RESEARCH & DEVELOPMENT
INFORMATION TECHNOLOGY
ARTIFICIAL INTELLIGENCE IN PHARMACY PRACTICE Artificial intelligence and machine learning-based technologies have the potential to transform healthcare by deriving new and important insights from the vast amount of data generated during the delivery of healthcare every day. The challenges for clinical pharmacy practice include discovering how to apply these technologies in ways that reveal new patterns in health data that make a real difference for patients. Josep M Guiu Segura, Dr Pharmacy department, Catalan Health and Social Care Consortium
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rtificial Intelligence (AI) and machine learning-based technologies have the potential to transform healthcare because they offer new and important insights derived from
the vast amount of data generated during the delivery of healthcare every day. The capacity of AI to learn from real-world feedback and improve its performance makes this technology uniquely suited
as Software as a Medical Device (SaMD) and is responsible for it being a rapidly expanding area of research and development. Clinical pharmacy practice may undergo major change due to the implementation of this technology. The challenges facing clinical pharmacists include discovering how to apply AI technologies in ways that reveal new patterns in health data that make a real difference to clinical practice. AI is expected to assist healthcare professionals in enhancing patient experiences and health outcomes, augmenting the health of the population, reducing costs and improving the interventions that pharmacists and other providers instigate with patients. Thereby, the use of AI could help clinical staff to provide more informed medication-use decisions and improve outcomes. However, healthcare professionals must ensure that there is evidence indicating that any new SaMD to be implemented and all AI implementations are safe and effective before they are put into use in practice. Whether or not a Health Technology Assessment (HTA) process is performed, pharmacists clearly play a critical role in helping to generate the evidence that is needed to inform decisions concerning how and when to implement AI on a widespread basis in routine clinical pharmacy practice.
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