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Latest AI and machine learning research in surveys for healthcare professionals.

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The mutual exclusivity bias of bilingual visually grounded speech models

Mutual exclusivity (ME) is a strategy where a novel word is associated with a novel object rather than a familiar one, facilitating language learning in children. Recent work has found an ME bias in a visually grounded speech (VGS) model trained on English speech with paired images. But ME has also been studied in bilingual children, who may employ it less due to cross-lingual ambiguity. We expl...

Average Calibration Losses for Reliable Uncertainty in Medical Image Segmentation

Deep neural networks for medical image segmentation are often overconfident, compromising both reliability and clinical utility. In this work, we propose differentiable formulations of marginal L1 Average Calibration Error (mL1-ACE) as an auxiliary loss that can be computed on a per-image basis. We compare both hard- and soft-binning approaches to directly improve pixel-wise calibration. Our exp...

Trustworthy Medical Question Answering: An Evaluation-Centric Survey

Trustworthiness in healthcare question-answering (QA) systems is important for ensuring patient safety, clinical effectiveness, and user confidence....

Attention-based transformer models for image captioning across languages: An in-depth survey and evaluation

Image captioning involves generating textual descriptions from input images, bridging the gap between computer vision and natural language processin...

The effects of using created synthetic images in computer vision training

This paper investigates how rendering engines, like Unreal Engine 4 (UE), can be used to create synthetic images to supplement datasets for deep com...

INESC-ID @ eRisk 2025: Exploring Fine-Tuned, Similarity-Based, and Prompt-Based Approaches to Depression Symptom Identification

In this work, we describe our team's approach to eRisk's 2025 Task 1: Search for Symptoms of Depression. Given a set of sentences and the Beck's Dep...

Large Language Models for EEG: A Comprehensive Survey and Taxonomy

The growing convergence between Large Language Models (LLMs) and electroencephalography (EEG) research is enabling new directions in neural decoding...

AI Data Development: A Scorecard for the System Card Framework

Artificial intelligence has transformed numerous industries, from healthcare to finance, enhancing decision-making through automated systems. Howeve...

DeepSeek in Healthcare: A Survey of Capabilities, Risks, and Clinical Applications of Open-Source Large Language Models

DeepSeek-R1 is a cutting-edge open-source large language model (LLM) developed by DeepSeek, showcasing advanced reasoning capabilities through a hyb...

AceVFI: A Comprehensive Survey of Advances in Video Frame Interpolation

Video Frame Interpolation (VFI) is a fundamental Low-Level Vision (LLV) task that synthesizes intermediate frames between existing ones while mainta...

Data Heterogeneity Modeling for Trustworthy Machine Learning

Data heterogeneity plays a pivotal role in determining the performance of machine learning (ML) systems. Traditional algorithms, which are typically...

Breaking Latent Prior Bias in Detectors for Generalizable AIGC Image Detection

Current AIGC detectors often achieve near-perfect accuracy on images produced by the same generator used for training but struggle to generalize to ...

UrduSER: A comprehensive dataset for speech emotion recognition in Urdu language.

Speech Emotion Recognition (SER) is a rapidly evolving field of research that aims to identify and categorize emotional states through speech signal a...

Jun 1 2025 40496743
Automatic implicit motive codings are at least as accurate as humans' and 99% faster.

Implicit motives, nonconscious needs that influence individuals' behaviors and shape their emotions, have been part of personality research for nearly...

Jun 1 2025 40208739
Concise multi-class anxiety disorder risk assessment: A novel advanced machine learning approach.

Rapidly assessing anxiety disorder risk is crucial for effective mental health screen and intervention. However, traditional survey tools such as DASS...

Jun 1 2025 40288106
Development and Validation of a Scale for Nurses' Ethical Awareness in The Use of Artificial Intelligence: A Methodological Study.

The integration of artificial intelligence in nursing practice presents significant ethical challenges that require a comprehensive assessment framewo...

Jun 1 2025 40414799
Assessing Clinician Consistency in Wound Tissue Classification and the Value of AI-Assisted Quantification: A Cross-Sectional Study.

This study investigated the relationship between clinician assessments and the AI-generated scores, highlighting how correlations vary based on clinic...

Jun 1 2025 40421826
Automatic cough detection via a multi-sensor smart garment using machine learning.

Coughing behavior is associated with conditions such as sleep apnea, asthma, and chronic obstructive pulmonary disorder and can severely affect qualit...

Jun 1 2025 40239229
Assessing bias in AI-driven psychiatric recommendations: A comparative cross-sectional study of chatbot-classified and CANMAT 2023 guideline for adjunctive therapy in difficult-to-treat depression.

The integration of chatbots into psychiatry introduces a novel approach to support clinical decision-making, but biases in their recommendations pose ...

Jun 1 2025 40267866
Sharing patient technology preferences with care networks: Stakeholders' views of the "Let's Talk Tech" decision aid for dementia care.

BackgroundLet's Talk Tech (LTT) is a self-administered web intervention for people with memory loss and their care partners that supports decision-mak...

Jun 1 2025 40313054
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