BackgroundSocial media platforms facilitate global discourse on the application of artificial intelligence (AI) in healthcare. Nevertheless, there is a paucity of longitudinal analyses of digitally mediated discussions.ObjectiveTo investigate the evolution of global English-language-dominated discourse on #AIinHealthcare over a three-year period on X (formerly Twitter).MethodsUsing Fedica analytics, we analysed 57,880 tweets by 17,991 distinct users across […]
Multimodal AI-based systems in major depressive disorder: a review of clinical and translational applications
Major Depressive Disorder (MDD) is one of the most prevalent and disabling psychiatric conditions worldwide, involving alterations in mood regulation, cognitive function, sleep, and physiological systems. Traditional diagnostic approaches often rely on time-consuming interviews and questionnaires, which are largely based on subjective clinical judgment, and may contribute to misdiagnosis or suboptimal treatment selection. Artificial Intelligence […]
Engagement, motivation, or sustained attention? Rethinking the effects of technology in autism
Technology-based interventions for Autism Spectrum Disorder (ASD) are frequently justified on the grounds that digital tools “increase engagement” and “enhance motivation.” However, across domains such as robot-assisted therapy, immersive environments (virtual and augmented reality), and ICT-based educational applications, outcomes labeled as engagement are often derived from observable indicators including gaze, time-on-task, interaction duration, task adherence, […]
Exploration of wearable sensor measures associated with panic attacks differs across mental health conditions
Panic attacks (PAs) are acute anxiety episodes that are pervasive, with one in 10 individuals having experienced a PA in the past year. PAs impair daily functioning and are associated with an increase in emergency room visits and suicide attempts. Despite their impact, the unpredictable nature of PAs makes them challenging to manage. PAs are […]
Promotion and preservation of mobility and autonomy in old age through smart rollators—a qualitative study
BackgroundDiseases and health limitations associated with ageing often result in loss of mobility and reduced social participation. The ongoing demographic shift towards an increasingly ageing population, combined with a declining number of healthcare professionals, highlights the need to integrate digital assistive solutions to reduce workload and healthcare costs. Smart rollators (SRs) equipped with sensor-based assistance […]
Beyond the algorithm: embedding ethics for trustworthy AI in radiology and oncology
BackgroundArtificial intelligence (AI) in radiology and oncology promises improvements in diagnostic accuracy and efficiency yet introduces complex ethical and societal challenges. Governance efforts frequently rely on high-level principles such as trustworthiness and fairness, which risk becoming ineffective when not grounded in specific contexts. This study presents findings from our work on ethical and societal aspects […]
The association of transformer-based sentiment analysis with symptom distress and deterioration in routine psychotherapy care
Sentiment analysis has been of long-standing interest in psychotherapy research. Recently, the Transformer deep learning architecture has produced text-based sentiment analysis models that are highly accurate and context-aware. These models have been explored as proxies for emotion measurement instruments in psychotherapy, but not investigated as stand-alone psychometric tools. Using proposed utterance-level and session-level sentiment features […]
Measuring and reducing surgical staff stress in a realistic operating room setting using EDA monitoring and smart hearing protection
BackgroundStress is a critical factor in the operating room (OR) and affects both the performance and well-being of surgical staff. Measuring and mitigating this stress can therefore improve patient safety and healthcare worker health.ObjectiveThis study aimed to evaluate the stress levels of OR staff in a simulated surgical setting using electrodermal activity (EDA) and to […]
Harmonizing Multi-Objective LLM Unlearning via Unified Domain Representation and Bidirectional Logit Distillation
arXiv:2604.15482v1 Announce Type: cross Abstract: Large Language Models (LLMs) unlearning is crucial for removing hazardous or privacy-leaking information from the model. Practical LLM unlearning demands satisfying multiple challenging objectives simultaneously: removing undesirable knowledge, preserving general utility, avoiding over-refusal of neighboring concepts, and, crucially, ensuring robustness against adversarial probing attacks. However, existing unlearning methods primarily focus […]
From Benchmarking to Reasoning: A Dual-Aspect, Large-Scale Evaluation of LLMs on Vietnamese Legal Text
arXiv:2604.16270v1 Announce Type: cross Abstract: The complexity of Vietnam’s legal texts presents a significant barrier to public access to justice. While Large Language Models offer a promising solution for legal text simplification, evaluating their true capabilities requires a multifaceted approach that goes beyond surface-level metrics. This paper introduces a comprehensive dual-aspect evaluation framework to address […]
Seeing the imagined: a latent functional alignment in visual imagery decoding from fMRI data
arXiv:2604.15374v1 Announce Type: new Abstract: Recent progress in visual brain decoding from fMRI has been enabled by large-scale datasets such as the Natural Scenes Dataset (NSD) and powerful diffusion-based generative models. While current pipelines are primarily optimized for perception, their performance under mental-imagery remains less well understood. In this work, we study how a state-of-the-art […]
Neuro-Symbolic ODE Discovery with Latent Grammar Flow
arXiv:2604.16232v1 Announce Type: cross Abstract: Understanding natural and engineered systems often relies on symbolic formulations, such as differential equations, which provide interpretability and transferability beyond black-box models. We introduce Latent Grammar Flow (LGF), a neuro-symbolic generative framework for discovering ordinary differential equations from data. LGF embeds equations as grammar-based representations into a discrete latent space […]