Artificial Intelligence in Medicine (Canada)

Em-dash and function-word shift in Polish parliamentary interpellations (2015–2025) — frozen analysis package

Frozen analysis package for a preregistered study of the em-dash (U+2014) as a putative marker of LLM-assisted writing in 60,275 Polish parliamentary interpellations, 2015–2025. The registered decision rule was not met: the decisive cell gave a paired conditional p = 0.00214 but...

Hybrid heuristic ant colony optimization algorithm for mobile robot path planning

Purpose Mobile robot path planning is a fundamental task in autonomous navigation. Conventional ant colony optimization (ACO) and some of its variants may exhibit weak search guidance, slow convergence, and redundant initial paths in complex grid environments. This study proposes...

A Non-Invasive Transtympanic Bioadhesive Hydrogel for Sustained Delivery of Dexamethasone in Otitis Media Effusion Therapy

Abstract Otitis media with effusion (OME) is a highly prevalent condition characterized by persistent inflammatory effusion in the middle ear cavity. The clinical management of OME remains challenging due to the anatomical inaccessibility of the middle ear, which limits drug effi...

Optimizing information retrieval tasks with large language model for data enhancement

Abstract The data science and artificial intelligence, optimizing information retrieval tasks has become crucial for extracting actionable insights from vast amounts of data. The problem is the need for precise query formulation to retrieve relevant data effectively, as LLMs can...

WPSeg-Net: A Boundary-Guided Dual-Branch Network for Molten Pool Segmentation

The geometric morphology and dynamic evolution of the molten pool are key indicators of heat input, metal transfer, and solidification behavior during welding, making them critical for quality monitoring and process control. To address the challenges of molten pool image segmenta...

An interpretable frequency-domain matrix rank transformation and time-domain spatial graph attention enhancement network for rolling bearing fault diagnosis under strong noise environment

Extracting weak fault features obscured by environmental noise is a critical challenge. However, existing fault feature enhancement methods ignore the low-rank distribution of feature correlation structures, making it difficult to effectively extract critical fault information. T...

Vectorized SVE2 Optimization of the Post-Quantum Signature ML-DSA on ARMv9-A Architecture

Post-quantum cryptography (PQC) is essential to securing data in the quantum computing era, and standardization efforts led by NIST have driven extensive research on practical and efficient implementations. With the emerging deployment of ARMv9-A processors in mobile and edge dev...

Efficient Techniques for Low-Rank Tensor Approximation and Applications in Robust Object Detection

This paper introduces efficient randomized fixed-precision and single-pass algorithms for low-tubal-rank approximation of third-order tensors. The proposed fixed-precision algorithms are faster and more efficient than the existing algorithms for approximating the truncated tensor...

Machine learning for early prediction of preterm birth

Abstract Background Preterm birth (PTB), defined as delivery before 37 completed weeks of gestation, remains a major cause of neonatal mortality and long-term morbidity worldwide. Conventional risk assessment strategies, including cervical length measurement and biomarker-based s...

Pan-retinal pathology detection in oct scans integrating natural language synthesis with diagnostic annotation

Abstract While OCT is pivotal for macular disease diagnosis, its adoption in primary care is limited by AI systems that cannot simultaneously analyze multi-sectional scans across the full spectrum of maculopathies or generate diagnostically integrated reports. Here we present iOC...

A latency-aware multispectral image processing framework for real-time band compression and spectral-spatial optimization

Real-time multispectral image processing requires a design criterion that is different from offline spectral modeling: the selected bands, features, and classifier must be evaluated by both recognition performance and end-to-end runtime. This paper presents a latency-aware framew...

Real-world use of large language models for mental health in 2024

The extent to which people use general-purpose large language models (LLMs) for their mental health is unknown. Information about use patterns is important for clinicians, developers, and regulators. We surveyed U.S. adults ( n = 1871) between August and October 2024 using strati...