# Narrative Review

This narrative review summarizes contemporary strategies for managing constipation, encompassing lifestyle and dietary modifications, pharmacological therapies, behavioral interventions, and surgical options for refractory cases, noting that traditional laxatives remain the mainstay while newer agents such as prosecretory drugs, serotonergic agonists, and bile acid modulators have expanded therapeutic possibilities, that non-pharmacological approaches including biofeedback and neuromodulation provide effective alternatives in selected patients, and that substantial methodological variability in clinical trials across diagnostic criteria, endpoints, and follow-up durations limits generalizability, with future research focused on individualized treatment for refractory constipation, surgical

This narrative review summarizes contemporary strategies for managing constipation, encompassing lifestyle and dietary modifications, pharmacological therapies, behavioral interventions, and surgical options for refractory cases, noting that traditional laxatives remain the mainstay while newer agents such as prosecretory drugs, serotonergic agonists, and bile acid modulators have expanded therapeutic possibilities, that non-pharmacological approaches including biofeedback and neuromodulation provide effective alternatives in selected patients, and that substantial methodological variability in clinical trials across diagnostic criteria, endpoints, and follow-up durations limits generalizability, with future research focused on individualized treatment for refractory constipation, surgical

This narrative review synthesizes recent clinical studies and technological advances to map virtual reality (VR) applications in otolaryngology nursing across patient-facing uses (preoperative education, pain and anxiety relief, vestibular rehabilitation, swallowing assessment and therapy, hearing rehabilitation, and tinnitus management) and nurse-facing uses (professional training and skills development), reporting that vestibular rehabilitation is supported by strong evidence from randomized controlled trials while swallowing therapy and tinnitus management rest mainly on preliminary exploratory studies, and discussing technical, economic, and clinical barriers alongside prospects for integrating VR with artificial intelligence, wearable biosensors, and telerehabilitation platforms.

This narrative review synthesizes recent clinical studies and technological advances to map virtual reality (VR) applications in otolaryngology nursing across patient-facing uses (preoperative education, pain and anxiety relief, vestibular rehabilitation, swallowing assessment and therapy, hearing rehabilitation, and tinnitus management) and nurse-facing uses (professional training and skills development), reporting that vestibular rehabilitation is supported by strong evidence from randomized controlled trials while swallowing therapy and tinnitus management rest mainly on preliminary exploratory studies, and discussing technical, economic, and clinical barriers alongside prospects for integrating VR with artificial intelligence, wearable biosensors, and telerehabilitation platforms.

This narrative review is the first to synthesize the convergence of artificial intelligence and endohepatology into four functional pillars—intelligent hemodynamic assessment, virtual histology, precision tissue acquisition, and integrated risk stratification with therapeutic and decision support—and proposes a three-tier readiness framework to separate demonstrated capabilities from extrapolated and conceptual applications, concluding that the field is still very early-stage but that AI has strong potential to turn endohepatology into a single machine-driven diagnostic and therapeutic platform provided standardized datasets, prospective validation, and clear regulatory and governance standards are in place.

This narrative review is the first to synthesize the convergence of artificial intelligence and endohepatology into four functional pillars—intelligent hemodynamic assessment, virtual histology, precision tissue acquisition, and integrated risk stratification with therapeutic and decision support—and proposes a three-tier readiness framework to separate demonstrated capabilities from extrapolated and conceptual applications, concluding that the field is still very early-stage but that AI has strong potential to turn endohepatology into a single machine-driven diagnostic and therapeutic platform provided standardized datasets, prospective validation, and clear regulatory and governance standards are in place.

This narrative review, based on structured searches of PubMed and ScienceDirect (2016-2026, plus a clinical-research-focused search for 2013-2026) supplemented by FDA, EMA, Cochrane Library, and industry sources, examines medical device safety across the full lifecycle from early design and clinical investigation through regulatory review, postmarket surveillance, software change management, cybersecurity, and AI/ML-enabled device oversight, arguing that device safety requires a broader socio-technical framework than drug safety and that safety evidence is shifting from retrospective, passive reporting toward proactive, real-time, lifecycle-based generation.

This narrative review, based on structured searches of PubMed and ScienceDirect (2016-2026, plus a clinical-research-focused search for 2013-2026) supplemented by FDA, EMA, Cochrane Library, and industry sources, examines medical device safety across the full lifecycle from early design and clinical investigation through regulatory review, postmarket surveillance, software change management, cybersecurity, and AI/ML-enabled device oversight, arguing that device safety requires a broader socio-technical framework than drug safety and that safety evidence is shifting from retrospective, passive reporting toward proactive, real-time, lifecycle-based generation.

This narrative review, based on a structured search of PubMed/MEDLINE, Scopus, and Web of Science, maps the clinical applications of artificial intelligence across otolaryngology subspecialties, noting that deep learning shows potential in sinonasal disease detection, automated image segmentation, lymph node metastasis prediction, thyroid nodule classification, and prognostic modeling in head and neck cancer, while multimodal systems and generative large language models are emerging in medical education, image interpretation, differential diagnosis, and clinical decision support; however, limited external validation, retrospective designs, dataset heterogeneity, algorithmic bias, lack of transparency, privacy concerns, medico-legal uncertainty, and automation bias still constrain broad imp

This narrative review, based on a structured search of PubMed/MEDLINE, Scopus, and Web of Science, maps the clinical applications of artificial intelligence across otolaryngology subspecialties, noting that deep learning shows potential in sinonasal disease detection, automated image segmentation, lymph node metastasis prediction, thyroid nodule classification, and prognostic modeling in head and neck cancer, while multimodal systems and generative large language models are emerging in medical education, image interpretation, differential diagnosis, and clinical decision support; however, limited external validation, retrospective designs, dataset heterogeneity, algorithmic bias, lack of transparency, privacy concerns, medico-legal uncertainty, and automation bias still constrain broad imp

This narrative review searched PubMed, Scopus and Web of Science (2012-2024) to map the role of radiomics and artificial intelligence in neuro-oncology across diagnostic, prognostic and therapeutic potential, reporting that radiomics can automatically extract quantitative features from MRI, CT and PET/CT that are not visible to the human eye, has been shown useful for pre-surgical classification of gliomas, survival prediction and differentiation between tumour progression and pseudo-progression, may improve diagnostic accuracy and support patient stratification by molecular biomarkers such as IDH and MGMT when integrated with deep learning, and links image phenotypes to genetic alterations through radiogenomics to enhance personalised medicine, while limitations persist from lack of proto

This narrative review searched PubMed, Scopus and Web of Science (2012-2024) to map the role of radiomics and artificial intelligence in neuro-oncology across diagnostic, prognostic and therapeutic potential, reporting that radiomics can automatically extract quantitative features from MRI, CT and PET/CT that are not visible to the human eye, has been shown useful for pre-surgical classification of gliomas, survival prediction and differentiation between tumour progression and pseudo-progression, may improve diagnostic accuracy and support patient stratification by molecular biomarkers such as IDH and MGMT when integrated with deep learning, and links image phenotypes to genetic alterations through radiogenomics to enhance personalised medicine, while limitations persist from lack of proto