IntroductionRobotics and technological interventions are increasingly being explored as solutions to improve rehabilitation outcomes but their implementation in clinical practice remains very limited. Understanding patient needs is crucial for effective integration of these technologies, ensuring they align with and address the actual requirements of individuals in clinical settings. The primary aim of this study is […]
Evaluating privacy leakages in LLM-driven ambient clinical documentation
IntroductionAutomated documentation tools are being rapidly adopted in healthcare and clinical workflows. Among these are AI-enabled ambient scribing products, which transcribe conversations between patients and healthcare providers, then produce clinical records using automatic speech recognition (ASR) and generative AI such as Large Language Models (LLMs). While research suggests these technologies can reduce clinical burden, safe […]
Assessing ChatGPT vs. evidence-based online responses for polycystic ovary syndrome self-management and education: an international cross-sectional blinded survey of healthcare professionals
Artificial intelligence (AI)-powered large language models, such as ChatGPT, are increasingly used by the public for health information. The reliability of such novel AI-tools in providing credible polycystic ovary syndrome (PCOS) information/advice requires investigation. Healthcare professionals involved in PCOS care (n = 43 from 14 countries) used a 5-point Likert scale to evaluate ChatGPT-generated responses to frequently […]
Evaluating the quality of online patient education materials for gastric adenocarcinoma
BackgroundGastric adenocarcinoma, or gastric cancer, typically has a poor prognosis. The objective of this study was to assess the quality, understandability, actionability, and comprehensiveness of online resources for patients diagnosed with gastric adenocarcinoma, or gastric cancer as patients increasingly rely on online health information.MethodsA systematic search using the term “stomach cancer” was conducted across three […]
Novartis pumps up community health footprint to tackle heart disease and cancer
Novartis is launching a three-front push to close healthcare gaps in heart disease and cancer, expanding initiatives with the goal of roughly tripling their footprint by 2030.
TR-EduVSum: A Turkish-Focused Dataset and Consensus Framework for Educational Video Summarization
arXiv:2604.07553v1 Announce Type: cross Abstract: This study presents a framework for generating the gold-standard summary fully automatically and reproducibly based on multiple human summaries of Turkish educational videos. Within the scope of the study, a new dataset called TR-EduVSum was created, encompassing 82 Turkish course videos in the field of “Data Structures and Algorithms” and […]
Google, AI Literacy, and the Learning Sciences: Multiple Modes of Research, Industry, and Practice Partnerships
arXiv:2604.07601v1 Announce Type: cross Abstract: Enabling AI literacy in the general population at scale is a complex challenge requiring multiple stakeholders and institutions collaborating together. Industry and technology companies are important actors with respect to AI, and as a field, we have the opportunity to consider how researchers and companies might be partners toward shared […]
GIRL: Generative Imagination Reinforcement Learning via Information-Theoretic Hallucination Control
arXiv:2604.07426v1 Announce Type: cross Abstract: Model-based reinforcement learning (MBRL) improves sample efficiency by optimizing policies inside imagined rollouts, but long-horizon planning degrades when model errors compound and imagined trajectories drift off the training manifold. We introduce GIRL (Generative Imagination Reinforcement Learning), a latent world-model framework that addresses this failure mode with two key components. First, […]
Cluster Attention for Graph Machine Learning
arXiv:2604.07492v1 Announce Type: cross Abstract: Message Passing Neural Networks have recently become the most popular approach to graph machine learning tasks; however, their receptive field is limited by the number of message passing layers. To increase the receptive field, Graph Transformers with global attention have been proposed; however, global attention does not take into account […]
TSUBASA: Improving Long-Horizon Personalization via Evolving Memory and Self-Learning with Context Distillation
arXiv:2604.07894v1 Announce Type: cross Abstract: Personalized large language models (PLLMs) have garnered significant attention for their ability to align outputs with individual’s needs and preferences. However, they still struggle with long-horizon tasks, such as tracking a user’s extensive history of conversations or activities. Existing memory mechanisms often fail to capture evolving behaviors, and RAG paradigms […]
The Weaponization of Computer Vision: Tracing Military-Surveillance Ties through Conference Sponsorship
arXiv:2604.07803v1 Announce Type: cross Abstract: Computer vision, a core domain of artificial intelligence (AI), is the field that enables the computational analysis, understanding, and generation of visual data. Despite being historically rooted in military funding and increasingly deployed in warfare, the field tends to position itself as a neutral, purely technical endeavor, failing to engage […]
Optimal Decay Spectra for Linear Recurrences
arXiv:2604.07658v1 Announce Type: cross Abstract: Linear recurrent models offer linear-time sequence processing but often suffer from suboptimal long-range memory. We trace this to the decay spectrum: for $N$ channels, random initialization collapses the minimum spectral gap to $O(N^-2)$, yielding sub-exponential error $exp(-Omega(N/log N))$; linear spacing avoids collapse but degrades to $exp(-O(N/sqrtT))$, practically algebraic over long […]