Artificial intelligence (AI) is increasingly being integrated into healthcare systems and has the potential to improve health outcomes. In Sub-Saharan Africa (SSA), however, concerns remain that AI may either reduce or exacerbate existing health inequities depending on how it is developed, governed, and implemented. This scoping review aimed to map and synthesise the existing evidence on the implications of AI for health equity among marginalised populations in Sub-Saharan Africa. PubMed, Web of Science, Scopus, and selected grey literature sources were searched between February and March 2026. Peer-reviewed and grey literature examining AI applications, governance, or implementation in healthcare involving marginalised populations or health systems within SSA were eligible for inclusion. The review followed the Arksey and O'Malley methodological framework and the PRISMA-ScR reporting guideline. Two reviewers independently screened sources of evidence and extracted data using a standardised charting form, and findings were synthesised thematically. Twenty-three sources of evidence met the inclusion criteria. Two dominant narratives emerged. AI may reinforce existing inequities through digital infrastructure gaps, algorithmic bias, under-representation of African datasets, weak governance, and data colonialism. Conversely, AI has the potential to improve health equity by expanding healthcare access, strengthening disease surveillance, supporting health system planning, and improving access to specialised services. Across the literature, AI's impact consistently depended on equitable infrastructure, inclusive governance, and context-specific implementation. AI has considerable potential to advance health equity in SSA. However, achieving equitable benefits requires investment in digital infrastructure, representative data systems, ethical governance, and inclusive policies. This paper situates AI within the context of health equity in Sub-Saharan Africa.Main findings: Artificial intelligence in African health systems presents both opportunities to expand healthcare access and risks of reinforcing existing inequalities driven by infrastructure gaps, data bias, and governance challenges.Added knowledge: This review provides a focused synthesis of how artificial intelligence specifically affects marginalised populations in Sub-Saharan Africa, highlighting the structural conditions that shape equitable or inequitable outcomes.Global health impact for policy and action: The findings emphasise the need for deliberate policy action on inclusive data systems, digital infrastructure investment, and contextually grounded governance to ensure artificial intelligence advances health equity rather than deepening disparities.
In the field of 3D printing technology in the medical application is becoming more and more widely, especially in orthopaedic surgery, its importance is increasingly prominent. This technology through precise biological tissue engineering, and can be produced with the patient's own tissue matching the artificial bone implants, thus in the bone graft surgery and cosmetic surgery play a key role. With the deepening of the research found that different material and printing method can significantly affect the artificial bone implant biological specificity and clinical effect. In addition, the personalization of 3D printing implants can better adapt to the patient's anatomical structure, improve the success rate of surgery and patient satisfaction. In recent years, 3D printing technology in expanding the application of orthopedic surgery, its potential value also gradually by mining. Therefore the author through the reviews in recent years, 3D biological technology and 3D printing to print and the research status of the artificial bone implants, as well as their application in orthopedic surgery and potential value, in order to provide new ideas for the research of the field.
Recent advances in protein structure determination and prediction, large-scale structural databases, and artificial intelligence have reshaped structure-based drug discovery. Structure-aware artificial intelligence models integrate molecular representation learning with three-dimensional protein information to model interactions, predict complex structures and binding poses, and generate novel molecules. In this review, we follow this paradigm along a continuum from protein-ligand modeling to the de novo design of biomolecular binders. We first outline the molecular and protein representations that render structures computable, together with the growing collection of structural data resources. We then survey state-of-the-art methods across drug-target interaction prediction, protein-ligand complex modeling and docking, de novo molecular generation, and biomolecule design, examining the convergence of docking, structure prediction, and molecular generation within co-folding and diffusion-based frameworks. Despite these advances, prospective experimental validation remains scarce, and persistent limitations such as biased structural coverage, limited and ambiguous negative supervision, fragmented benchmarking, and insufficient mechanistic interpretability continue to constrain real-world utility. Progress in data quality and supervision design, evaluation rigor, and design-relevant interpretability will be essential to translate methodological innovation into practical impact.
This Viewpoint explores how artificial intelligence may mitigate or exacerbate inequities in correctional health systems and outlines policy and implementation considerations necessary to ensure health equity for incarcerated populations.
Given the pivotal role of imaging in diagnosing urological cancers, artificial intelligence (AI) has emerged as a promising tool to improve diagnostic accuracy and reliability. This study systematically evaluates the diagnostic performance of AI models in radiologic imaging of urological cancers. A systematic search was conducted in four electronic databases up to June 2026 to identify studies that applied AI algorithms for the diagnosis of urological cancers using CT, MRI, or ultrasound. Eligible studies reported diagnostic accuracy metrics for AI models, with clinician comparator data extracted when available. A bivariate random-effects model was used to pool sensitivity, specificity, and AUC values. Subgroup analyses were conducted to examine diagnostic performance across different cancer types and imaging modalities, and to explore potential sources of heterogeneity. Study quality was assessed using the QUADAS-2 tool. A total of 110 studies were included in the meta-analysis. AI models achieved pooled sensitivity and specificity of 0.85 (95% CI: 0.83-0.87) and 0.83 (95% CI: 0.80-0.86), with an AUC of 0.91 (95% CI: 0.88-0.93). Clinicians demonstrated a pooled sensitivity of 0.82 (95% CI: 0.79-0.85) and specificity of 0.68 (95% CI: 0.62-0.73), with an AUC of 0.83 (95% CI: 0.80-0.86). Subgroup analyses indicated that AI models showed overall diagnostic advantages across cancer types and imaging modalities, particularly in specificity, AUC, and diagnostic odds ratios, although clinicians demonstrated higher sensitivity in the prostate cancer and MRI subgroups. AI models demonstrate strong diagnostic performance across various urological cancers and imaging modalities, showing potential as supportive tools in radiological workflows. Further prospective, standardized, and multi-center evaluations are warranted to confirm AI's clinical utility across diverse diagnostic tasks in urological oncology.
Artificial intelligence (AI) has achieved remarkable success in the diagnosis of Alzheimer's disease (AD) in the literature, where many of the models use multi-modal methods including neuroimaging, cerebrospinal fluid, genetics, and cognitive assessment. But clinical adoption of these systems is still limited since most systems are developed in an idealized setting, as cost-effective and specialized diagnostic studies are not universally accessible. We discuss the translation of benchmark performance of AI to real-world dementia care pathways. A practical framework that would be useful for scalable, equitable, and clinically deployable AI-assisted dementia care. In fact, recent advancements in blood-based biomarkers such as plasma phosphorylated tau, glial fibrillary acidic protein, and neurofilament light chain are providing new opportunities for a flexible and minimally invasive diagnosis method. Based on these advances, we propose a clinically grounded AI-assisted cascading model which mirrors real-world workflows via progressive screening, biomarker-guided assessment, selective imaging escalation, and longitudinal prognostic monitoring. We further discuss enabling methods such as sequential decision-making, reinforcement learning, cost-sensitive learning, missing-modality robustness, and explainable AI. Finally, we outline the challenges for data design, for future validation and integration into healthcare systems, and ethical use.
There is a significant intra-provider variability in colonoscopy performance but this is difficult to measure in routine practice. We describe an artificial intelligence tool (AI-CQ) that measures colonoscopy quality via analysis of recorded colonoscopy procedures and compare AI-CQ assessment of quality metrics with manual measurement in a large cohort of colonoscopists. Colonoscopy procedures were performed at one of two endoscopy locations at a single academic medical center. Select analyses were restricted to higher volume screening colonoscopists performing ≥100 screening or surveillance colonoscopies over the 11-month study period. Colonoscopy quality metrics were calculated from recorded colonoscopy videos using the AI-CQ tool and compared to manually calculated metrics (via nurse documentation) including adenoma detection rate (ADR) and withdrawal time (WT). A total of 18,597 colonoscopy procedures performed by 55 unique attendings were recorded with 31 higher volume screening colonoscopists performing 12,456 screening or surveillance colonoscopies (median colonoscopist ADR 43.2%). AI-Insertion time (AI-IT) and AI-WT strongly correlated with manually calculated IT (r=0.60) and WT (r=0.91). AI-polyps per colonoscopy (AI-PPC) was 1.47 (SD ±0.54) and strongly correlated with ADR (0.54) and serrated detection rate (0.67). The AI-CQ accurately measured performance of any polypectomy and cold snare polypectomy with a mean cold snare polypectomy rate of 84.0% (range 63.0-95.3%). The AI-CQ can accurately measure commonly utilized quality metrics using recorded colonoscopy videos. Use of this AI tool provides a novel feasible approach to reliably measuring colonoscopy quality.
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Previous research has demonstrated changes in neural oscillations associated with varying levels of roughness during active-touch exploration of surfaces. In the present study, we aimed to investigate changes in neural oscillatory activity and softness perception during touch exploration of skin. Two experiments were conducted. Study 1 evaluated active touch of artificial skin samples mounted to a purpose-built touch sensor, whereas Study 2 investigated active stroking of one's own forearm. In both experiments, the substrates were treated with commercially available bar soaps to deliver either a soft or draggy skin feel. Oscillatory brain activity was measured using a 129-channel electroencephalography system. In 31 participants, changes in oscillatory band power were evaluated in relevant frequency bands during touch exploration periods. For the artificial skin study, the soft condition led to lower alpha-band power over bilateral somatosensory cortices, which has previously been proposed as a marker of reduced roughness, compared to the draggy condition. Similar results were obtained during the self-touch paradigm, which additionally led to reduced theta-band changes over the frontal and central-parietal electrodes indicating modulation of activity involved in affective (pleasant) touch. Using a novel and highly controlled experimental approach using a novel artificial skin paradigm and active exploration during self-touch of participants own skin, we were able to demonstrate for the first time the neural correlates associated with softness perception of skin and their impact on brain activity associated with affective touch, thereby advancing our understanding of brain oscillatory activity during active-touch exploration of skin. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
BackgroundWith population aging and an increase in sports injuries, articular cartilage wear has become increasingly severe, significantly impairing patients' quality of life. Cross-shear motion is a common loading pattern in daily joint activities, yet its effects on cartilage and counterpart materials remain insufficiently investigated.ObjectiveTo investigate the wear behavior and underlying mechanisms of articular cartilage under cross-shear motion, and to compare the wear resistance of different artificial joint materials, so as to propose material optimization strategies suitable for this motion pattern.MethodsFresh bovine knee femoral cartilage was used to simulate the human cartilage environment under various cross-shear conditions and loading regimes. Stepwise loading, microhardness testing, cartilage compression deformation measurements, and surface wettability tests were performed to systematically analyze the tribological characteristics and wear mechanisms of each material. The evaluated materials included CoCrMo, ultra-high molecular weight polyethylene, and polyether-ether-ketone (PEEK).ResultsCross-shear motion significantly increased wear in all materials. CoCrMo exhibited the largest increase (ΔK = 230.51 ± 25.67%), while PEEK showed the smallest increase in wear rate (138.37%). No significant linear correlation was found between material hardness and cross-shear wear rate (r = -0.32, p > 0.05). In terms of dynamic wettability, PEEK performed best, with a hysteresis angle 38.46% lower than that of natural cartilage, and its energy dissipation parameters were close to those of natural cartilage, indicating the best cartilage compatibility.ConclusionUnder cross-shear conditions, PEEK demonstrates the best overall wear resistance and biomechanical compatibility, suggesting its potential as a promising material for joint repair. The wear mechanisms revealed in this study provide experimental evidence and reference for performance optimization and clinical selection of artificial joint materials.
Manual gating for plasma cell (PC) identification in multiparametric flow cytometry (MFC) is time-consuming and operator-dependent, especially when PCs are scarce. Artificial intelligence approaches such as unsupervised clustering (e.g., FlowSOM) map high-dimensional data that still require expert interpretation. The primary aim of this work was to develop a lightweight, transparent, spreadsheet-based algorithm for a classification model that integrates with automated clustering outputs for standardized B-cell and PC identification in research flow cytometry datasets. Bone marrow aspirates were stained with a standard BD OneFlow™ PC screening tube (CD38, CD56, β2-microglobulin, CD19, cyIgκ, cyIgλ, CD45, CD138) and acquired on a BD FACSLyric™ flow cytometer. FCS files were exported to CellEngine cytometry software and singlet nucleated events underwent FlowSOM clustering (8 clusters/sample). Cluster-level median fluorescence intensities (MFIs) were exported to an Excel "PC Trainer Classifier" that (i) normalizes markers to an in-sample B-cell anchor, (ii) computes a PCscore with CD138 as a hard gate and CD38 as a soft gate, plus secondary features (CD19↓, CD45↓, CD56↑), (iii) applies a forced core fallback (highest CD38/CD138 core score) when strict criteria yield no PCs, and (iv) derives a NEOscore (CD56↑, CD19↓, CD45↓) for neoplastic phenotype. The rule-based classifier was trained on expert assigned PC and B-cell clusters from 40 samples (30 clonal and 10 polyclonal). Validation was done on a new set of 52 samples, independent of the model. Elements of the Excel formula design and error-proofing were co-developed with ChatGPT (OpenAI); all outputs were verified by the authors. Across 52 validation cases (8 clusters/case; 416 clusters total), B-cell detection achieved: Sensitivity 0.902 (0.79-0.96), Specificity 0.984 (0.96-0.99), Precision 0.885 (0.77-0.94), Accuracy 0.973 (0.95-0.99) and F1 0.893. PC identification achieved: Sensitivity 0.651 (0.54-0.75), Specificity 0.828 (0.79-0.86), Precision 0.458 (0.37-0.55), Accuracy 0.796 (0.76-0.83) and F1 0.537. A transparent Excel-based classifier integrated with FlowSOM clustering enables highly reproducible B-cell identification and provides a structured approach to PC classification in research flow cytometry datasets. While the B-cell classifier demonstrated excellent discriminatory performance, PC identification yielded moderate sensitivity and precision, likely reflecting underlying biological and phenotypic heterogeneity. Consequently, the PC classification component is best interpreted as a triage or augmented-intelligence tool intended to support, rather than replace, expert assessment. This approach provides a structured and auditable framework that reduces operator dependency and improves inter-case harmonization. Its interpretability, low cost and portability make it particularly suited to research laboratories operating in resource-variable settings. Further optimisation and prospective validation may refine PC classification performance.
ICU procedures are increasingly complex, often requiring deeper sedation or general anesthesia, and are increasingly performed at the bedside to avoid transport risk. This trend has expanded the role of anesthesiologists in ICU nonoperating room anesthesia (NORA). This review summarizes practical anesthetic considerations for these procedures, focusing on optimization, monitoring, and systems issues. ICU patients frequently have shock, hypoxemia, right ventricular failure, metabolic acidosis, organ dysfunction, and neurologic injury that change anesthetic pharmacokinetics and hemodynamic responses. Bedside tracheostomy, percutaneous endoscopic gastrostomy, extracorporeal membrane oxygenation cannulation, bronchoscopy, thoracic interventions, and selected neurosurgical and interventional radiology procedures are feasible in the ICU but carry higher rates of cardiorespiratory events than operating room cases, largely because of illness severity and environmental constraints. Short-acting sedatives, noninvasive respiratory support, point-of-care ultrasound, and structured checklists, simulation, and dedicated ICU NORA pathways can reduce complications. Emerging artificial intelligence and machine learning tools that process physiologic and waveform data may further improve risk stratification, early detection of instability, and decision support. Bedside ICU procedures blur the boundaries between sedation, monitored anesthesia care, and general anesthesia. Effective practice requires individualized plans, clear rescue pathways, and coordination between anesthesia, ICU, and procedural teams, supported by advanced monitoring and data-driven decision support.
Autosomal Dominant Polycystic Kidney Disease (ADPKD) is the most prevalent hereditary kidney disorder. While the disease is well-defined, its clinical course is highly variable, making the prediction of individual patient outcomes a significant challenge for clinicians and a source of psychological distress for those affected. Current prognostic tools fall into two primary categories: imaging-based models and genetic scoring systems. However, these unimodal approaches have inherent limitations. Imaging-based metrics often lose prognostic resolution in advanced disease stages as fibrosis begins to outweigh cyst expansion. Meanwhile, genetic scoring often fails to account for intrafamilial variability and may underestimate risk in a significant percentage of rapid progressors. Upcoming tools seek to fill these gaps by exploring new dimensions of the disease, including genome-wide polygenic scores (GPS) to account for background genetic influences and imaging approaches that use artificial intelligence that can capture cyst architecture and parenchymal changes beyond conventional volumetric measures. Future directions in ADPKD prognostication point toward multimodal frameworks that integrate AI-derived imaging features, genomic risk measures, clinical risk factors, and molecular biomarkers. Integrated prognostic tools could help translate complex disease information into more consistent, clinically actionable guidance for treatment decisions and shared decision-making.
The ability to acquire iron drives microbial fitness in marine environments where iron solubility is extremely low. Iron uptake via the siderophore aerobactin plays an important role in Vibrio fischeri colonization in the light organ of the Hawaiian bobtail squid. To date, the contribution of aerobactin to this mutualistic relationship has not been demonstrated quantitatively. Here we report a revised total synthesis of aerobactin, characterize its stability and iron-binding properties, and demonstrate its importance to Vibrio-squid mutualism. Synthetic aerobactin was characterized by nuclear magnetic resonance (NMR) and high-resolution mass spectrometry and iron binding was measured via the Chrome Azurol S (CAS) assay. Stability experiments in artificial seawater showed that aerobactin degrades slowly, with a half-life on the order of weeks. The activity of synthetic aerobactin was then demonstrated by growth recovery experiments with wild-type V. fischeri ES114 and mutant strains deficient in aerobactin biosynthesis (ΔiucABCD), its outer-membrane aerobactin receptor (ΔiutA), and in the inner-membrane importer (ΔfhuCDB). Available iron was controlled in cell culture experiments by the addition of 2,2'-bipyridine (BPY), bathocuproine disulfonic acid (BCDS), or citrate to experimental growth media. Exogenous aerobactin restored growth in iron-limited environments in a concentration-dependent manner, particularly in the biosynthesis-deficient mutant. Additionally, we report that BPY inhibits V. fischeri ES114 growth with a broad MIC range (128-256 μM) that highlights its capacity to disrupt the intracellular iron pool. Together, these findings support the use of AB-FeCit as the optimal media system for testing siderophore-dependent growth in V. fischeri and link synthetic aerobactin and iron availability to bacterial growth in a model marine mutualism.
Precise regulation of Cas12a activity is crucial for expanding its application in molecular diagnostics. However, existing split crRNA systems exhibit hardly any activation efficiency at low-abundance target and lack a well-defined regulated mechanism, representing a persistent bottleneck for practical application. This work proposes a DNA-guided spatially ordered assembly of split crRNA for activating CRISPR/Cas12a (DIRECTOR) strategy. This work combines artificial intelligence-driven AlphaFold3 structure prediction, computer-powered molecular dynamics simulations with fluorescence analysis to demonstrate that the 3' terminal extension of activator acts as a spatial director, utilizing DNA-guided spatially ordered assembly of split crRNA and stabilizing key Cas12a domains, thereby activating Cas12a. Conversely, the 5' terminal extension serves as a spatial misdirector, inhibiting Cas12a activation by destabilizing the protein structure and introducing the steric hindrance to shield the catalytic center. Furthermore, the structural and energy thresholds required for effective Cas12a activation were identified. Finally, utilizing the spatial director as an energy amplification element, DIRECTOR achieves a limit of detection as low as 42.1 fM for single-target miR-155 and dual-response detection of wide-scope nucleic acids. Owing to its direct activation strategy, DIRECTOR provides mechanistic insights for affordable and programmable CRISPR molecular diagnostics.
Ergonomics is essential for ensuring safe, high-quality surgical care. However, existing studies vary widely in their clinical settings, terminology, and assessment methods. This scoping review aimed to map the terminology and methods used to evaluate surgeons' ergonomics in real-world surgical environments. We systematically searched MEDLINE, Embase, CENTRAL, IEEE Xplore, the WHO International Clinical Trials Registry Platform, and ClinicalTrials.gov from inception to July 1, 2025 for studies that assessed physical, cognitive, or organizational ergonomics. Two reviewers independently screened titles and abstracts, assessed full texts, and extracted study characteristics and outcomes. Assessment tools were categorized as self-reported, observational, direct or instrumental, or computer based. The review protocol was registered in PROSPERO (CRD420251088370). Ninety-nine studies met the inclusion criteria. Minimally invasive surgery predominated: 60 studies involved laparoscopic surgery (including thoracoscopic and other endoscopic minimally invasive approaches), and 36 evaluated robotic surgery. Comparative designs most frequently compared laparoscopic versus robotic surgery (n = 14). Physical, cognitive, and organizational ergonomics were assessed in 64, 49, and 19 studies, respectively, with 31 addressing multiple domains. Considerable variation was observed in terminology and assessment methods. Physical ergonomics was most commonly evaluated using observational or instrumental approaches, whereas cognitive ergonomics primarily relied on self-reported measures. Computer-based assessments, including artificial intelligence-assisted tools, have recently emerged. This review provides a structured framework for assessing ergonomics in surgery and highlights substantial heterogeneity in terminology and assessment methods. Standardized, multimodal, and objective approaches are needed to enhance comparability across studies.
Research on potentially inappropriate care at the end of life in older adults rarely distinguishes between acute and elective care. As elective care is plannable, it allows for timely deliberation on its appropriateness. This study aimed to explore elective hospital care and specific elective treatments in the final three months of life to identify areas warranting re-evaluation. A nationwide retrospective observational study was conducted using linked administrative data, including data from electronic patient records and health insurance claims from all secondary and tertiary hospitals across the Netherlands. Included were 104,544 older adults aged ≥ 65 who died between April and December 2019. In the final three months of life, 41.7% of older adults received no hospital care, 14.5% received outpatient care only, 42.4% were admitted, and 15.9% were electively admitted. Some of the most frequent elective treatments were chemo- and/or immunotherapy (12,102), radiotherapy (13,924), dialysis (30,087), and cataract surgery (584). Treatments during elective admissions included gastrointestinal endoscopies (979), gastrointestinal tract organ resections (229), gastrointestinal-ostomy surgeries (228), exploratory laparotomies (177), heart valve surgeries (179), arterial interventions (coronary: 266; aorta/side branches: 147; peripheral: 786), hip surgeries (136), leg amputations (115), and artificial feeding procedures (683). A substantial proportion occurred in the final month of life. Elective hospital care is common in the final three months of life among older adults, with limited time to experience meaningful benefit. These findings highlight the need to reconsider the appropriateness of elective care at the end of life to better align care with patients' preferences.