This Viewpoint explores the potential for integration of artificial intelligence (AI) into ophthalmology clinic workflows and shares an example of such an integration in Beijing, China.

This diagnostic study compares the ability of 6 different large language models (LLMs) to identify missed opportunities for diagnosis among patients admitted to emergency departments (EDs) that use electronic trigger (eTrigger) tools for quality and safety surveillance.

This case series evaluates the accuracy of artificial intelligence smart eyeglasses in the identification and description of single and multiple objects as a pilot feasibility study of a potential intervention for patients with low to no vision.

This cohort study examines which features of naturalistic speech are associated with concurrent variation in symptom severity in adult patients with schizophrenia spectrum disorders in Dutch and US samples.

This cluster randomized trial assesses whether the receipt of targeted feedback based on artificial intelligence (AI)-enabled medical record abstraction can improve physician compliance with the Centers for Medicare & Medicaid Services Severe Sepsis and Septic Shock Management Bundle.

This Viewpoint describes a Centers for Medicare & Medicaid Services (CMS) payment model with an outcome-aligned payment structure for behavioral health care delivered by artificial intelligence (AI) systems.

This Viewpoint describes writing letters of recommendation in the era of artificial intelligence.

This prognostic study describes the development, deployment, and evaluation of an artificial intelligence (AI) language tool for generating preliminary sign-outs to support a urine drug testing service.

This quality improvement study investigates the association of an electronic health record-integrated artificial intelligence note summarization tool with emergency physician medical record review time and user experience.

This Viewpoint proposes that routine adoption of clinical artificial intelligence (AI) requires explicit alignment of payer, primary beneficiary, value metric, and life cycle accountability.

This cohort study aims to develop and validate machine learning models to predict major 30-day postoperative complications after glossectomy and to compare performance with the American College of Surgeons National Surgical Quality Improvement Program risk calculator.

This stepwise evaluation uses a prospective silent-mode validation study followed by a randomized clinical trial to assess the diagnostic and referral performance of an artificial intelligence–optical coherence tomography (OCT) system for detection of diabetic macular edema within a diabetic retinopathy screening pathway in clinical settings in Hong Kong.

Nature Machine Intelligence, Published online: 14 July 2026; doi:10.1038/s42256-026-01271-3

Realigning AI technology towards the Sustainable Development Goals

Nature Machine Intelligence, Published online: 14 July 2026; doi:10.1038/s42256-026-01259-z

Kindt et al. present a unifying framework for superposition in neural networks. Their three-step approach clarifies how latent features can be identified, disentangled and assessed.

Nature Machine Intelligence, Published online: 13 July 2026; doi:10.1038/s42256-026-01273-1

The brain’s architecture exhibits diversity across many temporal and spatial scales, yet our computing architectures remain largely homogeneous. Low-powered neuromorphic hardware offers a path towards energy-efficient AI, but could these approaches be improved with heterogeneous computing architectures?

Nature Machine Intelligence, Published online: 13 July 2026; doi:10.1038/s42256-026-01272-2

Sartore et al. present ergoCub, a humanoid robot that prioritizes human safety at hardware and motion levels. Using a shared embodied intelligence framework, design and control are jointly optimized with human-related metrics such as back stress alongside locomotion objectives, reducing spinal load and improving walking robustness.

Nature Machine Intelligence, Published online: 13 July 2026; doi:10.1038/s42256-026-01260-6

Song et al. propose Sustainability Robotics as a new discipline to overcome fragmentation and enhance societal and environmental impact. They define three guiding principles, alongside two dimensions spanning sustainable design and robotics for sustainability.

Nature Machine Intelligence, Published online: 10 July 2026; doi:10.1038/s42256-026-01270-4

The push to make biological data ‘AI-ready’ is accelerating worldwide. But to understand what AI-ready means for biological data, there are several questions to answer.

Nature Machine Intelligence, Published online: 06 July 2026; doi:10.1038/s42256-026-01262-4

Park and Walsh introduce a reinforcement learning framework that could accelerate the discovery of new, thermodynamically stable and diverse crystalline materials with desired properties.

Nature Machine Intelligence, Published online: 03 July 2026; doi:10.1038/s42256-026-01267-z

Berner et al. show how to adapt popular neural networks into discretization-agnostic neural operators that learn from continuous scientific data, enabling scientific simulations that generalize more reliably across resolutions.

npj Digital Medicine, Published online: 23 July 2026; doi:10.1038/s41746-026-03013-6

Promoting help-seeking using e-technology for adolescents with mental health problems: Results from a RCT within the ProHEAD Consortium

npj Digital Medicine, Published online: 22 July 2026; doi:10.1038/s41746-026-03047-w

Shaping the future of cancer screening with artificial intelligence-empowered multi-cancer early detection

npj Digital Medicine, Published online: 22 July 2026; doi:10.1038/s41746-026-03049-8

Video observed therapy (VOT) for people with tuberculosis (TB): a scoping review

npj Digital Medicine, Published online: 22 July 2026; doi:10.1038/s41746-026-02846-5

Agile legislation adapts principles from agile software development to lawmaking, emphasizing iteration, multi-stakeholder feedback, and embedded revision. We outline this learning-oriented governance model using three case studies: Germany’s stepwise digital health legislation, the EU AI Act, and U.S. FDA user-fee reauthorization. These examples highlight legislative designs that enable structured generation of real-world data and evidence during implementation, informing regulatory interpretation and iterative refinement in rapidly evolving technological domains.

npj Digital Medicine, Published online: 22 July 2026; doi:10.1038/s41746-026-02467-y

Large language models for interpretation of health checkup results

npj Digital Medicine, Published online: 21 July 2026; doi:10.1038/s41746-026-03036-z

Large-scale bilateral cardiovascular monitoring via wearable rings

npj Digital Medicine, Published online: 21 July 2026; doi:10.1038/s41746-026-02964-0

Real-world deployment of remote sleep monitoring technologies reveals distinct patterns associated with cognitive decline

npj Digital Medicine, Published online: 21 July 2026; doi:10.1038/s41746-026-03026-1

Developer evidence and user satisfaction in mental health apps across China and the United States

Nature Medicine, Published online: 17 July 2026; doi:10.1038/d41591-026-00037-z

The AI agent Biomni performs research tasks across diverse biomedical fields; with further training and optimization, it could be a powerful research partner for scientists.

Nature Medicine, Published online: 16 July 2026; doi:10.1038/s41591-026-04569-2

Author Correction: Evolution of claudin18.2 therapies in gastroesophageal cancers

Nature Medicine, Published online: 16 July 2026; doi:10.1038/s41591-026-04581-6

Author Correction: Integrated community-based HIV and sexual and reproductive health services for youth: a cluster-randomized trial

Nature Medicine, Published online: 16 July 2026; doi:10.1038/d41591-026-00036-0

Using immunopeptidomics, researchers identify conserved Plasmodium T cell antigens, revealing potential for cross-stage, cross-species malaria vaccine development.

Nature Medicine, Published online: 16 July 2026; doi:10.1038/s41591-026-04498-0

A bidirectional sensorimotor neuroprosthetic system that decodes brain signals associated with movement intention while delivering patterned neuromodulation to the spinal cord and cortex provides long-term recovery of hand motor and sensory function, even when the device is off.

Nature Medicine, Published online: 16 July 2026; doi:10.1038/s41591-026-04533-0

Combining the CD19–4-1BBL co-stimulatory molecule englumafusp alfa with the bispecific antibody glofitamab in patients with relapsed or refractory aggressive B cell non-Hodgkin lymphoma showed acceptable safety signals and encouraging preliminary clinical responses, with mechanistic data supporting the rational choice of therapy.

Nature Medicine, Published online: 15 July 2026; doi:10.1038/s41591-026-04526-z

Our pregnant patients deserve accurate conversations about risk.

Nature Medicine, Published online: 15 July 2026; doi:10.1038/s41591-026-04515-2

Spatial and circulating multi-omics of pig-to-human extracorporeal liver cross-circulation reveals an early innate immune response, species-specific complement dynamics and xenograft–platelet interactions, while showing preserved metabolic support even after removal of the native liver. Von Willebrand factor, endothelium, hepatocytes, and resident and infiltrating immune cells were implicated in xenograft-associated thrombocytopenia.

AI is no longer a future-state consideration for healthcare organizations. It’s already embedded in documentation workflows, operational reporting, care coordination, and scheduling systems across the industry. For security and compliance teams, that shift happened fast, and the governance infrastructure in most organizations has not kept pace.  While AI adoption is accelerating, so are concerns around ...;

While generative AI has flooded the enterprise software market with chatbots and thin-wrapper copilots, the pharmaceutical industry has largely had to sit on the sidelines. In a sector where a single hallucination can halt production, violate FDA regulations, or delay a life-saving drug, being “approximately right” is worthless. Enter Katalyze AI, a startup that just ...;

Introduction  Living with a chronic illness can feel profoundly isolating. Whether someone is managing rheumatoid arthritis, ankylosing spondylitis (AS), lupus, or another long-term condition, the challenges extend far beyond physical symptoms. Many patients face prolonged uncertainty, emotional distress, and a persistent feeling that few people truly understand their daily reality.  Communities and patient-led organisations such ...;

Callie Care Inc., an AgeTech startup building a proactive phone-first voice AI that calls seniors daily — helping them fight isolation, manage everyday needs, and stay connected to their families and care teams — has received non-dilutive funding from InterSystems Ventures and closed a $500K pre-seed round backed by angel investors. It addresses one of ...;

Healthcare companies are starting to use AI in parts of the business that rarely get the same attention as clinical tools. Diagnosis support, documentation and treatment recommendations are still where most of the public scrutiny goes, for good reason. Those tools sit close to patient care, and the risks are easier to understand.  But some ...;

The first time I used a meeting transcription bot, I spent the next hour regretting it. Not because the transcript was bad. It was fine. Accurate, even. The problem was the room. Three people on my team became noticeably more careful the moment they realized the meeting was being recorded. One senior engineer angled his ...;

Telemedicine software supports the full digital care flow, including scheduling, identity checks, video visits, secure messaging, prescriptions, documentation, billing handoffs, and patient follow-up. Each step creates data movement, so healthcare teams need a product view that includes privacy, security, workflow, and clinical operations from the start. Why Telemedicine Requires More Than a Video Link HIPAA ...;

In a major move to bring AI to the bedside, healthtech startup HealthLeap announced today that it is deploying its AI-driven clinical screening platform across the entire Houston Methodist health system. The enterprise-wide rollout will automatically screen all of Houston Methodist’s 150,000-plus annual inpatients for clinical risks, starting with a massive, often-overlooked hospital liability: malnutrition. ...;

Medicine is advancing at an extraordinary pace. We can now sequence genomes, deliver precision therapies tailored to individual patients, develop groundbreaking biotechnology and perform surgical procedures that would have seemed impossible just decades ago. Yet despite these remarkable achievements, healthcare systems continue to be challenged by far more basic issues. Medication errors, misread X-rays and test results, and ...;

Objective;This study developed a radiomics-based pipeline to identify suspicious findings on 2D screening mammograms by training models to distinguish radiologist-annotated abnormalities from normal breast tissue.Methods;A total of 1604 screening mammograms (<span style="font-style:italic;">n; = 1294 participants) were used in this retrospective study. Each mammogram included an original capture image, and a secondary capture image with a single radiologist-drawn annotation indicating a region of interest (ROI) with an abnormality. The annotation on each secondary capture image was used to select an ROI in the original image. An ROI with normal tissue was automatically selected from the remaining breast tissue for comparison. Radiomics features were extracted from the ROIs with abnormalities and normal tissue. Feature selection was performed using the SciKit-Learn SelectKBest method with Chi-squared, analysis of variance (ANOVA) <span style="font-style:italic;">F;-, and mutual information score functions. Logistic regression, random forest, XGBoost, bagging, discriminant analysis (DA), and support vector machine classifiers were trained on the selected features. The model performance was evaluated with the area under the receiver operating characteristic curve (AUC) on a holdout test set.Results;While AUC values ranged from 0.69 to 0.73, no significant differences were observed between models (DeLong test, <span style="font-style:italic;">P; > .05). The nominally highest performance was achieved via ANOVA <span style="font-style:italic;">F;-score feature selection and DA (AUC: 0.73; 95% CI, 0.70-0.77).Conclusion;The radiomics-based pipeline shows promise in distinguishing abnormalities from normal tissue on screening mammograms.Advances in knowledge;Radiomics has the potential to enhance breast cancer detection and is a step towards the integration of advanced machine learning into screening workflows.;

<span class="paragraphSection">Abstract;Artificial intelligence (AI) can revolutionize clinical workflows in radiology but requires organizational change. An institutional strategy to develop and evaluate AI tools is outlined. A multidisciplinary AI board with a patient and public involvement and engagement group was created. A comprehensive framework was formed, comprising workstreams covering information governance; technical rigor, performance, and safety; economic considerations; and ethical and medical-legal aspects. In addition to recurring meetings, a workshop with clinicians, information technology specialists, and patient representatives helped to identify priority use cases. Technical infrastructure was enhanced to support the development, performance assessment, and deployment of AI tools. Primary areas for AI applications included training staff, vetting of image requests, quality assurance, image interpretation, and communicating imaging findings to patients. Potential barriers, gaps in evidence, and subsequent actions for AI implementation were outlined. Avenues for collaboration with industry and market-available solutions were outlined. A virtual Picture Archiving and Communication System server was developed and then connected to a deployment platform for performance evaluation of AI products. Establishing an institutional AI board and imaging AI sandbox has guided safe, effective AI implementation while creating an ideal setting for innovation and industry partnership. Our approach to the integration of imaging AI provides a pragmatic guide for other institutions.;

Objectives;This study aimed to restore missing regions from the limited field of view (FOV) using image- and sinogram-based conditional GAN (cGAN) models.Methods;cGANs are deep learning frameworks that generate realistic data via a competitive neural network process. We used planning CT (pCT) datasets from 96 patients: 64 for training, 16 for validation, and 16 for internal testing. Two cGAN models (image-based and sinogram-based) were developed to generate body contour outside the FOV. Next, 23 cone-beam CT (CBCT) datasets were evaluated as an external test group.Results;In pCT internal test datasets, the median values for mean absolute error (MAE), root mean square error (RMSE), and structural similarity index measure (SSIM) for each model were as follows: image-based model—101.73 HU for MAE, 39.26 HU for RMSE, and 0.83 for SSIM; sinogram-based model—16.91 HU for MAE, 23.19 HU for RMSE, and 0.91 for SSIM. In CBCT external test datasets, the sinogram-based model outperformed the image-based model with a median MAE of 73.32 HU versus 180.72 HU, a median RMSE of 37.02 HU versus 43.42 HU, and a median SSIM of 0.75 versus 0.63. The sinogram-based model demonstrated significant improvements in MAE, RMSE, and SSIM (<span style="font-style:italic;">P ;< .05).Conclusions;The sinogram-based cGAN model exhibits considerable potential for restoring missing regions outside the FOV, outperforming the image-based model in accuracy metrics.Advances in knowledge;This model offers a novel approach to accurately predict missing regions from a limited FOV, enhancing continuity of the body contour while accommodating patient-specific variations.;

Objectives;Artificial intelligence (AI) has shown promise for estimating volumetric breast density values from processed, “for presentation,” mammograms. However, previous evaluations have typically used small datasets or focused on a single vendor. In this study, we aimed to improve volumetric breast density estimation from processed mammograms for the three main UK vendors with a combination of improved training methods and the utilization of up-to-date data from the large OPTIMAM Mammography Image Database (OMI-DB).Methods;Paired processed/unprocessed mammograms were obtained from OMI-DB. Ground-truth, image-level density values were calculated by passing unprocessed images through a commercial density estimation tool. AI tools, comprising feed-forward convolution neural networks, were then trained to reproduce these values from the corresponding processed mammograms.Results;Patient-level AI predictions for volumetric breast density demonstrated strong correlation with ground-truth values derived from unprocessed image counterparts (<span style="font-style:italic;">r; = 0.954-0.976). Models trained on less prevalent manufacturers performed worse (<span style="font-style:italic;">r; = 0.954 compared to 0.976 for the most prevalent manufacturer), highlighting the importance of collecting larger training datasets in future. Error levels were higher in patients with dense breasts. Model performance was generally consistent across screening sites but correlated with patient age, possibly due to the correlation of age and breast density.Conclusions;The presented models demonstrated good performance overall and were generally consistent across screening sites.Advances in knowledge;The presented AI tools provide a means of estimating breast density from processed mammograms, enabling further research into breast cancer epidemiology and risk where only processed mammograms are available.;

<span class="paragraphSection">Abstract;This paper presents a reproducible, data-driven approach for prioritisation of AI-detected chest X-ray (CXR) findings to support faster lung cancer diagnosis in the NHS. The Annalise Enterprise CXR system was deployed in shadow mode across seven acute trusts in Greater Manchester. Two cohorts were used: a retrospective cancer cohort (<span style="font-style:italic;">n; = 1,282) with confirmed lung cancer and visible CXR abnormalities, and a prospective cohort (<span style="font-style:italic;">n; = 13,802) comprising consecutively acquired GP-referred CXRs. Prevalence ratios were calculated for 124 AI-detected abnormalities across both cohorts, and three prioritisation strategies were developed. Strategy 3, which combined prevalence analysis with expert clinical review, achieved optimal performance with a sensitivity of 95.87% and estimated specificity of 79.11%, while maintaining a negative predictive value of 99.95%, for identification of lung cancer. Findings most associated with cancer included solitary lung mass, mediastinal mass, and hilar lymphadenopathy. An Excel-based tool was developed to support rapid configuration and evaluation of categorisation. Application of this approach enabled safe deployment of AI using shadow mode to inform configuration prior to live use. This work provides a scalable model for AI implementation in radiology workflows that aligns with the National Optimal Lung Cancer Pathway and addresses real-world challenges of diagnostic capacity, safety, and reproducibility.;

Objective;To assess the cost-effectiveness of using artificial intelligence (AI)–derived software to assist reading CT scans of the chest to identify and analyse lung nodules compared to unaided reading in symptomatic, incidental and screening populations.Methods;Decision tree structures were developed in TreeAge Pro 2021. Structures were informed by British Thoracic Society clinical guidelines and clinical opinion. Results were presented as incremental cost-effectiveness ratios (ICERs) expressed as cost per quality-adjusted life-year (QALY) over a lifetime from the UK National Health Service and Personal Social Services perspective.Results;For the symptomatic population, the unaided radiologist reading strategy dominated the AI-assisted reading strategy. In the incidental population, unaided radiologist reading was cost-effective with an ICER of approximately £1000 per QALY. Conversely, in the screening population, AI-assisted radiologist reading dominated unaided reading. The cause of AI assistance being cost-effective depended on the number of people who had undergone CT surveillance because of non-cancerous findings. Given the limitations in the quality and quantity of evidence to inform inputs, these results should be interpreted with caution.Conclusion;Current analyses based on limited evidence suggested that, in the symptomatic and incidental populations, unaided radiologist reading may be the more cost-effective strategy, while in the screening population, AI-assisted radiologist reading appeared to be the dominant strategy. Better quality evidence is required to have a definitive answer about their cost-effectiveness.Advances in knowledge;This paper shows whether adding AI-derived software to radiologists' reading of CT scans to identify lung nodules offers good value for money.;

<span class="paragraphSection">Abstract;Radiology is undergoing a major shift with the growing use of artificial intelligence (AI), and more change is expected with the emergence of agentic AI—systems that can initiate, manage, and coordinate tasks. So far, most discussions about AI’s impact on radiology follow 2 main approaches. The first, the “displacement” approach, tries to predict which jobs are most at risk of being replaced by AI. This narrative often warns that radiologists may be displaced. The second, the automation-versus-augmentation approach, looks within jobs to identify which tasks are likely to be fully automated (automation) and which will be improved by AI working alongside humans (augmentation). This paper introduces a third approach: <strong>reconfiguration</strong>. Instead of focusing on job loss or task replacement, the reconfiguration model looks at how AI changes the way tasks connect, how responsibilities shift, and how professional roles evolve. Drawing on recent research and developments in AI, this paper advances the reconfiguration approach and articulates why it offers a clearer way to understand—and help shape—the future of work in radiology. This paper offers a forward-looking reflection on the shifting nature of radiological work—clinically, educationally, and organizationally—as AI systems become increasingly integrated into practice.;

Objectives;Deep learning neural network (DLNN)-based tools can automate body composition analysis for cancer cachexia research. We aimed to evaluate a DLNN tool trained on a European population of Chinese cancer patients.Methods;Computed tomography (CT) images at the 3rd lumbar vertebral (L3) level of Chinese gastric cancer patients were retrospectively collected. An externally validated DLNN tool (Mosamatic) was used to segment skeletal muscle, visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT). Manual segmentation was performed using SliceOmatic software (TomoVision, version 5.0). Geometric similarity between automated and manual segmentation, and the reliability was assessed.Results;The cohort comprised 203 patients with a median body mass index (BMI) of 22.2 kg/m², and 604 CT images at L3 were collected. The median Dice Similarity Coefficient (IQR) of skeletal muscle, VAT and SAT were 0.973 (0.961-0.980), 0.980 (0.964-0.989), and 0.967 (0.945-0.977), respectively. The median Lin’s Concordance Correlation Coefficient for skeletal muscle area (0.983), VAT area (1.000), SAT area (0.998), skeletal muscle radiation attenuation (0.995), VAT radiation attenuation (0.994), and SAT radiation attenuation (0.997) demonstrated excellent reliability. Low BMI (<18.5 kg/m²) and ascites impaired the agreement between the 2 methods. The automated method showed high diagnostic concordance with manual segmentation for sarcopenia (<span style="font-style:italic;">κ ;= 0.843, <span style="font-style:italic;">P ;< .001) and myosteatosis (<span style="font-style:italic;">κ ;= 0.946, <span style="font-style:italic;">P ;< .001).Conclusions;The Mosamatic tool displays excellent generalizability to analyse body compositions in Chinese gastric cancer patients and can facilitate cachexia research.Advances in knowledge;The Mosamatic tool displayed excellent generalizability without recalibration to analyse body composition on the 3rd lumbar vertebral CT images in Chinese gastric cancer patients.;

<span class="paragraphSection">Abstract;Recent advances in artificial intelligence (AI) offer significant potential to address the growing bottleneck in radiology caused by an increasing volume of imaging studies amidst a global shortage of radiology professionals. This study presents a comprehensive review of the latest developments in AI, particularly in vision-language models for radiology report generation, providing radiologists with a current reference. We conducted a focused literature search for studies published from 2020 to 2024 and included 14 studies in our review specifically on chest X-ray datasets with limited coverage of 3D modalities, reflecting the early stage of research and ongoing methodological advances in report generation for volumetric imaging. We analysed the model architectures, report generation capabilities, training datasets, evaluation metrics, and performance of these models. Our review highlights the evolution of AI in radiology report generation and underscores the critical need for diverse datasets and standardized evaluation metrics. Despite rapid progress, current AI models are not yet capable of consistently producing high-quality reports and require further improvements in data diversity, model training, and evaluation metrics to achieve a level comparable to human experts.;

Objectives;There is a growing need to develop user-friendly, bladder-specific image analysis tools that can produce reliable artificial intelligence (AI)-quantitative imaging biomarkers (QIBs) derived from multiparametric (mp)MRI data for clinical applications. To address it, we developed an AI-powered BLADdEr multiparametric MRI Analysis for Clinical Application (AI-BLADE, current release v1.0) toolbox designed for extracting mpMRI-derived quantitative metrics.Methods;AI-BLADE is an advanced tool for bladder-specific mpMRI data analysis with 2 core functionalities: (1) Deep Feature Analysis (MRI-DFA toolkit) and (2) Data-Driven Model-Based Analysis (MRI-MBA toolkit). AI-BLADE offers customizable options and serves as a one-stop shop solution for bladder cancer (BCa) clinical applications. The models within DFA and MBA were tested separately on 2 patient cohorts. DFA was used to classify BCa histology subtypes (<span style="font-style:italic;">n; = 104) with T2-weighted images, while MBA was used to interrogate tumour physiology by deriving mpMRI QIBs, including apparent diffusion coefficient (ADC), and volume transfer constant (K^trans) obtained from 34 BCa patients.Results;Out of the 17 AI models tested, the VGG19 model with a decision tree classifier and no feature selection for the fully connected layer 7 achieved the highest area under the curve of the receiver operating characteristic of 0.79 in classifying BCa histology subtypes, demonstrating the strongest performance. The mean ADC and K^trans values were 1.22 × 10⁻³ (mm²/s) and 0.27 (min⁻¹), respectively, reflecting underlying tumour physiology.Conclusion;The AI-BLADE (v1.0), a flexible and user-friendly software toolbox for analysing mpMRI data, shows strong potential for application in BCa oncology, offering capabilities that can enhance diagnostic accuracy and support improved patient outcomes.Advances in knowledge;This is the first study to design, develop, and implement a novel bladder-specific AI toolbox for analysing mpMRI data. AI-BLADE enables an advanced image analysis workflow, facilitating AI-QIB-based clinical decision-making for patients with BCa.;

Objectives;Deep learning models developed for the classification of radiological reports have lacked explainability. We aimed to validate and explain a pretrained classification model by applying it to the removal of confounding data from a radiological dataset.Methods;Two radiologists categorized 2038 anonymized MRI head free-text radiology reports for abnormality and for small vessel disease presence. Of these reports, 80% (<span style="font-style:italic;">n; = 1630) were used to fine-tune pretrained transformer models to classify scans. Five-fold cross-validation was used in model development. The models were tested on the remaining 20% of the reports (<span style="font-style:italic;">n; = 408). SHapley Additive exPlanations (SHAP) were used to explain the results.Results;The models exhibited excellent classification performance, with a mean receiver operating characteristic (ROC) area under the curve (AUC) of 0.98 for abnormality classification and 0.99 for small vessel disease classification. SHAP highlighted relevant words in both cases.Conclusions;This application validated the use of a pretrained transformer in detecting confounding data in research cohorts, and exhibited explainable results that allow the models’ decisions to be understood. By highlighting the specific report terms that drive each prediction, the explainable model output can be reviewed and critiqued by subject matter experts, supporting trust, error analysis, and iterative refinement of AI tools within clinical workflows.Advances in knowledge;This application demonstrates the feasibility of explainable report classification, and the fine-tuned model could be used in future for automatic removal of confounding data from radiology datasets, while providing transparent, case-level justifications that support audit, governance, and clinician acceptance.;

Objectives;Dynamic MRI of the upper vocal tract is increasingly used to study speech. Image segmentation is often required to analyse the organs of speech; however, manual segmentation is labour intensive and time consuming and automatic methods are being developed. In this paper, a new hybrid transformer network is proposed for such task.Methods;We introduce a deep learning-based decoder model termed “Progressively Refinement Decoding (PRORED).” This model incorporates a directional field (DF) module designed to capture the contour details of features. The acquired contour information is leveraged to refine the boundaries both between and within classes. By integrating the DF module at different stages of the decoder, features are enhanced progressively, ensuring a more detailed and accurate segmentation.Results;Our model is evaluated using a publicly accessible speech MRI dataset and a cardiac dataset. The metrics employed are the Dice coefficient and the Hausdorff distance. Results indicate that our model attains an average Dice coefficient of 97.78% and a Hausdorff distance of 6.84 mm. Additionally, our network was able to identify closure patterns more efficiently than the baseline network and previously published work. In addition, the model was also evaluated on a cardiac dataset, and achieved 91.90% dice score.Conclusions;The proposed model leads to a more accurate segmentation of speech MRI data and in particular allows for a better velopharyngeal closure study. The proposed model was also evaluated on a cardiac dataset and achieved competitive performance, showing its strong generalizability.Advances in knowledge;First model that utilizes vision transformer and progressive refinement decoder to segment dynamic speech MRI.;

<span class="paragraphSection">Abstract;Liver diseases consistently plague people’s daily lives as a result of their high morbidity and mortality rates. Ultrasound (US), favoured by its flexibility, free of radiation, cost-effectiveness, and real-time capabilities, has been commonly employed as one of the first-line imaging tools for hepatic conditions. Artificial intelligence (AI) algorithms are increasingly applied to automatically identify intricate patterns and perform quantitative analyses in US imaging, potentially reducing radiologists’ workload and improving diagnostic efficiency. AI-based US has been of substantial assistance in detecting, diagnosing, screening as well as monitoring of various liver diseases, and has attracted extensive attention among the medical community. In this review, we present a general introduction to AI in medical imaging; we next review its rapidly evolving applications in liver US, covering evaluation of hepatic steatosis severity, assessment of liver fibrosis, identification of focal hepatic lesions, preoperative prediction of high-risk pathological characteristics, assessment of postoperative prognosis, and the analysis of the model of integrated application of multi-omics data; finally, we present an outlook on the clinical applications of AI-based US in the liver diseases.;