Latest AI and machine learning research in surveys for healthcare professionals.
Low-field to high-field MRI synthesis has emerged as a cost-effective strategy to enhance image quality under hardware and acquisition constraints, particularly in scenarios where access to high-field scanners is limited or impractical. Despite recent progress in diffusion models, diffusion-based approaches often struggle to balance fine-detail recovery and structural fidelity. In particular, the ...
PET/CT imaging is pivotal in oncology and nuclear medicine, yet summarizing complex findings into precise diagnostic impressions is labor-intensive. While LLMs have shown promise in medical text generation, their capability in the highly specialized domain of PET/CT remains underexplored. We introduce PET-F2I-41K (PET Findings-to-Impression Benchmark), a large-scale benchmark for PET/CT impression...
Elasmobranch populations are experiencing significant global declines, and several species are currently classified as threatened. Reliable monitoring...
Understanding how neural networks rely on visual cues offers a human-interpretable view of their internal decision processes. The cue-conflict benchma...
Background: Previous research has shown that radiomics-based machine learning models are promising precision medicine tools for lesion-level predictio...
Objective: In Parkinson's disease (PD), gait-related digital mobility outcomes (DMOs) show promise for monitoring mobility decline, but convergent val...
Objective: To evaluate the effectiveness of various Large Language Models (LLMs) in identifying reliable predictors of Electronic Nicotine Delivery Sy...
Reliable unmanned aerial vehicle (UAV) detection is critical for autonomous airspace monitoring but remains challenging when integrating sensor stream...
Training-free one-shot segmentation offers a scalable alternative to expert annotations where knowledge is often transferred from support images and f...
Ensuring fairness in image classification prevents models from perpetuating and amplifying bias. Concept bottleneck models (CBMs) map images to high-l...
Agentic retrieval-augmented reasoning pipelines are increasingly used to structure how large language models (LLMs) incorporate external evidence in c...
Accurate polyp segmentation from colonoscopy images is critical for colorectal cancer prevention, yet the generalization of deep learning models under...
Measuring the growth rate of filamentous fungi is an essential phenotype assay in fungal biology, enabling the comparison of nutrient-related fitness ...
We study how architectural inductive bias reshapes the implicit regularization induced by the edge-of-stability phenomenon in gradient descent. Prior ...
Popular explanation methods often produce unreliable feature importance scores due to missingness bias, a systematic distortion that arises when model...
Background: Artificial intelligence chatbots (AICs) are increasingly being integrated into scholarly publishing, with the potential to automate routin...
We developed and validated a self-administered clinical vignette platform powered by a large language model (LLM), deployed through a SurveyCTO web su...
Cell migration is a key biological process underlying wound healing, tissue development, and cancer metastasis, yet calibrating mathematical models of...
We study a systematic bias in modern image generation models: the mention order of entities in text spuriously determines spatial layout and entity--r...
When visual evidence is ambiguous, vision models must decide whether to interpret face-like patterns as meaningful. Face pareidolia, the perception of...