'Thank God they're still alive': Kaiser therapists claim its new screening system puts patients at higher risk by delaying their care Kaiser pushed back on striking workers' claims and AI fears, saying it delivers'timely, high-quality care to meet members' needs' I lana Marcucci-Morris is worried about the patients she treats and how long it took for them to arrive in her office. At Kaiser Permanente's psychiatry outpatient clinic in Oakland, California, she says she increasingly finds herself assessing people experiencing severe mental health issues whom she believes should have been sent to the emergency room weeks earlier. For those who do make it to their appointments, she thinks: "Thank God they're still alive." It wasn't always this way, according to Marcucci-Morris, a licensed clinical social worker. Licensed professionals used to almost always be the first point of contact for patients with behavioral health issues at Kaiser, she said. She has noticed a change since January 2024, after the healthcare giant introduced a new screening process for first-time patients.

Antibodies are crucial proteins produced by the immune system to eliminate harmful foreign substances and have become pivotal therapeutic agents for treating human diseases.

To accelerate therapeutic antibody discovery, computational methods, especially machine learning, have attracted considerable interest for predicting specific interactions between antibody candidates and target antigens such as viruses and bacteria.

A new report warns that the number of US investors in powerful commercial spyware rose sharply in 2024 and names new countries linked to the dangerous technology. The United States has emerged as the largest investor in commercial spyware --a global industry that has enabled the covert surveillance of journalists, human rights defenders, politicians, diplomats, and others, posing grave threats to human rights and national security . In 2024, 20 new US-based spyware investors were identified, bringing the total number of American backers of this technology to 31. This growth has largely outpaced other major investing countries such as Israel, Italy, and the United Kingdom, according to a new report published today by the Atlantic Council. The study surveyed 561 entities across 46 countries between 1992 and 2024, identifying 34 new investors.

Although the recent wave of AI models, known as Large Language Models (LLMs), are seamlessly surpassing the Turing Test, this milestone has been overshadowed by their rapid commercialization and the profound ways they are already reshaping society. The pursuit of Artificial General Intelligence (AGI)--commonly defined as human-level intelligence--is touted as the next major milestone. Yet whether the continued progress within the current framework could ever lead to agency and meaning at the scale of AI itself remains an open and contested question. Critics argue that current LLMs operate through algorithmic mimicry, that is simulating intelligent behavior without embodying the principles behind it (Jaeger, 2024; Jaeger et al., 2024) . Artificial Neural Networks--the main framework behind LLMs--operate on behaviorist assumptions: a framework that focuses exclusively on observable input-output patterns while treating internal states as part of a "black box" to be optimized (Brooks, 1991; Sutton & Barto, 2015) . This does not mean LLMs do not have sophisticated engineering, but their structure is designed to optimize internal states based on input-output feedback loops. Even though the logic behind behaviorism is likely one of the key principles supporting an intelligent system, it likely is not sufficient for intelligence and is not what enables agency and intelligence in the first place (Dreyfus, 1992; Searle, 1980) . Furthermore, it would be naive to consider outward behavior of intelligence as having acquired intelligence or sentience since a good simulation can be powerful and convincing. To address such issues, alternative approaches grounded in organismal intelligence are emerging to instead explain the principles behind intelligence through intrinsic and goal-directed models of the body and its relationship to the environment (Deacon, 2012; Jacob, 2023; Jaeger et al., 2024; Levin, 2019; Roli et al., 2022; Varela et al., 1993; Watson, 2024) .

Local governments around the world are making consequential decisions on behalf of their constituents, and these constituents are responding with requests, advice, and assessments of their officials at public meetings. So many small meetings cannot be covered by traditional newsrooms at scale. We propose PUBLICSPEAK, a probabilistic framework which can utilize meeting structure, domain knowledge, and linguistic information to discover public remarks in local government meetings. We then use our approach to inspect the issues raised by constituents in 7 cities across the United States. We evaluate our approach on a novel dataset of local government meetings and find that PUBLICSPEAK improves over state-of-the-art by 10% on average, and by up to 40%.

Accurate segmentation of anatomical structures in ultrasound (US) images, particularly small ones, is challenging due to noise and variability in imaging conditions (e.g., probe position, patient anatomy, tissue characteristics and pathology). To address this, we introduce Segment Anything Small (SAS), a simple yet effective scale- and texture-aware data augmentation technique designed to enhance the performance of deep learning models for segmenting small anatomical structures in ultrasound images. SAS employs a dual transformation strategy: (1) simulating diverse organ scales by resizing and embedding organ thumbnails into a black background, and (2) injecting noise into regions of interest to simulate varying tissue textures. These transformations generate realistic and diverse training data without introducing hallucinations or artifacts, improving the model's robustness to noise and variability. We fine-tuned a promptable foundation model on a controlled organ-specific medical imaging dataset and evaluated its performance on one internal and five external datasets. Experimental results demonstrate significant improvements in segmentation performance, with Dice score gains of up to 0.35 and an average improvement of 0.16 [95% CI 0.132,0.188]. Additionally, our iterative point prompts provide precise control and adaptive refinement, achieving performance comparable to bounding box prompts with just two points. SAS enhances model robustness and generalizability across diverse anatomical structures and imaging conditions, particularly for small structures, without compromising the accuracy of larger ones. By offering a computationally efficient solution that eliminates the need for extensive human labeling efforts, SAS emerges as a powerful tool for advancing medical image analysis, particularly in resource-constrained settings.

Accurate morphometric assessment of cartilage-such as thickness/volume-via MRI is essential for monitoring knee osteoarthritis. Segmenting cartilage remains challenging and dependent on extensive expert-annotated datasets, which are heavily subjected to inter-reader variability. Recent advancements in Visual Foundational Models (VFM), especially memory-based approaches, offer opportunities for improving generalizability and robustness. This study introduces a deep learning (DL) method for cartilage and meniscus segmentation from 3D MRIs using interactive, memory-based VFMs. To improve spatial awareness and convergence, we incorporated a Hybrid Shuffling Strategy (HSS) during training and applied a segmentation mask propagation technique to enhance annotation efficiency. We trained four AI models-a CNN-based 3D-VNet, two automatic transformer-based models (SaMRI2D and SaMRI3D), and a transformer-based promptable memory-based VFM (SAMRI-2)-on 3D knee MRIs from 270 patients using public and internal datasets and evaluated on 57 external cases, including multi-radiologist annotations and different data acquisitions. Model performance was assessed against reference standards using Dice Score (DSC) and Intersection over Union (IoU), with additional morphometric evaluations to further quantify segmentation accuracy. SAMRI-2 model, trained with HSS, outperformed all other models, achieving an average DSC improvement of 5 points, with a peak improvement of 12 points for tibial cartilage. It also demonstrated the lowest cartilage thickness errors, reducing discrepancies by up to threefold. Notably, SAMRI-2 maintained high performance with as few as three user clicks per volume, reducing annotation effort while ensuring anatomical precision. This memory-based VFM with spatial awareness offers a novel approach for reliable AI-assisted knee MRI segmentation, advancing DL in musculoskeletal imaging.