The primary goal of pharmacovigilance, the cornerstone of public health, is to track and evaluate adverse drug reactions in order to guarantee patient safety. Conventional approaches suffer from biases in human error, inefficiency, and scalability problems. A new era in pharmacovigilance is being ushered in by the introduction of artificial intelligence (AI), which holds the promise of vast data analysis, automated procedures, and enhanced safety signal detection. Artificial intelligence technologies improve adverse event detection and signal identification by providing unparalleled speed, accuracy, and scalability. They are adept at applying sophisticated algorithms, machine learning models, and natural language processing to glean insights from unstructured data sources like clinical notes, patient narratives, and regulatory reports. This capacity makes it possible to implement risk management plans that are more thorough and proactive. However, using artificial intelligence in pharmacovigilance necessitates large expenditures for processing power, infrastructure, and regulatory compliance. For artificial intelligence-driven systems to be accurate, dependable, and applicable, ongoing validation, monitoring, and improvement efforts are essential. The ethical and legal implications of patient privacy, data security, and regulatory compliance underscore the necessity of cautious artificial intelligence deployment in order to maintain public trust and protect patient rights. Regulatory agencies, healthcare professionals, and artificial intelligence developers must work together efficiently to implement explainable artificial intelligence frameworks, adaptive surveillance techniques, and improved signal validation processes in order to fully realize the potential of artificial intelligence in pharmacovigilance. This will usher in a new era of proactive risk assessment and improved public health outcomes. Keywords Pharmacovigilance, Artificial Intelligence, Signal Detection, Regulatory Affairs, Patient Safety, Public Health

Background: Traditional animal models in preclinical pharmacology show limited predictive accuracy for human outcomes due to interspecies differences, contributing to high drug attrition rates and ethical concerns. Recent regulatory changes have pushed the development of alternative, human-centered methods.Objective: To comprehensively investigate and evaluate novel non-animal approaches for establishing pharmacological safety and efficacy, such as organ-on-a-chip technologies, in vitro systems, and in silico models.Methods: A narrative evaluation of existing literature was conducted, concentrating on the technological principles, pharmacological applications, predictive performance, and regulatory acceptance of alternative testing strategies.Results: Compared with traditional 2D cultures, advanced in vitro models such as 3D organoids and iPSC-derived cells exhibit greater physiological relevance. Rapid, economical, and high-throughput predictions are made possible by in silico methods such as QSAR, PBPK modeling, and AI-based technologies. With improved translational fidelity, organ-on-a-chip technologies mimic the dynamic microenvironments of human organs. Strong, mechanism-based safety assessment frameworks are produced by integrated testing procedures that combine these methods. But there are still issues with scalability, validation, and standards.Conclusion: With growing regulatory approval for their future application, the incorporation of alternative approaches signifies a paradigm change toward more ethical, human-centered, and predictive drug development.

Quantum dots (QDs) are tiny semiconductor particles with unique light and electronic properties that can be adjusted by changing their size. They are widely usedin drug delivery, bioimaging, and theranostic applications. However, designing the best QDs is difficult because there are many possible combinations, makingtraditional trial-and-error methods slow and inefficient. Artificial intelligence (AI) and machine learning (ML) have improved this process by helping scientistspredict properties, design better QDs, and automate experiments. This review explains how various AI methods, including supervised learning, graph neuralnetworks, generative models, Bayesian optimisation, and active learning, are applied to QD-based drug delivery. These approaches have helped improve QDsynthesis, control drug release, and target specific areas such as tumours and the brain. AI has also supported applications in cancer treatment, neurological diseases,infections, and gene delivery. Despite these benefits, there are still challenges, such as a lack of reliable data, difficulty applying models to real-world conditions,and a limited understanding of how AI models make decisions. New technologies such as self-driving labs, advanced AI models, and quantum computing areexpected to further advance this field. Overall, combining AI with nanotechnology is making drug delivery faster, smarter, and more precise.

Background and Aims: Diabetic neuropathy (DN) is the most prevalent chronic complication of diabetes mellitus and a major cause of nontraumatic lower-limb amputation. Despite increasing research activity, the structural dynamics, thematic evolution, collaboration patterns, and institutional landscape of DN research remain incompletely characterized. This study aimed to map global DN research using bibliometric and network visualization analyses of PubMed-indexed publications from 2000 to 2025. Methods: A systematic PubMed search using MeSH and free-text terms identified 11,405 eligible publications. Annual publication trends were assessed, and VOSviewer v1.6.20 was used for author co-authorship, keyword co-occurrence, and institutional collaboration analyses. Modularity-based clustering identified major research themes. Results: Annual publications increased from 182 in 2000 to 1,055 in 2025, representing a 5.8-fold increase. Publications from 2021–2025 accounted for 36.6% of the corpus. The author network identified 37 clusters, with Malik, Rayaz A (137 publications; TLS = 738) as the most productive author, followed by Feldman, Eva L (100; TLS = 355) and Tesfaye, Solomon (86; TLS = 325). Keyword analysis identified nine clusters, with experimental/preclinical research dominating. Advanced diagnostics showed the most recent thematic orientation. Institutional analysis identified Denmark, particularly Aalborg and Aarhus universities, as a prominent research hub, whereas LMICs remained underrepresented. Conclusions: DN research has expanded substantially, with increasing emphasis on corneal confocal microscopy, neuroinflammation, and pharmacological pain management. Major gaps include LMIC underrepresentation, inadequate sex-disaggregated research, and limited translation of preclinical findings into disease-modifying therapies.