Keyword: Artificial Intelligence (AI)

ABSTRACT: Telemedicine has become a vital element of modern surgical practice, facilitating virtual consultations, intraoperative collaboration, and postoperative monitoring. One of its most innovative applications is remote proctoring, or teleproctoring—the real-time supervision and assistance of surgical procedures across distances. The COVID-19 epidemic has accelerated the impact of these technologies on surgical education, credentialing, and global access to specialized expertise. This narrative review synthesizes literature from 2005 to 2025 obtained from PubMed, ResearchGate, Web of Science, and Google Scholar. The emphasis includes telemedicine and remote proctoring in surgical care, education, and quality assurance. Chosen materials comprise original investigations, systematic reviews, and policy documents that pertain to technical platforms, clinical outcomes, educational applications, implementation issues, and regulatory considerations.  Modern teleproctoring technologies include secure, low-latency, high-definition video broadcasts enhanced by augmented reality features and telestration capabilities. The available evidence, primarily from observational studies, confirms the approach's viability, cost-effectiveness, improved training efficiency, and high user acceptance across disciplines such as minimally invasive, robotic, and endoscopic surgery. However, inconsistencies in outcome measurements, a lack of randomized controlled trials, and varying legal frameworks restrict wider applicability. The safety profiles appear promising, yet data deficiencies remain. Telemedicine and remote proctoring are developing into integral components of surgical care. Essential future directions include the implementation of artificial intelligence solutions, the development of standardized outcome metrics, the execution of comparative research, the enhancement of cybersecurity measures, and the promotion of fair access in resource-constrained settings. When integrated within strong regulatory and ethical frameworks, remote proctoring has the potential to function as a fundamental pillar of efficient and interconnected global surgical practice.

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.