Surveys

The Relatives Experience Questionnaire for Acute Inpatient Child and Adolescence Mental Health Services (REQ-AICAMHS): reliability and validity following a Norwegian survey

Introduction: Adolescents with mental health disorders represent a vulnerable group with complex care needs, yet their and their relatives experiences in acute inpatient mental health services remain poorly understood. While patient-reported experience measures (PREMs) are increasingly recognized as essential for improving healthcare quality, validated instruments for child and adolescent mental h...

Structural bias in machine learning-guided peptide design

Machine learning continues to accelerate peptide and protein design through the rapid prediction and generation of sequences with desired characteristics. Many applications focus on predicting properties, functions, and structures, as well as generating point mutations and de novo designs. Nevertheless, many models prove less generalizable than initially claimed. Most predictors and generators are...

Open-Rosalind: Tool-First Biomedical LLM Agents with Process-Aware Benchmarking

Large language models are increasingly used as scientific agents, yet the flexibility that benefits general-purpose agents can conflict with the accou...

Towards Fairness under Label Bias in Image Segmentation: Impact, Measurement and Mitigation

Labeled datasets reflect the biases of their annotation pipelines, which sometimes introduce label bias: group-conditional label errors that cause sys...

The Cost of Context: Mitigating Textual Bias in Multimodal Retrieval-Augmented Generation

While Multimodal Large Language Models (MLLMs) are increasingly integrated with Retrieval-Augmented Generation (RAG) to mitigate hallucinations, the i...

Correcting heterogeneous diagnostic bias when developing clinical prediction models using causal hidden Markov models

In routine care, individuals identified a priori as high-risk are usually tested for conditions more frequently. Protected attributes, such as sex or ...

BAMI: Training-Free Bias Mitigation in GUI Grounding

GUI grounding is a critical capability for enabling GUI agents to execute tasks such as clicking and dragging. However, in complex scenarios like the ...

Identifying the determinants of health protective behaviors during the COVID-19 pandemic using machine learning: an analysis of six countries

Individuals adapt their behavior in response to infectious disease epidemics. Understanding the determinants of behavior, particularly the impact of i...

CropVLM: A Domain-Adapted Vision-Language Model for Open-Set Crop Analysis

High-throughput plant phenotyping, the quantitative measurement of observable plant traits, is critical for modern breeding but remains constrained by...

Full-Field Stimulus Test for Visual Function Assessment in Ultra-Low Vision with Retinitis Pigmentosa

Purpose: Assessing visual function in patients with ultra-low vision (ULV), particularly those with retinitis pigmentosa (RP), remains a significant c...

MIFair: A Mutual-Information Framework for Intersectionality and Multiclass Fairness

Fairness in machine learning remains challenging due to its ethical complexity, the absence of a universal definition, and the need for context-specif...

Cross-Subject Generalization for EEG Decoding: A Survey of Deep Learning Methods

Deep learning for cross-subject EEG decoding is hindered by high inter-subject variability, which introduces a severe domain shift between training an...

A Sequential Multiple Assignment Randomized Trial Design with Response-Adaptive Tailoring Function

We present a novel sequential multiple assignment randomized trial (SMART) design that integrates response-adaptive randomization with tailoring funct...

Are Data Augmentation and Segmentation Always Necessary? Insights from COVID-19 X-Rays and a Methodology Thereof

Purpose: Rapid and reliable diagnostic tools are crucial for managing respiratory diseases like COVID-19, where chest X-ray analysis coupled with arti...

Correcting Performance Estimation Bias in Imbalanced Classification with Minority Subconcepts

Class-level evaluation can conceal substantial performance disparities across subconcepts within the same class, causing models that perform well on a...

Validation of an AI-Assisted Framework for Systematic Bias Assessment in Observational Studies

Background: The rapid expansion of medical literature has led to substantial variability and frequent contradictions in study findings, making it incr...

Feature-Based Parametric Response Mapping on Thoracic Computed Tomography for Robust Disease Classification in COPD

Purpose: To develop an interpretable feature-based Deep Parametric Response Mapping (PRMD) method that combines wavelet scattering convolution network...

Toward trustworthy clinical AI for obsessive-compulsive disorder: reliability, generalizability, and interpretability of a transformer model across the ENIGMA-OCD consortium

Background. Studies applying machine learning to obsessive-compulsive disorder (OCD) typically report accuracy in homogeneous samples but rarely asses...

COMO: Closed-Loop Optical Molecule Recognition with Minimum Risk Training

Optical chemical structure recognition (OCSR) translates molecular images into machine-readable representations like SMILES strings or molecular graph...

FAIR_XAI: Improving Multimodal Foundation Model Fairness via Explainability for Wellbeing Assessment

In recent years, the integration of multimodal machine learning in wellbeing assessment has offered transformative potential for monitoring mental hea...