Latest AI and machine learning research in surveys for healthcare professionals.
Introduction: The use of artificial intelligence (AI) by clinicians has increased rapidly in recent years, with large language models (LLMs) emerging as tools that can equal clinician diagnostic performance in simulated settings. However, limited data exist regarding physicians use of LLMs in real-world clinical practice. This study aimed to evaluate the frequency of LLM use among practicing hospi...
Neuroimaging based pain decoding faces two underappreciated challenges: between subject variability that prevents classifiers from generalizing across patients, and within session cross validation designs that inflate reported accuracy by conflating within person and between person variance. Here we address both using portable functional near infrared spectroscopy (fNIRS) during pharmacologically ...
Machine learning frequently encounters challenges when applied to population-stratified datasets, where data representation bias and data distribution...
Background: Patients with CKD and polypharmacy face high rates of drug-related problems, yet comprehensive medication review remains time-intensive an...
Fixed larval specimens often shrink and curve, making length measurement labor-intensive. Although recent studies have demonstrated efficient fish-len...
Semi-supervised learning has become a dominant paradigm for reducing annotation costs. However, we argue that the current progress is clouded by a two...
Posttraumatic stress disorder (PTSD) is a prevalent and debilitating mental health condition with significant personal and societal impacts. Current c...
Background: With growing impetus to integrate artificial intelligence (AI) tools into radiology, clinical practices must navigate workflow redesign. T...
Introduction: Large-scale free-text data with socio-demographic information can capture nuanced accounts of lived experience that are difficult to det...
While multi-task learning based automatic radiology report generation (RRG) is widely adopted to ensure clinical consistency, most focus on architectu...
In view of the outstanding progress of machine learning (ML) and growing cost of health systems, it is a current challenge to incorporate artificial i...
Skin-tone labels are absent from public dermoscopy benchmarks such as the International Skin Imaging Collaboration (ISIC), making it impossible to aud...
Objectives To evaluate whether class-conditional conformal prediction (CP) can provide reliable uncertainty quantification (UQ) under severe class imb...
Cardiac rehabilitation is critical for secondary prevention, yet long-term adherence remains low. We present CUOREMA, a new personalized mobile health...
Data curation has shifted the quality-compute frontier for language-model and contrastive image-text pretraining, but its role for vision-language mod...
Rapid and accurate situational awareness is essential for effective response during natural disasters, where delays in analysis can significantly hind...
Implicit Neural Representations (INRs) model continuous signals using multilayer perceptrons (MLPs), enabling compact, differentiable, and high-fideli...
Self-verification, re-invoking the same vision language model (VLM) in a fresh context to check its own generated answer, is increasingly used as a de...
Objective: To describe the ophthalmic examination protocol within the German National Cohort (NAKO) / NAKO Gesundheitsstudie, to report the baseline p...
Individual treatment effects are not point-identified from data. The Probability of Necessity and Sufficiency (PNS) circumvents this limitation by cha...