Background: Artificial intelligence (AI) is increasingly used to classify skin lesions from dermoscopic and clinical images, yet high performance in curated datasets may not translate into safe or equitable clinical use. Objective: This structured narrative review evaluates diagnostic accuracy, skin-tone-related performance disparities, dataset representation, and governance requirements for AI-based skin-lesion classification. Methods: PubMed/MEDLINE was used as the primary database, supplemented by PubMed Central, Google Scholar, Semantic Scholar, journal websites, and reference-list screening. English-language, peer-reviewed literature published mainly between 2021 and 2026 was synthesised thematically across diagnostic performance, external validity, skin-tone representation, clinical implementation, and governance. Results: Meta-analytic evidence indicates high sensitivity for AI-based skin-lesion classification, including pooled sensitivity of 0.91 and an HSROC-AUC of 0.88, but lower specificity and substantial heterogeneity limit direct clinical translation. Performance declined in darker skin-tone groups in several external evaluations; in selected models, sensitivity fell between Fitzpatrick types I–II and V–VI. Limited reporting of skin tone, reliance on internally tested or curated datasets, and sparse prospective evidence further constrain generalisability. Conclusion: AI should be implemented as clinician-supervised decision support rather than as an autonomous diagnostic system. Equitable deployment requires representative datasets, external and subgroup-stratified validation, transparent reporting, defined clinical accountability, regulatory oversight, and continuous post-deployment monitoring.

Introduction: In hemodialysis patients, Body Mass Index is insufficient in assessing their nutritional status due to the ‘obesity paradox’ and the impact of body composition on inflammation. The aim of the study was to assess the relationship between body composition, traditional inflammatory markers, and the new NETosis indicators (neutrophil extracellular traps), as well as to determine their impact on 12-month mortality. Methods: The study included 99 patients with end-stage renal disease. Their body composition was assessed using bioelectrical impedance analysis (Seca mBCA 525). Blood serum was tested for inflammatory markers (hs-CRP, IL-6, TNF-α, IL-1B), NETosis markers (citrullinated histone H3, MPO, elastase), and nutritional status parameters (albumin, transferrin). Results: No correlation between BMI and inflammation was demonstrated. Higher contents of the adipose tissue, particularly visceral, were significantly associated with increased levels of IL-6 and hs-CRP, while muscle mass was negatively correlated with inflammation. The use of dialysis catheters stimulated NETosis (higher CH3 levels), which had a negative effect on albumin concentrations. Low albumin levels and high TNF-α levels were independent predictors of death. Conclusions: It is body composition, and not BMI, that determines the severity of inflammation. Visceral obesity promotes inflammation, while muscle mass has a protective effect. Dialysis catheters, by stimulating NETosis, contribute to a decrease in albumin levels and a poorer prognosis.

Background: Sepsis remains a primary cause of mortality and organ dysfunction in Intensive Care Units (ICUs) globally. Although AI models show considerable promise for the early identification of sepsis, moving these tools from retrospective performance to bedside utility is hindered by socio-technical barriers, workflow friction, and algorithmic equity issues. Objective: This structured review evaluates the state of AI-driven sepsis Early Warning Systems (EWS), identifying systemic barriers to clinical integration and assessing algorithmic fairness across demographic cohorts. Methodology: A structured review of peer-reviewed literature published in PubMed/MEDLINE and Google Scholar between January 2018 and June 2026 was conducted, focusing on predictive performance, clinical implementation, and algorithmic equity. Results: Retrospective assessments of deep sequential architectures demonstrate high diagnostic accuracy (AUROC>0.95) (Boussina et al., 2024; Siddiqui, 2026). Prospective trials of landmark systems such as TREWS (Adams et al., 2022), COMPOSER (Shashikumar et al., 2025), and CONCERN EWS (Rossetti et al., 2025), confirm clinical utility, yielding mortality reductions of 17% to 35.6%. However, real-world effectiveness is frequently hindered by severe alert fatigue (Wong et al., 2021). Furthermore, because models rely on historical health data, they often inherit systemic biases, risking the exacerbation of existing disparities related to race, gender and socioeconomic status (Chen et al., 2023; Obermeyer et al., 2019; Seyyed-Kalantari et al., 2021). Conclusion: Maximizing the clinical impact of AI-driven EWS requires a paradigm shift from purely performance-centric model development to rigorous, equity-oriented clinical governance guided by international reporting frameworks like TRIPOD-AI (Collins et al., 2024) and DECIDE-AI (Vasey et al., 2022). To operationalize this, we propose the integration of the Clinical AI Safety Officer (CAISO) to maintain real-time algorithmic safety, audit demographic equity, and manage the clinical accountability loop.

Background Clinical-grade wearable biosensors offer significant potential for tracking chronic diseases outside the hospital. However, while the hardware itself is highly accurate, the medical field still struggles with the logistical reality of deploying these tools. The structural barriers preventing widespread clinical use, and the ways these hurdles impact overall health equity, lack rigorous synthesis in the current literature. Objective Moving beyond pure hardware accuracy, this analysis maps the practical realities of continuous patient surveillance. It specifically investigates the institutional workflow bottlenecks, hardware usability hurdles, and underlying economic disparities that dictate how monitoring programs actually function for populations managing heart failure, diabetes, or COPD. Methodology Peer-reviewed literature spanning 2018 to 2026 was extracted from PubMed/MEDLINE and Google Scholar. The inclusion criteria deliberately filtered out purely technical validation studies. Instead, the analysis targeted data documenting the logistical friction, long-term adherence failures, and unequal access surrounding continuous glucose monitors, ambulatory cardiac patches, and multi-parameter tracking devices. Results The synthesized data show that severe workflow friction and poor electronic health record interoperability actively block hospital networks from scaling these programs. On the patient side, a distinct "compliance paradox" emerges. High initial motivation frequently collapses into device abandonment, usually triggered by physical skin irritation, convoluted software interfaces, and low technical literacy. The literature also exposes a severe digital divide. A patient's income bracket, geographic location, and advanced age strictly gatekeep their ability to access continuous medical monitoring. Conclusion Healthcare networks cannot rely solely on precise hardware to improve chronic disease management. Deploying continuous monitors successfully requires a fundamental shift toward repairing broken clinical workflows and addressing systemic exclusion. Without structural interventions targeting financial and technical accessibility, digital health initiatives will simply reinforce existing medical inequalities.

Background: Hospital readmission rates are often used to judge the quality of care and how well healthcare systems are working. Predictive models based on electronic health records (EHRs) can help estimate which patients are at higher risk, but when they don’t properly reflect socioeconomic realities, they can unintentionally reinforce existing inequalities (Obermeyer et al., 2019). This review looks at how well these EHR-based models predict 30-day readmissions, how Social Determinants of Health (SDOH) are included, and what this means for fairness and health equity in real-world use. Methodology: A structured literature review was conducted using PubMed/MEDLINE and Google Scholar. It included peer-reviewed studies published between 2018 and 2026, focusing on model validation studies, retrospective cohort analyses, and research on bias and fairness in predictive healthcare models. Results: Synthesis reveals that incorporating unstructured and structured SDOH variables yields mixed impacts on discriminatory performance (Area Under the Receiver Operating Characteristic curve changes ranging from -0.01 to +0.04), yet significantly rectifies systemic algorithmic underestimation of risk for marginalized demographics. Unadjusted models consistently show racial, ethnic, and socioeconomic bias, often because they rely on proxy variables embedded in historical healthcare data (Gianfrancesco et al., 2018). As a result, preventive transition-of-care resources can be systematically directed away from the patients who need them most (Pierson et al., 2021). Conclusion: To make predictive healthcare tools truly useful in practice, it is not enough to focus only on accuracy. They also need to account for fairness, real clinical context, and the actual consequences of their use in everyday healthcare settings (Wiens et al., 2019). Policy-level interventions must mandate standardized SDOH extraction alongside equity-focused calibration algorithms before widespread clinical deployment (Ghassemi et al., 2021).

Background: Suicide remains a critical global public health crisis, demanding proactive intervention strategies. Recent advancements in artificial intelligence (AI), including machine learning and natural language processing, offer unprecedented capabilities for early suicide risk prediction utilizing electronic health records, clinical notes, and digital phenotyping. Objective: This structured review evaluates current evidence regarding the performance of AI models in predicting suicide risk, analyzes the ethical challenges associated with their deployment, and identifies primary barriers and facilitators to successful clinical implementation. Methodology: A structured literature search was conducted utilizing PubMed/MEDLINE and Google Scholar for peer-reviewed articles published between 2018 and 2026. Following specific inclusion and exclusion criteria, the final corpus of studies was analyzed using a thematic synthesis framework focusing on predictive accuracy, ethical implications, and workflow integration. Results: While AI algorithms frequently demonstrate robust predictive accuracy, their clinical utility is heavily complicated by significant ethical and operational hurdles. Key challenges include algorithmic bias exacerbating health inequities, severe data privacy concerns, and a notable lack of clinician trust stemming from the "black box" nature of complex machine learning models. Furthermore, poor integration into existing psychiatric workflows limits real-world feasibility. Conclusion: AI represents a powerful, yet imperfect, socio-technical tool for suicide prevention. Bridging the translational gap from algorithmic validation to routine clinical use requires rigorous ethical governance, prioritized model explainability, and human-centered implementation strategies tailored to frontline healthcare settings.