Using Artificial Intelligence to Identify Patterns and Predictors of Adverse Drug Reactions in Cardiovascular Patients: A Comprehensive Narrative Review
Jul 2026· American Journal of Cardiovascular Drugs· Vol 26, pp. 537 - 557· 0 citations· 137 references
Medicine
TL;DR
This narrative review synthesizes current evidence on AI applications across the cardiovascular PV continuum, including safety signal detection, patient-level risk prediction, and text-based surveillance and holds substantial promise for enhancing cardiovascular drug safety.
The systematic monitoring of post-marketing drug safety is paramount to ensuring patient
welfare and population-level health, requiring the timely detection and prevention of adverse
drug events. Traditional pharmacovigilance schemes, which largely rely on spontaneous
reporting, have weaknesses, including underrepo...
C. R. Darwin, Meruva Sathish Kumar, S. Marakatham et al.· Current Drug Safety· 0 citations
Adverse drug reaction (ADR) signal detection is the process of identifying new or previously unclear associations between a medicine and an unwanted effect, using data collected after the medicine has been approved for use. Because clinical trials involve limited numbers of patients over a short period, many uncommon o...
Mohamed Arshak, Mohamed Shafeek A, A. A· International journal of all...· 0 citations
Background: Adverse drug reactions (ADRs) continue to be a leading source of morbidity, hospitalization, hospital stay, and avoidable health expenditure. The conventional pharmacovigilance process has relied heavily upon spontaneous, manual reporting, prone to the following limitations: under-reporting, delayed reporti...
Due to the ageing of the population, multimorbidity and polypharmacy has significantly increased, causing medication safety to become a global health concern. Traditionally, medication risk assessment methods based on the counts of medications or rule-based criteria are inadequate to perceive the dynamic, individual an...
Jian-Feng Chen, Le-Yu Tao· Frontiers in Public Health· 0 citations
Challenges such as data quality, algorithmic bias, lack of transparency, privacy, cybersecurity, regulatory uncertainty and the need for human oversight remain important.
Rajashree Somnath Chorgade, Srushti Dipak Nevase, Shubham Hanumant Dhavale et al.· International Journal of Inn...· 0 citations
AI is revolutionising clinical trials by improving efficiency, accuracy, and decision-making throughout the study process. Traditional clinical trials are frequently time-consuming, expensive, and constrained by factors such as patient recruiting delays, protocol complexity, data management concerns, and high dropout r...
E. Nikitha, K. L. Supriya, K. Rakesh et al.· Journal of Pharmaceutical Re...· 0 citations
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