The role of AI in optimizing drug dosage and reducing medication errors
1 University of Akron, School of Nursing, US.
2 Life's Journey Inc. Winnipeg, Manitoba, Canada.
3 Enugu State University Teaching Hospital, Parklane, Enugu, Nigeria.
4 Austin Peay State University, Clarksville, TN, USA.
Review
International Journal of Biology and Pharmacy Research Updates, 2024, 04(01), 018–034.
Article DOI: 10.53430/ijbpru.2024.4.1.0027
Publication history:
Received on 01 July 2024; revised on 11 August 2024; accepted on 13 August 2024
Abstract:
Artificial intelligence (AI) is transforming healthcare by optimizing drug dosage and minimizing medication errors, significantly enhancing patient safety and treatment efficacy. AI algorithms, particularly those utilizing machine learning and deep learning, analyze vast amounts of patient data, including genetic information, medical history, and real-time health metrics, to determine the most effective drug dosages tailored to individual patients. One of the critical areas where AI excels is in precision medicine. AI-driven systems can process complex datasets to predict how different patients will respond to specific medications, thereby personalizing drug dosage. For instance, pharmacogenomics leverages AI to understand how genetic variations affect drug metabolism, helping to customize dosages that maximize therapeutic benefits while minimizing adverse effects. Moreover, AI enhances clinical decision support systems (CDSS) by integrating with electronic health records (EHRs). These AI-powered CDSS provide healthcare professionals with real-time alerts about potential medication errors, such as incorrect dosages, drug interactions, or patient-specific contraindications. By continuously learning from new data, these systems improve their accuracy and reliability over time, reducing the incidence of medication errors significantly. AI is also pivotal in the development of adaptive dosing algorithms. These algorithms use patient-specific data, such as kidney function and liver enzyme levels, to adjust drug dosages dynamically. This approach is particularly beneficial in managing chronic conditions like diabetes and hypertension, where maintaining optimal drug levels is crucial for effective disease management. For example, AI can help determine the precise insulin dose required for diabetic patients by analyzing patterns in their blood glucose levels. In addition to individual patient care, AI aids in broader pharmacovigilance efforts by identifying and predicting adverse drug reactions (ADRs). Machine learning models analyze large datasets from clinical trials, post-marketing surveillance, and patient reports to detect early signals of ADRs, allowing for timely interventions and adjustments in drug prescriptions. In conclusion, AI's role in optimizing drug dosage and reducing medication errors is a significant advancement in personalized medicine and patient safety. By harnessing the power of AI, healthcare providers can deliver more precise, effective, and safer treatments, ultimately improving patient outcomes and reducing healthcare costs.
Keywords:
Role; AI; Optimizing; Drug Dosage; Medical Errors
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