Eurasian Journal of Chemical, Medicinal and Petroleum Research

Abstract Adverse drug reactions (ADRs) and adverse drug events (ADEs) remain major threats to patient safety in hospitals, particularly among older adults, patients with multimorbidity, individuals exposed to polypharmacy, and patients experiencing rapid changes in physiological status. Conventional pharmacovigilance and medication-monitoring approaches depend heavily on retrospective reporting and clinical recognition, which may delay identification of preventable harm. Artificial intelligence (AI), particularly machine learning (ML), natural language processing, and deep learning, offers opportunities to transform pharmacovigilance from a predominantly reactive process into a predictive and continuously updated safety system. This systematic evidence synthesis examined the performance and clinical implications of AI-based models for predicting ADRs and ADEs among hospitalized patients, with particular attention to nursing practice. Evidence published in major biomedical databases synthesized with emphasis on model performance, predictors, validation, explainability, and implications for medication administration and clinical surveillance. Recent systematic reviews indicate that random forest, gradient boosting, support vector machines, regularized regression, and deep-learning architectures frequently used, with pooled predictive performance generally demonstrating moderate discrimination. A 2025 systematic review specifically examining hospitalized patients reported pooled sensitivity and specificity of 78.1% and 70.6%, respectively, for development-only models, 81.5%, and 79.5% for models undergoing external validation. Contemporary evidence also demonstrates increasing use of longitudinal EHR representations, clinical narratives, nursing documentation, and foundation models. However, heterogeneous outcome definitions, missing-data procedures, limited external validation, class imbalance, alert fatigue, algorithmic bias, and insufficient explainability remain substantial barriers to implementation. AI therefore conceptualized as an augmentation technology rather than a replacement for nurses or other clinicians. Effective implementation requires interdisciplinary governance, transparent algorithms, prospective evaluation, workflow integration, education, and continuous monitoring. Properly implemented, AI-supported ADR prediction may strengthen nursing vigilance, prioritize high-risk patients, improve medication safety, and facilitate earlier clinical intervention.

Abstract The field of AI ethics has become a global enterprise with a wide range of actors. However, AI ethics is not new. With new uses of AI, AI ethics has flourished beyond academia. For example, in 2021, UNESCO appointed 24 experts from around the world and launched a global online consultation on AI ethics to facilitate dialogue and exchange of views with all member states. One of the major problems that AI ethics and ethicists may face is the ambiguity of what is actually happening in AI, given that understanding what is good about an activity is itself very useful in determining its ethical issues. Despite the lack of transparency of AI in its current form, the important point is what kind of ethical issues can arise due to the use and development of this technology, which is a matter of debate and investigation. Another concern is transparency and explain ability. Many AI systems act like black boxes. The decision-making process is not transparent. This undermines trust among healthcare providers and patients and their companions. It is unacceptable in the medical field that even the designers of AI systems do not know exactly how AI reached a particular result due to the complexity of algorithms such as artificial neural networks. Because we are dealing with human lives here. The challenge of human decision-making is also raised. The use of medical automation processes can reach the point where AI judgment is pitted against human judgment. Relying on AI for diagnostic or treatment decisions raises questions about the role of humans. Although the use of processing power and big data reduces the error rate of AI results, who will be responsible if AI makes a mistake?