This Viewpoint discusses artificial intelligence for treatment of diabetic retinopathy.

This quality improvement study compares true hospital discharge dates with those estimated using artificial intelligence (AI) and by case managers.

This comparative effectiveness study examines the ability of a large language model vs human reviewer consensus to identify adverse events from encounter notes from 4 randomized clinical trials.

This study attempts to develop and externally validate a circulating exosomal microRNA-based machine learning model for preoperative prediction of occult early liver metastasis in pancreatic ductal adenocarcinoma.

This Viewpoint discusses problems at each stage of the artificial intelligence prescription refill encounter.

This cross-sectional study examines whether affective generative artificial intelligence use is associated with emotional problems among youth.

This cohort study develops and validates a machine learning–based prediction model for patients with transthyretin cardiac amyloidosis (ATTR-CM).

This cohort study uses natural language processing to assess the administrative burdens of Medicare beneficiaries engaging with care coordinators.

This diagnostic study develops and validates a deep learning algorithm to detect moderate or greater aortic stenosis and prospectively evaluates its performance using artificial intelligence–guided focused cardiac ultrasound acquired by novice operators.

This diagnostic/prognostic study uses postoperative wound photographs to develop and externally validate a deep learning model to detect wounds suggestive of surgical site infections.

This cross-sectional study evaluates how often adolescents use artificial intelligence (AI) chatbots to discuss mental health concerns.

This quality improvement study evaluates whether an electronic health record (EHR)–based large language model (LLM) can accurately identify surgical patients who would be good candidates for comanagement.

This cross-sectional study evaluates whether artificial intelligence–assisted image recognition paired with line-field confocal optical coherence tomography can detect subclinical basal cell carcinoma (BCC) in inconspicuous facial skin among patients at high risk for BCC.

This article implores clinicians to consider whether artificial intelligence models may induce delusionlike beliefs—and how to help overcome this phenomenon.

This Perspective discusses the advantages and disadvantages of physician-led medical care vs that provided by artificial intelligence (AI).

Nature Machine Intelligence, Published online: 03 September 2026; doi:10.1038/s42256-026-01289-7

Wang et al. introduce a hardware-efficient quantum neural operator that overcomes classical linear capacity limits. Using an implicit quadratic frame, it offers accelerated expressivity for solving differential equations in the noisy intermediate-scale quantum era.

Nature Machine Intelligence, Published online: 03 September 2026; doi:10.1038/s42256-026-01295-9

RNA structure and function are hard to infer because annotations are scarce, despite abundant sequence data. Upadhyay et al. trained a self-supervised model on large-scale RNA data that derives biologically meaningful patterns from sequence correlations.

Nature Machine Intelligence, Published online: 01 September 2026; doi:10.1038/s42256-026-01302-z

Representational alignment can reveal similarities between human brain activity and language models. Work now demonstrates that it can also guide learning, improving the reliability of artificial reasoning.

Nature Machine Intelligence, Published online: 01 September 2026; doi:10.1038/s42256-026-01300-1

Lee and Lai study bias-like processing differences in large language reasoning models and find that, for most models, processing stereotypical information takes less computational effort than processing counter-stereotypical information.

Nature Machine Intelligence, Published online: 28 August 2026; doi:10.1038/s42256-026-01299-5

AI integration in Earth system models enhances prediction and modelling capabilities but also amplifies challenges for reproducibility. This Perspective introduces a framework for assessing reproducibility and provides practical ways to strengthen reproducibility in hybrid Earth system models.

Nature Machine Intelligence, Published online: 28 August 2026; doi:10.1038/s42256-026-01283-z

Although language models can be helpful in molecular design, they are not typically calibrated for uncertainty. Rankovic and colleagues present a method to train language models while taking into account the uncertainty of the data.

Nature Machine Intelligence, Published online: 26 August 2026; doi:10.1038/s42256-026-01294-w

When authors use generative AI in cognitive tasks, without spending time and effort to understand the output, a gap opens between what they present and what they can defend. This gap widens as further work is built on top, resulting in epistemic debt.

Nature Machine Intelligence, Published online: 26 August 2026; doi:10.1038/s42256-026-01296-8

Lee, Oh et al. propose interoception as a biologically inspired framework for building more autonomous and adaptive AI agents, learning from living organisms to build autonomous and adaptive intelligence.

npj Digital Medicine, Published online: 05 September 2026; doi:10.1038/s41746-026-03131-1

Senescence-aware filtering facilitates robust prediction of cancer immunotherapy outcomes

npj Digital Medicine, Published online: 05 September 2026; doi:10.1038/s41746-026-03177-1

Unsupervised machine learning for placental disease using cell spatial organization

npj Digital Medicine, Published online: 05 September 2026; doi:10.1038/s41746-026-03108-0

From feasibility to neuroanatomic validity of remote cognitive smartphone assessments in early Alzheimer’s disease

npj Digital Medicine, Published online: 05 September 2026; doi:10.1038/s41746-026-03198-w

Exploring generalizability and explainability of LLMs in classifying clinically rated suicidal ideation using heterogeneous data

npj Digital Medicine, Published online: 04 September 2026; doi:10.1038/s41746-026-03206-z

A voice-biomarker foundation model for ALS monitoring and Parkinson’s screening

npj Digital Medicine, Published online: 04 September 2026; doi:10.1038/s41746-026-03189-x

Platforms for artificial intelligence-enabled infectious disease surveillance

npj Digital Medicine, Published online: 03 September 2026; doi:10.1038/s41746-026-03190-4

Author Correction: Effect of a digital health behaviour change support system on cardiovascular disease risk in a randomized weight loss trial

npj Digital Medicine, Published online: 03 September 2026; doi:10.1038/s41746-026-03185-1

Differences in tone of AI and care team responses to patient messages by patient demographics

Nature Medicine, Published online: 04 September 2026; doi:10.1038/s41591-026-04694-y

An LLM-based clinical assistant that orchestrates multiple tools to integrate sequential electronic health record data can forecast maternal and infant conditions, opening a window of opportunity to enhance risk-stratified care for mothers and infants.

Nature Medicine, Published online: 03 September 2026; doi:10.1038/s41591-026-04637-7

Reply to: Limited benchmarks constrain the conclusions of a general-purpose versus clinical AI comparison

Nature Medicine, Published online: 03 September 2026; doi:10.1038/s41591-026-04682-2

A large, multimodal molecular characterization of the adult human hippocampus provides evidence for sustained neurogenesis and identifies a stalled neurogenic process in major depressive disorder (MDD). Cell- and circuit-specific genetic, epigenetic, stress, immune, metabolic and synaptic mechanisms are identified that underlie impaired hippocampal plasticity, providing a framework for disease subtyping and therapeutic development.

Nature Medicine, Published online: 03 September 2026; doi:10.1038/s41591-026-04638-6

Limited benchmarks constrain the conclusions of a general-purpose versus clinical AI comparison

Nature Medicine, Published online: 03 September 2026; doi:10.1038/d41591-026-00045-z

The first approved PROTAC for breast cancer validated the idea that medicines can work by bringing proteins together rather than by simply blocking them. Now, a new generation of proximity-based therapies is pushing that principle further.

Nature Medicine, Published online: 02 September 2026; doi:10.1038/s41591-026-04604-2

Preeclampsia and fetal growth restriction (FGR) are major causes of fetal morbidity and mortality globally. Both conditions are associated with impaired invasion of the uterus by trophoblasts. We found that lower maternal serum levels of isthmin 2 (ISM2) in early pregnancy are predictive of preeclampsia and FGR and that ISM2 is essential for trophoblast invasion.

Nature Medicine, Published online: 01 September 2026; doi:10.1038/s41591-026-04613-1

Author Correction: Activating mutations in CSF1R and additional receptor tyrosine kinases in histiocytic neoplasms

Nature Medicine, Published online: 01 September 2026; doi:10.1038/s41591-026-04630-0

Leveraging a human brain tissue bank with cognitive data across the lifespan, we identify a role for oligodendrocytes — myelin-producing cells in the brain — in age-related cognitive decline. With worsening cognitive decline, oligodendrocytes become dysfunctional and produce myelin of poor integrity, and experimental modeling of this myelin pathology impairs cognitive performance in aging.

For a decade, “AI in healthcare” meant a diagnostic algorithm reading a scan. In 2026, the technology that’s actually reaching the exam room is far less glamorous: software that listens to the doctor-patient visit and writes up the notes. Ambient clinical documentation — AI that captures a patient-clinician exchange, structures it into a chart entry, ...;

In healthcare AI, a mislabeled data point isn’t just an error; it can become a clinical risk. Medical AI relies on large volumes of accurately labeled data. The process of annotating medical images with diagnostic labels is essential for training healthcare machine learning models.   Demand for high-quality annotation services is rising, supported by the expanding ...;

Artificial intelligence is transforming biomedical research at an unprecedented pace. From accelerating drug discovery to identifying novel disease biomarkers, AI is enabling researchers to analyze complex biological datasets faster and with greater precision than ever before. Yet while computational models continue to evolve, their effectiveness still depends on one fundamental requirement: high-quality experimental data. Behind ...;

Healthcare has spent decades becoming better at treating disease. I want the next decade to be defined by preventing it, and AI is going to play a key role. This is because AI can bring foresight. Rather than waiting for patients to deteriorate before intervening, AI is beginning to identify subtle warning signs days in ...;

The Information Burden No One Talks About Healthcare leaders have spent years trying to address burnout through investments in staffing, retention, wellbeing initiatives, and workforce support programs. These efforts are both necessary and important. Yet as organizations continue to search for solutions, there is another contributor to workforce strain that receives far less attention despite ...;

Some practices are always short-staffed, and it is rarely bad luck. The same hiring mistakes repeat across thousands of clinics, each one adding weeks to every vacancy and pushing good candidates toward employers who move better. Here are the eight most damaging, and what the consistently staffed practices do differently. 1. Screening clinical candidates like ...;

Ask a hospital executive what their new AI does, and the answer is usually some version of “it helps the staff.” It drafts the note. It flags the deteriorating patient. It suggests the next step. All useful. But notice what every one of those verbs has in common. The AI assists, then hands the work ...;

Much of the conversation around artificial intelligence in healthcare focuses on breakthrough diagnostics and drug discovery. Stefano Rosa believes some of the most meaningful near-term gains may come from a less visible area: reducing the administrative delays, denials, billing problems, and fragmented processes that prevent patients from receiving timely care. Rosa’s perspective is shaped by ...;

Artificial intelligence is becoming an increasingly valuable tool across healthcare, with half of U.S. organizations implementing generative AI, helping providers process information more efficiently, identify patterns, and support clinical decision-making. Dentistry is no exception. One area where AI-assisted technology is making a meaningful impact is occlusion, or the way a patient’s teeth come together when ...;

Purpose;To develop an approach for evaluating and reporting the quality of synthetic medical data (SMDs).Method;As SMDs become increasingly common, their responsible utilization benefits from a detailed characterization of their quality and value within the intended usage context. Here, we introduce an approach to assess SMDs across 7 dimensions: Congruence, Coverage, Constraint, Consistency, Comprehension, Compliance, and Completeness. We also describe an approach for reporting the quality of synthetic data to stakeholders.Results;We applied the proposed approach to 7 digital mammography datasets. The analysis underscored the strengths and limitations of each dataset across these dimensions. For example, while datasets generated by generative AI methods show high Congruence, they often fall short in Constraint and Completeness compared to those generated by knowledge-based methods. Additionally, our findings highlighted that the quality of generated data varies across different subgroups (eg, breast density) with certain subgroups (ie, dense and hetero) showing lower quality. This suggests that subgroup-specific fine-tuning of the generative process may have an effect on downstream tasks.Conclusions;The proposed approach provides stakeholders with a tool for assessing data quality, which may be used to better understand and analyse synthetic datasets..Advances in knowledge;This study introduces an approach for assessing SMD quality. The approach offers a practical mechanism for reporting SMD quality, which can enhance the reliability and usefulness of synthetic datasets.;

Objectives;The interpretation of contrast-enhanced mammography (CEM) images heavily depends on radiologists’ expertise, highlighting the need for automated tools to assist clinical decision-making. This pilot study aimed to develop a deep learning model using CEM images to predict the histological diagnosis of breast cancer.Methods;We retrospectively analyzed patients who underwent CEM followed by histopathological assessment (October 2022-May 2023) across 2 centers. CEM images from center 1 were used for model development, including training and 10-fold cross-validation, while images from center 2 served as an independent external test set. The breast region was manually segmented, and 2 deep learning architectures were implemented as ensemble classifiers, using biopsy results as reference standard. Performance metrics included accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the Receiver Operating Characteristic Curve (ROC) curve (AUC).Results;A total of 106 CEM images were retrospectively analyzed (50 from center 1 and 56 from center 2), obtained using different mammography systems. Histopathology identified 51 lesions (48%) as malignant and 55 (52%) as benign. Cross-validation yielded an ROC-AUC of 75% [58.4-91.6], accuracy of 68.7% [58.3-79], sensitivity of 68.9% [52.2-85.6], specificity of 67.8% [65.4-70.2], PPV of 69.3% [55.5-83.1], and NPV of 73.5% [55.2-91.8] (<span style="font-style:italic;">P; < .05). External testing on images from center 2 (27 malignant, 29 benign) achieved an accuracy of 64.3%.Conclusions;The study showed promising performance but requires further development and dataset expansion. The model has the potential to be integrated into clinical practice.Advances in knowledge;A deep learning model based on CEM provides a tool to support physicians in the decision-making process and reduce invasiveness in breast cancer management.;

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.;

<span class="paragraphSection">Abstract;Chest X-rays (CXRs) are the most commonly performed imaging investigation. In the UK, many centers experience reporting delays due to radiologist workforce shortages. Artificial intelligence (AI) tools capable of distinguishing “normal” from “abnormal” CXRs have emerged as a potential solution. If “normal” CXRs could be safely identified and reported without human input, a substantial portion of radiology workload could be reduced.This article examines the feasibility and implications of autonomous AI reporting of “normal” CXRs, using the United Kingdom as an example setting. Key issues include defining “normal,” ensuring generalizability across populations, and managing the sensitivity-specificity trade-off. It also addresses legal and regulatory challenges, such as compliance with IR(ME)R and GDPR, and the lack of accountability frameworks for errors. Further considerations include the impact on radiologists practice, the need for robust post-market surveillance, and incorporation of patient perspectives. While the benefits are clear, adoption must be cautious, with strong governance, legal clarity, and rigorous clinical validation to ensure safe and sustainable use.;

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.;