Keyword: Artificial Intelligence (AI)

ABSTRACT: Adverse drug reactions (ADRs) remain a major global challenge, contributing substantially to patient morbidity, mortality, and healthcare costs. Traditional pharmacovigilance approaches—spontaneous reporting and post-marketing surveillance—are hampered by underreporting, delays, and limited contextual data. The growing availability of electronic health records (EHRs), which capture longitudinal structured and unstructured patient information, presents an unprecedented opportunity to advance ADR prediction. This narrative review synthesizes recent progress in developing and validating predictive models that leverage EHRs, highlighting methodological approaches, challenges, and future directions. Predictive strategies range from traditional regression models to advanced machine learning and deep learning architectures, with multimodal frameworks increasingly integrating structured fields (demographics, labs, prescriptions) and unstructured clinical text through natural language processing. While ensemble and deep learning methods demonstrate superior performance, issues of data quality, missingness, bias, and interpretability persist. Robust validation frameworks—spanning internal cross-validation to multi-center external testing—are critical to ensure generalizability and clinical trustworthiness. Ethical considerations, including fairness, privacy, and transparency, remain central to safe deployment. Looking forward, promising avenues include federated learning across institutions, integration of multi-omics and pharmacogenomic data, explainable AI tailored for clinical use, and real-time monitoring through digital twin frameworks. These trajectories, combined with robust governance and clinician–data scientist collaboration, have the potential to transform ADR detection from a reactive process to proactive, personalized prevention. By synthesizing the existing evidence, this review provides insights into the development of more effective predictive models for ADRs and informs strategies for improving pharmacovigilance. This study will contribute to the ongoing efforts to leverage EHRs and predictive models for improving patient outcomes and reducing the burden of ADRs.

ABSTRACT: The rapid application of artificial intelligence (AI) in diagnostic disciplines such as radiology, pathology, microbiology, and genomics has revolutionized the way in which doctors and laboratory workers provide patient care. AI has enhanced the efficacy, accuracy, and cost-effectiveness of laboratory operations, clinical decision support systems, and image interpretation. However, these advantages are accompanied by a severe behavioral issue: an excessive reliance on automation could result in a generation of professionals who lack the reasoning abilities necessary to independently assess or contextualize machine outputs. The dual effects of AI integration are the focus of this paper, which highlights its beneficial aspects—including decreased cognitive load, increased confidence, and educational reinforcement—as well as its adverse effects, which include skill degradation, diagnostic deskilling among trainees, complacency, and reduced situational awareness. The research emphasizes the potential for unregulated dependence on AI to progressively alter professional conduct and expertise by utilizing case examples from radiology, pathology, laboratory medicine, and clinical decision support, as well as parallels from automation in aviation. In order to address these concerns, a conceptual framework is proposed that integrates AI into a "human-in-the-loop" approach, thereby preserving the significance of human judgment while leveraging machine accuracy. In order to achieve equilibrium, strategies include curriculum reform to integrate AI with hands-on experience, regular retraining, the implementation of explainable AI to promote active thinking, and institutional measures similar to recurrent training in high-stakes sectors. Ultimately, AI should complement the existing infrastructure rather than supplant it. In order to guarantee this, we must establish strategic educational, organizational, and regulatory safeguards to preserve diagnostic expertise, ensure accountability, and maintain the resilience of healthcare systems as they become increasingly dependent on intelligent technologies.