescription drug value chain like prescription filling and manufacturing, are more amenable to AI innovation, some areas, including drug development, will need to be more carefully regulated and scrutinized before widespread adoption occurs.

Being served by AI pharmacists seems unlikely anytime soon, but AI-assisted processes have already begun. The future of pharmacy might not be “I, pharmacist” but “We, pharmacist,” as AI-powered pharmacy offers faster processes, fewer errors and more individualized healthcare.