Leveraging Artificial Intelligence and Machine Learning in Pharmaceutical Care: A Systematic Review

Artificial Intelligence (AI) and Machine Learning (ML) are transforming pharmaceutical care by enhancing decision-making, optimizing workflows, and improving patient outcomes. Applied in areas such as medication management, patient monitoring, and personalized treatment plans, these technologies support pharmacists in selecting medications, predicting treatment responses, and identifying drug interactions—reducing errors and hospital readmissions. AI also aids in real-time monitoring and operational improvements in hospital and community pharmacies. However, challenges persist, including data security, ethical considerations, regulatory needs, and some resistance to adopting AI-driven systems. This review consolidates findings from 16 articles, highlighting both the benefits and barriers to AI integration. Overall, while AI and ML hold great promise for enhancing patient safety and streamlining pharmacy practice, overcoming issues related to data privacy, ethics, and awareness among pharmacists will be essential to fully unlock their potential in pharmaceutical care.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-12-04
DOI
https://doi.org/10.5281/zenodo.14274341
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
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article

Leveraging Artificial Intelligence and Machine Learning in Pharmaceutical Care: A Systematic Review

Zenodo (CERN European Organization for Nuclear Research)
Artificial Intelligence in Healthcare and Education
article

Leveraging Artificial Intelligence and Machine Learning in Pharmaceutical Care: A Systematic Review

article en

Abstract

Artificial Intelligence (AI) and Machine Learning (ML) are transforming pharmaceutical care by enhancing decision-making, optimizing workflows, and improving patient outcomes. Applied in areas such as medication management, patient monitoring, and personalized treatment plans, these technologies support pharmacists in selecting medications, predicting treatment responses, and identifying drug interactions—reducing errors and hospital readmissions. AI also aids in real-time monitoring and operational improvements in hospital and community pharmacies. However, challenges persist, including data security, ethical considerations, regulatory needs, and some resistance to adopting AI-driven systems. This review consolidates findings from 16 articles, highlighting both the benefits and barriers to AI integration. Overall, while AI and ML hold great promise for enhancing patient safety and streamlining pharmacy practice, overcoming issues related to data privacy, ethics, and awareness among pharmacists will be essential to fully unlock their potential in pharmaceutical care.

Zenodo (CERN European Organization for Nuclear Research)
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Artificial Intelligence in Healthcare and Education
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