Head Trauma

Diagnostic Accuracy of Large Language Models for Rare Diseases: A Systematic Review and Meta-Analysis

Background: Large language models (LLMs) have been evaluated as tools to assist rare disease diagnosis, yet evidence on their accuracy remains fragmented. We conducted a systematic review and meta-analysis to synthesize the available evidence on the diagnostic performance of LLMs, identify sources of heterogeneity, and evaluate the current evidence base for clinical translation. Methods: We search...

Agentic Automation of BT-RADS Scoring: End-to-End Multi-Agent System for Standardized Brain Tumor Follow-up Assessment

The Brain Tumor Reporting and Data System (BT-RADS) standardizes post-treatment MRI response assessment in patients with diffuse gliomas but requires complex integration of imaging trends, medication effects, and radiation timing. This study evaluates an end-to-end multi-agent large language model (LLM) and convolutional neural network (CNN) system for automated BT-RADS classification. A multi-age...

Automatic Configuration of LLM Post-Training Pipelines

LLM post-training pipelines that combine supervised fine-tuning and reinforcement learning are difficult to configure under realistic compute budgets:...

Learning Transferable Temporal Primitives for Video Reasoning via Synthetic Videos

The transition from image to video understanding requires vision-language models (VLMs) to shift from recognizing static patterns to reasoning over te...

InViC: Intent-aware Visual Cues for Medical Visual Question Answering

Medical visual question answering (Med-VQA) aims to answer clinically relevant questions grounded in medical images. However, existing multimodal larg...

Informative Perturbation Selection for Uncertainty-Aware Post-hoc Explanations

Trust and ethical concerns due to the widespread deployment of opaque machine learning (ML) models motivating the need for reliable model explanations...

Informative Perturbation Selection for Uncertainty-Aware Post-hoc Explanations

Trust and ethical concerns due to the widespread deployment of opaque machine learning (ML) models motivating the need for reliable model explanations...

Rethinking Machine Unlearning: Models Designed to Forget via Key Deletion

Machine unlearning is rapidly becoming a practical requirement, driven by privacy regulations, data errors, and the need to remove harmful or corrupte...

Rectified flow-based prediction of post-treatment brain MRI from pre-radiotherapy priors for patients with glioma

Purpose/Objective: Brain tumors result in 20 years of lost life on average. Standard therapies induce complex structural changes in the brain that are...

Comparative Evaluation of Traditional Methods and Deep Learning for Brain Glioma Imaging. Review Paper

Segmentation is crucial for brain gliomas as it delineates the glioma s extent and location, aiding in precise treatment planning and monitoring, thus...

AI-Generated Responses to Patient's Messages: Effectiveness, Feasibility and Implementation

Background Generative artificial intelligence (GenAI) in healthcare may reduce administrative burden and enhance quality of care. Large language model...

VascFlexMap: Microvascular Ultrasound Imaging at Low Frame Rates Using Sparse Data and a Transformer-Decoder Network

Objective: Superresolution ultrasound (SR US) reveals microvascular structures with exquisite resolution, but clinical translation remains limited by ...

Fed-ADE: Adaptive Learning Rate for Federated Post-adaptation under Distribution Shift

Federated learning (FL) in post-deployment settings must adapt to non-stationary data streams across heterogeneous clients without access to ground-tr...

Neural Image Space Tessellation

We present Neural Image-Space Tessellation (NIST), a lightweight screen-space post-processing approach that produces the visual effect of tessellated ...

Space Syntax-guided Post-training for Residential Floor Plan Generation

Pre-trained generative models for residential floor plans are typically optimized to fit large-scale data distributions, which can under-emphasize cri...

How to gain valuable insight from scarce data with Machine Learning: a post-hoc explanation tool to identify biases in biological images classification

Machine learning (ML) models are effective at classifying images across various fields, including biology. However, their performance on biomedical im...

Transforming Behavioral Neuroscience Discovery with In-Context Learning and AI-Enhanced Tensor Methods

Scientific discovery pipelines typically involve complex, rigid, and time-consuming processes, from data preparation to analyzing and interpreting fin...

Life-course comorbidity patterns and integrated prediction of postpartum depression, multimorbidity, and symptom progression

Perinatal depression (PD) is common and disabling, yet its longitudinal comorbidity patterns and predictability remain poorly understood. This study l...

Systems Biology and Machine Learning Decode an Immunometabolic Signature for Post-Thrombotic Syndrome

Objective: Post-thrombotic syndrome (PTS), a common complication of deep vein thrombosis, lacks objective diagnostic biomarkers and its molecular mech...

A Mixed Reality System for Robust Manikin Localization in Childbirth Training

Opportunities for medical students to gain practical experience in vaginal births are increasingly constrained by shortened clinical rotations, patien...