Surveys

Confidence-aware Monocular Depth Estimation for Minimally Invasive Surgery

Purpose: Monocular depth estimation (MDE) is vital for scene understanding in minimally invasive surgery (MIS). However, endoscopic video sequences are often contaminated by smoke, specular reflections, blur, and occlusions, limiting the accuracy of MDE models. In addition, current MDE models do not output depth confidence, which could be a valuable tool for improving their clinical reliability. M...

Longitudinal Impact of NLP-Augmented Clinical Decision Support on Cognitive Decline Detection in German Geriatric Primary Care: A Dynamic Panel Data Analysis Using System GMM Estimation

Background: Cognitive decline and dementia represent major public health challenges in aging populations. Natural language processing (NLP)-augmented clinical decision support systems (CDSS) offer promising tools for early detection, yet causal evidence on their longitudinal impact at the health system level remains sparse. This study examines whether the phased adoption of NLP-augmented CDSS acro...

Bias in respiratory diagnoses by Large Language Models (LLMs) in Low Middle Income Countries (LMICs)

Introduction Clinicians and patients are likely to increasingly use Large Language Models (LLMs) for diagnostic support. Use of LLMs mostly created in...

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...

Interpretable Debiasing of Vision-Language Models for Social Fairness

The rapid advancement of Vision-Language models (VLMs) has raised growing concerns that their black-box reasoning processes could lead to unintended f...

Development of a Multi-Trait Polygenic Score for Intrinsic Capacity

Background: Intrinsic capacity (IC) is a key marker of healthy ageing, which captures an individuals physical and mental capacities, measured across f...

Developing And Internally Validating AI-Based Aging Resilience Biomarkers in Non-Human Primates

Quantifying biological aging is crucial for understanding functional decline before the onset of morbidity. While many accelerated aging and frailty m...

Explainability-Aware Evaluation of Transfer Learning Models for IoT DDoS Detection Under Resource Constraints

Distributed denial-of-service (DDoS) attacks threaten the availability of Internet of Things (IoT) infrastructures, particularly under resource-constr...

Virtual Biopsy for Intracranial Tumors Diagnosis on MRI

Deep intracranial tumors situated in eloquent brain regions controlling vital functions present critical diagnostic challenges. Clinical practice has ...

Evaluating the AI Potential as a Safety Net for Diagnosis: A Novel Benchmark of Large Language Models in Correcting Diagnostic Errors

Background: Diagnostic errors are a leading cause of preventable patient harm, often occurring during early clinical encounters where diagnostic uncer...

Detecting labeling bias using influence functions

Labeling bias arises during data collection due to resource limitations or unconscious bias, leading to unequal label error rates across subgroups or ...

LATA: Laplacian-Assisted Transductive Adaptation for Conformal Uncertainty in Medical VLMs

Medical vision-language models (VLMs) are strong zero-shot recognizers for medical imaging, but their reliability under domain shift hinges on calibra...

Anatolution, an online platform for consensus morphology

Introduction: Supervised statistical learning for cell-level segmentation and morphometry in optical microscopy is limited less by algorithmic capacit...

KG-Orchestra: An Open-Source Multi-Agent Framework for Evidence-Based Biomedical Knowledge Graphs Enrichment.

Biomedical Knowledge Graphs (BKGs) offer integrative representations of complex biology, yet their utility is compromised by the limitations of curren...

Systematic Evaluation of Transfer Learning Strategies for Clinical Chemotherapy Response Prediction

Accurately predicting chemotherapy response remains a major challenge in precision oncology. Although machine-learning models based on tumour omics da...

Latent Regularization in Generative Test Input Generation

This study investigates the impact of regularization of latent spaces through truncation on the quality of generated test inputs for deep learning cla...

The Stationarity Bias: Stratified Stress-Testing for Time-Series Imputation in Regulated Dynamical Systems

Time-series imputation benchmarks employ uniform random masking and shape-agnostic metrics (MSE, RMSE), implicitly weighting evaluation by regime prev...

Reproducible symptom subtypes of depression identified using unsupervised machine learning

Depression is a heterogeneous disorder, often diagnosed based on symptom co-occurrence. However, individuals may present with markedly different sympt...

Evaluating LLMs in Finance Requires Explicit Bias Consideration

Large Language Models (LLMs) are increasingly integrated into financial workflows, but evaluation practice has not kept up. Finance-specific biases ca...

Boards-style benchmarks overestimate prior-chat bias in large language models: a factorial evaluation study

Background: Large language models (LLMs) are increasingly piloted as chat interfaces for chart review and clinical decision support. Although leading ...