Latest AI and machine learning research in risk management for healthcare professionals.
Accelerated magnetic resonance imaging reduces acquisition time, but reconstruction from undersampled k-space can blur diagnostically relevant structures or introduce failures that are not captured by global image metrics. We propose SA-RDM-DC, a Self-Auditing Residual generative Drifting Model with Data Consistency for accelerated knee MRI. The method adapts the newly proposed generative drifting...
Objectives: To evaluate the diagnostic accuracy of a publicly available DenseNet-121 convolutional neural network (TorchXRayVision) for triaging chest radiographs of health assessment applicants at a tertiary hospital in Nepal. Design: Prospective, single-centre, shadow-mode diagnostic accuracy validation study. Reported in accordance with the STARD 2015 checklist and STARD-AI/DECIDE-AI guidelines...
Language models increasingly write probabilistic programs (in NumPyro, Stan, or Pyro), but a program that compiles, runs, and passes every unit test c...
Electrocardiographic (ECG) interval measurements underpin clinical decision-making and large-scale cardiovascular research, yet existing automated met...
While Text-to-Image (T2I) models have shown remarkable success in generating photorealistic visual content, they still struggle with the rigorous sema...
Longitudinal glioblastoma response assessment requires comparing subtle tumor changes across MRI time points using structured clinical criteria such a...
Graph neural networks have moved from a niche representation-learning technique to the default model class wherever data carry relational structure. T...
Reasoning in multimodal large language models (MLLMs) has shown strong promise in medical imaging. However, this reasoning is usually free-form text j...
Introduction Stillbirth prevention requires reliable detection of potential causes for timely interventions. Currently, there is no effective screenin...
A robot working alongside people must reason about what they have done, in what order, and with what intent. Video carries the spatial layouts, object...
Automated homework assessment depends not only on recognizing student answers, but also on accurately locating where each answer and each intermediate...
Quantitative maps from dynamic contrast-enhanced MRI (DCE-MRI) are essential for tumor assessment but are often unavailable due to contrast-agent risk...
Automated classification of acute lymphoblastic leukemia (ALL) from peripheral blood smear images has often reported near-perfect performance on the C...
Single-cell foundation models are increasingly positioned as virtual cells, yet their capabilities are assessed by fragmented, largely single-task ben...
Artificial intelligence (AI) has achieved remarkable success in medical imaging, but it is widely recognized that these models often perform inconsist...
Background: Guiding risk-appropriate inpatient thromboprophylaxis requires venous thromboembolism (VTE) risk stratification; however, reliable risk de...
Background: Children and young people (CYP) are particularly affected by mental health problems. Mobile apps provide a scalable and accessible approac...
Conformal risk control (CRC) provides distribution-free guarantees on segmentation quality by calibrating a prediction-set threshold on held-out data....
A companion study established a de-biased, cross-model VLM-as-3D-judge that reliably ranks single-image-to-3D mesh quality where cheap geometry and CL...
The Frechet Inception Distance (FID) is the de facto arbiter of image generation, yet most papers report just a single number from a single trained mo...