pubmed-ai.html PubMed AI 的长期订阅 RSS，汇总最近命中的论文和归档。 zh-CN Wed, 22 Apr 2026 03:37:20 +0000 ../papers/doi-8b199115e87e.html https://pubmed.ncbi.nlm.nih.gov/42013456/#2026-04-22#pubmed-ai Wed, 22 Apr 2026 11:37:03 +0800 BACKGROUND: The American Society of Anesthesiologists Physical Status (ASA-PS) classification is integral to preoperative risk assessment; yet, assignment remains subjective and labor-intensive. Recent large language models (LLMs) process free-text electronic health records (EHRs), but few studies have evaluated parameter-efficient adaptations that both predict ASA-PS and provide clinician-readable rationales. Low-rank adaptation (LoRA) is a parameter-efficient technique that updates only a sma… ../papers/doi-eefd4e77621d.html https://pubmed.ncbi.nlm.nih.gov/42012584/#2026-04-22#pubmed-ai Wed, 22 Apr 2026 11:37:03 +0800 BACKGROUND: Current machine learning (ML) prediction models offer limited guidance for individualized actionable management. Large language models (LLMs) can transform ML model-predicted risk estimates with Shapley Additive Explanations (SHAP) into clinically meaningful support information, yet the added value of incorporating ML-derived data and the relative performance of different LLMs remain uncertain. To address these gaps, we used our previously developed IMPACT framework to evaluate the… ../papers/doi-637d5e47b283.html https://pubmed.ncbi.nlm.nih.gov/42013455/#2026-04-22#pubmed-ai Wed, 22 Apr 2026 11:37:03 +0800 The rapid integration of large language models into electronic medical record systems introduces a critical theoretical vulnerability. Drawing on foundational computer science proofs of "model collapse," this viewpoint introduces the concept of "Clinical Model Autophagy"-a systemic degradation of diagnostic integrity that occurs when clinical artificial intelligence (AI) models are recursively trained on unverified, AI-generated synthetic data. As these recursive models may progressively regres… ../papers/doi-e00fc28ccec0.html https://pubmed.ncbi.nlm.nih.gov/42013267/#2026-04-22#pubmed-ai Wed, 22 Apr 2026 11:37:03 +0800 Approximately one-fifth of patients with acute pancreatitis (AP) develop severe forms, which are associated with high mortality rates, making early prediction of severity crucial for effective patient management. In this study, we present APSevLM (Acute Pancreatitis Severity Language Model), a large language model (LLM)-based approach that integrates admission-time clinical data, imaging reports, and expert knowledge to predict AP severity at an early stage. Through a comprehensive evaluation u… ../papers/doi-ec10f242cbed.html https://pubmed.ncbi.nlm.nih.gov/42012852/#2026-04-22#pubmed-ai Wed, 22 Apr 2026 11:37:03 +0800 OBJECTIVES: To compare the difference in the number of cardiopulmonary bypass surgical patients who receive sugammadex vs. placebo and who meet the Society of Thoracic Surgery early extubation quality benchmark. DESIGN: Single-center, randomized, double-blind, placebo-controlled trial. SETTING: Participants were enrolled at a single U.S. hospital between August 2023 and July 2025. PATIENTS: Seventy-four eligible cardiac surgery patients undergoing cardiopulmonary bypass with anticipated institu… ../papers/doi-a39ecce65f3a.html https://pubmed.ncbi.nlm.nih.gov/42004487/#2026-04-21#pubmed-ai Tue, 21 Apr 2026 11:40:46 +0800 BACKGROUND: Clinical trial enrollment in oncology remains critically low, with fewer than 5% of eligible adults participating, in large part due to the complexity and labor intensity of eligibility screening. We prospectively evaluated a neuro-symbolic, multi-agent artificial intelligence (AI) platform integrating domain-specific large language model (LLM) agents, an oncology-specific knowledge graph, a real-time recommendation engine, and human-in-the-loop review to determine whether automated… ../papers/doi-481edb543c43.html https://pubmed.ncbi.nlm.nih.gov/42004488/#2026-04-21#pubmed-ai Tue, 21 Apr 2026 11:40:46 +0800 BACKGROUND: The KEYNOTE-522 trial showed that neoadjuvant chemotherapy (NAC) plus adjuvant pembrolizumab improved overall survival, event-free survival (EFS), and pathological complete response (pCR) in high-risk early-stage triple-negative breast cancer. As treatments evolve, evaluating real-world (RW) effectiveness is key to understanding trial generalizability. This study benchmarked RW efficacy endpoints in early-stage triple-negative breast cancer patients treated with NAC. MATERIALS AND M… ../papers/doi-eebfc182eb48.html https://pubmed.ncbi.nlm.nih.gov/42004490/#2026-04-21#pubmed-ai Tue, 21 Apr 2026 11:40:46 +0800 BACKGROUND: Unstructured oncology consultation notes contain rich clinical information that may support survival prediction. Open-weight large language models (LLMs) can utilize these notes with zero-shot inference or fine-tuning, but their relative value for this setting remains unclear. The objective of this study is to evaluate open-weight LLMs for predicting 60-month survival from initial oncology consultation notes, comparing (i) zero-shot performance, (ii) performance after fine-tuning, a… ../papers/doi-db4a2a7daf35.html https://pubmed.ncbi.nlm.nih.gov/42003757/#2026-04-21#pubmed-ai Tue, 21 Apr 2026 11:40:46 +0800 BACKGROUND: Large language models (LLMs) are increasingly used to obtain health information, including guidance on child and adolescent mental health. In anorexia nervosa (AN), where early recognition and timely intervention are critical, the accuracy of AI-generated information available to parents may have important clinical implications. This study evaluated the performance of LLMs in responding to parent-oriented questions about AN. METHODS: A comparative model evaluation was conducted usin… ../papers/doi-878d50e507af.html https://pubmed.ncbi.nlm.nih.gov/42006279/#2026-04-21#pubmed-ai Tue, 21 Apr 2026 11:40:46 +0800 BACKGROUND: Lung cancer is a heterogeneous and complex disease requiring multidisciplinary input for optimal management planning, with guidelines recommending that all patients be discussed in a multidisciplinary setting. Multidisciplinary meeting (MDM) discussion aims to enhance evidence-based management, improve treatment access, and optimize complex management plans. METHODS: We aimed to assess the extent and impacts of MDM discussion in patients with lung cancer described by the Victorian L… ../papers/doi-00657ec6b105.html https://pubmed.ncbi.nlm.nih.gov/42001305/#2026-04-20#pubmed-ai Mon, 20 Apr 2026 11:48:52 +0800 INTRODUCTION: Military-Civilian Partnerships (MCP) were developed to mitigate degradation of combat medical readiness during peacetime. Although these programs have historically focused on sustaining surgical readiness and training military physicians, MCP increasingly augment training for Army Combat Medics, Navy Hospital Corpsmen, Air Force Aerospace Service Specialist, and other non-physician military medical personnel. The effectiveness, scalability, and alignment of MCP along with evolving… ../papers/doi-d4977a45ef49.html https://pubmed.ncbi.nlm.nih.gov/41996418/#2026-04-18#pubmed-ai Sat, 18 Apr 2026 11:26:55 +0800 This paper presents a study of the impact of corpus selection and vocabulary design on the performance of T5-based language models in clinical and biomedical domains. We introduce five different T5-EHR models, each pretrained from scratch using different combinations of clinical and biomedical corpora alongside domain-specific vocabularies. We evaluated these models across a variety of clinical and biomedical tasks to quantify the impact of pretraining data and vocabulary tokenization choices o… ../papers/doi-04f076dfee40.html https://pubmed.ncbi.nlm.nih.gov/41994492/#2026-04-18#pubmed-ai Sat, 18 Apr 2026 11:26:55 +0800 PURPOSE: Vision-language models (VLMs) are increasingly used to interpret multimodal educational materials, yet their reliability on diagram-, equation-, and text-dense scientific lecture slides remains poorly understood. This work introduces Medical Imaging Lecture Understanding (MILU), a large-scale benchmark designed to characterize cross-model variability in structured understanding of real medical imaging lectures. APPROACH: MILU includes 23 lecture sets with 1117 slides. LLaVA-OneVision,… ../papers/doi-b257aeab2d15.html https://pubmed.ncbi.nlm.nih.gov/41994367/#2026-04-18#pubmed-ai Sat, 18 Apr 2026 11:26:55 +0800 BACKGROUND: Antiseizure medications (ASMs) are frequently co-prescribed and are associated with a high risk of clinically significant drug-drug interactions (DDIs). Large language models (LLMs) are increasingly used for clinical queries, yet their performance in detecting ASM-related DDIs compared with established drug interaction databases remains uncertain. METHODS: A cross-sectional comparative study evaluated 186 ASM-comedication pairs (126 classified as major/moderate by Lexicomp) using Ch… ../papers/doi-4950fa4bce35.html https://pubmed.ncbi.nlm.nih.gov/41996440/#2026-04-18#pubmed-ai Sat, 18 Apr 2026 11:26:55 +0800 Existing object re-identification (re-ID) and composed image retrieval (CIR) methods capture different aspects of real-world retrieval requirements; re-ID preserves identity but cannot specify desired appearance changes, whereas CIR supports attribute-guided retrieval but does not enforce identity consistency. To bridge this gap, we introduce composed object re-identification (CORI), a new task that requires the retrieved target to simultaneously satisfy identity preservation and text-guided at… ../papers/doi-ff821e86a727.html https://pubmed.ncbi.nlm.nih.gov/41996403/#2026-04-18#pubmed-ai Sat, 18 Apr 2026 11:26:55 +0800 The rapid proliferation of Internet of Medical Things (IoMT) devices in healthcare environments has created critical cybersecurity vulnerabilities that demand both accurate and interpretable intrusion detection solutions. Existing deep learning-based intrusion detection systems (IDS) achieve high detection accuracy but lack inherent explainability, limiting their clinical adoption under regulatory frameworks such as GDPR and FDA guidelines. This paper presents MedDefender-MHAN, an explainable m… ../papers/doi-caeec9f876b5.html https://pubmed.ncbi.nlm.nih.gov/41990239/#2026-04-17#pubmed-ai Fri, 17 Apr 2026 11:39:21 +0800 OBJECTIVES: Patients with rare diseases often face long delays before receiving a diagnosis. Using electronic health records for automated phenotyping and diagnosis of rare diseases is a promising approach but can be challenging because critical information is often recorded in unstructured notes rather than structured fields. This systematic review synthesizes the current literature applying natural language processing (NLP) and large language models (LLMs) for rare disease phenotyping and dia… ../papers/doi-2fe134b4d7bc.html https://pubmed.ncbi.nlm.nih.gov/41989203/#2026-04-17#pubmed-ai Fri, 17 Apr 2026 11:39:21 +0800 ObjectivesAccurate triage of lumbar spine magnetic resonance imaging (MRI) referrals for sciatica is important for patient assessment, diagnosis and surgical planning. This study evaluates the accuracy and speed of large language models (LLMs) in automatically vetting lumbar spine MRI referrals from general practice.MethodsThree LLMs (GPT-4, Claude Opus, Gemini) were tasked with assigning an outcome (Accept - Routine, Accept - Urgent, Reject) and flagging MRI contraindications for lumbar spine… ../papers/doi-71303bb82f13.html https://pubmed.ncbi.nlm.nih.gov/41989909/#2026-04-17#pubmed-ai Fri, 17 Apr 2026 11:39:21 +0800 Multimodal large language models (MLLMs) offer immense potential for biomedical AI, yet current applications remain limited to coarse-grained image understanding and basic textual queries-falling short of the fine-grained reasoning required in clinical contexts. In this work, we present a comprehensive solution spanning data, model, and training innovations to advance pixel-level multimodal intelligence in biomedicine. First, we construct MeCoVQA, a new visual-language benchmark that spans eigh… ../papers/doi-d44eb8c5ebfc.html https://pubmed.ncbi.nlm.nih.gov/41990328/#2026-04-17#pubmed-ai Fri, 17 Apr 2026 11:39:21 +0800 OBJECTIVE: Computable phenotypes derived from electronic health records (EHRs) are central to clinical research and quality reporting. Although large language models (LLMs) can extract clinically rich information from unstructured notes, routine application to all patients is computationally expensive. We evaluated whether uncertainty-guided selective use of LLMs can improve phenotyping accuracy while preserving scalability. MATERIALS AND METHODS: We developed a selective augmentation framework… ../papers/doi-fa629176d611.html https://pubmed.ncbi.nlm.nih.gov/41989204/#2026-04-17#pubmed-ai Fri, 17 Apr 2026 11:39:21 +0800 OBJECTIVES: This study conducted an informatics system evaluation of two LLMs (GPT-4o and DeepSeek-V3) for patient education, combining clinician-rated quality with patient-perceived usability across thematically stratified queries. MATERIALS AND METHODS: In a blinded, within-subject design, 16 frequently asked questions about biologic therapies were categorized into three domains: treatment/drug selection, safety/adverse effects, and special conditions/daily life. Responses were standardized,… ../papers/doi-39281e964532.html https://pubmed.ncbi.nlm.nih.gov/41984624/#2026-04-16#pubmed-ai Thu, 16 Apr 2026 11:43:00 +0800 BACKGROUND: Accurate tumor node metastasis (TNM) staging is fundamental for treatment planning and prognosis in non-small cell lung cancer (NSCLC). However, its complexity poses significant challenges. Traditional rule-based natural language processing methods are constrained by their reliance on manually crafted rules and are susceptible to inconsistencies in clinical reporting. OBJECTIVE: This study aimed to develop and validate a robust, accurate, and operationally efficient artificial intel… ../papers/doi-5a4aadf4d2b0.html https://pubmed.ncbi.nlm.nih.gov/41982804/#2026-04-16#pubmed-ai Thu, 16 Apr 2026 11:43:00 +0800 PURPOSE: Psychiatric diagnosis faces significant challenges due to subjective symptom reporting and complex diagnostic criteria. While Large Language Models (LLMs) offer potential clinical decision support, their implementation is hindered by privacy constraints on commercial models (e.g., GPT-o3, Gemini-2.5) and computational demands of massive-scale open-source alternatives (e.g., DeepSeek-R1). These constraints necessitate knowledge-enhanced approaches with smaller-scale LLMs as the primary… ../papers/doi-442942d6cd6f.html https://pubmed.ncbi.nlm.nih.gov/41985066/#2026-04-16#pubmed-ai Thu, 16 Apr 2026 11:43:00 +0800 BACKGROUND: Large language models use machine learning to produce natural language. These models have a range of potential applications in health care, such as patient education and diagnosis. However, evaluations of large language models in health care are still scarce. OBJECTIVE: This study aimed to (1) evaluate the accuracy and efficiency of automated fact-checking by 2 large language models and (2) illustrate a process through which a large language model might support a patient in redrafti… ../papers/doi-80e22cb1c8f2.html https://pubmed.ncbi.nlm.nih.gov/41983921/#2026-04-16#pubmed-ai Thu, 16 Apr 2026 11:43:00 +0800 Purpose To develop a fine-tuned large language model (Medical Imaging Report Assistant, MIRA) and evaluate its performance in generating radiology impressions from multicenter data with respect to accuracy, reporting efficiency, and clinical applicability. Materials and Methods A retrospective multicenter dataset comprising 1.87 million radiology reports (including CT, MRI, and digital radiography data) from 42 hospitals across 22 provinces in China (January 2019 to August 2024) was compiled. T… ../papers/doi-1c2530337309.html https://pubmed.ncbi.nlm.nih.gov/41982325/#2026-04-16#pubmed-ai Thu, 16 Apr 2026 11:43:00 +0800 BACKGROUND AND OBJECTIVES: Traditional medical board examinations present clinical information in static vignettes with multiple-choices (MC), fundamentally different from how physicians gather and integrate data in practice. Recent advances in large language models (LLMs) offer promising approaches to creating more realistic clinical interactive conversations. However, these approaches are limited in neurosurgery, where patient communication capacity varies significantly and diagnosis heavily… ../papers/doi-c5e38020b821.html https://pubmed.ncbi.nlm.nih.gov/41979962/#2026-04-15#pubmed-ai Wed, 15 Apr 2026 11:35:50 +0800 The emergence of Large Vision-Language Models (LVLMs) marks significant strides towards achieving general artificial intelligence. However, these advancements are accompanied by concerns about biased outputs, a challenge that has yet to be thoroughly explored. Existing benchmarks are not sufficiently comprehensive in evaluating biases due to their limited data scale, single questioning format and narrow sources of bias. To address this problem, we introduce VLBiasBench, a comprehensive benchmar… ../papers/doi-fb5d26b2eb57.html https://pubmed.ncbi.nlm.nih.gov/41979660/#2026-04-15#pubmed-ai Wed, 15 Apr 2026 11:35:50 +0800 The use of multimodal data is essential for the precise diagnosis and treatment of brain tumors. In this context, multimodal data encompass multisequence magnetic resonance imaging, computed tomography, positron emission tomography, histopathological images, molecular and genomic profiles, structured clinical variables, and radiological reports. With the rapid advancement of artificial intelligence, integrating these heterogeneous data sources has become a central research direction for improvi… ../papers/doi-48f3f7f35ec5.html https://pubmed.ncbi.nlm.nih.gov/41979955/#2026-04-15#pubmed-ai Wed, 15 Apr 2026 11:35:50 +0800 Vision-language models in healthcare face a critical limitation, i.e., the modality gap, where image and text embeddings occupy distantly separated regions in shared representation space. This is reinforced by traditional contrastive learning objectives, and manifests itself through fundamental constraints in cross-modal understanding and downstream task performance. Existing approaches focus on addressing input-level requirements, however, the geometric constraints imposed by multimodal contra… ../papers/doi-d5f518895cd3.html https://pubmed.ncbi.nlm.nih.gov/41980262/#2026-04-15#pubmed-ai Wed, 15 Apr 2026 11:35:50 +0800 BACKGROUND: Artificial intelligence-powered conversational agents (ie, chatbots) are increasingly popular outlets for users seeking psychological support, yet little is known about how users experience early-stage prototypes or which therapeutic processes contribute to clinical improvement. A transparent evaluation of emerging chatbot prototypes is needed to clarify if, how, and why artificial intelligence companions work and to guide their continued development. OBJECTIVE: This mixed methods p… ../papers/doi-e4a154d94827.html https://pubmed.ncbi.nlm.nih.gov/41979097/#2026-04-15#pubmed-ai Wed, 15 Apr 2026 11:35:50 +0800 OBJECTIVE: Rapid advancements in artificial intelligence (AI) technologies offer new opportunities in medical education. The aim of this study is to compare the performance of large language models, specifically ChatGPT-4 and Gemini, in analyzing clinical scenarios with that of chest diseases research assistants (residents), and to evaluate their potential roles in medical education. MATERIAL AND METHODS: This cross-sectional, comparative study included 28 resident physicians working in the dep… ../papers/doi-d201429cca0c.html https://pubmed.ncbi.nlm.nih.gov/41973653/#2026-04-14#pubmed-ai Tue, 14 Apr 2026 11:37:06 +0800 BACKGROUND: Translation of medical consultation summaries is essential for equitable health care communication in culturally and linguistically diverse populations. While machine translation (MT) tools and large language models (LLMs) are widely accessible, their feasibility and safety for health care contexts remain underexplored. OBJECTIVE: This pilot study investigates the feasibility and limitations of using LLMs and traditional MT tools to translate medical consultation summaries from Engl… ../papers/doi-a4e602e64d3b.html https://pubmed.ncbi.nlm.nih.gov/41972595/#2026-04-14#pubmed-ai Tue, 14 Apr 2026 11:37:06 +0800 The aim of this study was to assess and benchmark plastic consumption in sample preparation for forensic analysis, alongside the development of an LC-MS method for ketamine analogues in whole blood, with various sustainability-related scores and parameters examined throughout. Ketamine analogues are emerging psychoactive substances associated with intoxication and fatalities globally. An analytical method was developed for determining ketamine and eight of its analogues in human whole blood. Fo… ../papers/doi-b67edb02c604.html https://pubmed.ncbi.nlm.nih.gov/41973591/#2026-04-14#pubmed-ai Tue, 14 Apr 2026 11:37:06 +0800 Multimodal Large Language Models (MLLMs) have shown exceptional capabilities in vision-language tasks. However, effectively integrating image segmentation into these models remains a significant challenge. In this work, we propose a novel text-as-mask paradigm that casts image segmentation as a text generation problem, eliminating the need for additional decoders and significantly simplifying the segmentation process. Our key innovation is semantic descriptors, a new textual representation of s… ../papers/doi-ce81229c31f1.html https://pubmed.ncbi.nlm.nih.gov/41969623/#2026-04-14#pubmed-ai Tue, 14 Apr 2026 11:37:06 +0800 OBJECTIVE: The FDA requires clinical trials to reflect real-world diversity. Systemic lupus erythematosus (SLE) is a disease that disproportionately affects individuals of Black African descent that has not been assessed for diversity in clinical trials to date. This study compared demographics from two real-world data (RWD) sources and proposes parameters for representative trial populations. METHODS: Demographics of United States (US) SLE patients were extracted from electronic health records… ../papers/doi-a326948aeb7e.html https://pubmed.ncbi.nlm.nih.gov/41970036/#2026-04-14#pubmed-ai Tue, 14 Apr 2026 11:37:06 +0800 OBJECTIVE: To compare the performance of state-of-the-art Gemini and GPT models on ophthalmology board-style questions and examine variation by subspecialty, cognitive complexity, and question type. DESIGN: A cross-sectional evaluation of 12 distinct large language model (LLM) configurations using a standardized ophthalmology question set. SUBJECTS: Five hundred multiple-choice questions (250 from the American Academy of Ophthalmology's Basic and Clinical Science Course [BCSC]; 250 StatPearls).… ../papers/doi-4321668119eb.html https://pubmed.ncbi.nlm.nih.gov/41965370/#2026-04-12#pubmed-ai Sun, 12 Apr 2026 22:15:33 +0800 We report on the integration of three methods that predict, on a proteome-wide scale, whether two proteins are likely to form a binary complex. The methods include PrePPI, which uses three-dimensional structure information as a basis for predictions, Topsy-Turvy, which uses a protein language model, and ZEPPI, which uses evolutionary information to evaluate protein-protein interfaces. Testing on the high-quality HINT database of binary PPIs reveals that the integrated method has better performa… ../papers/doi-4f9dec389ad2.html https://pubmed.ncbi.nlm.nih.gov/41963950/#2026-04-11#pubmed-ai Sat, 11 Apr 2026 23:09:08 +0800 《Factors influencing large language model adoption among dental students: a cross-sectional study.》〔应用 / 方法〕：This research evaluates the factors influencing the behavioural intention (BI) to adopt large language models… 归档日期：2026-04-11。 ../papers/doi-a877eb92b775.html https://pubmed.ncbi.nlm.nih.gov/41963879/#2026-04-11#pubmed-ai Sat, 11 Apr 2026 23:09:08 +0800 《Factors influencing large language model adoption among dental students: a cross-sectional study.》〔应用 / 方法〕：This research evaluates the factors influencing the behavioural intention (BI) to adopt large language models… 归档日期：2026-04-11。 ../papers/doi-cecfcd7bdb9a.html https://pubmed.ncbi.nlm.nih.gov/41963402/#2026-04-11#pubmed-ai Sat, 11 Apr 2026 23:09:08 +0800 《Factors influencing large language model adoption among dental students: a cross-sectional study.》〔应用 / 方法〕：This research evaluates the factors influencing the behavioural intention (BI) to adopt large language models… 归档日期：2026-04-11。 ../papers/doi-d7ceffe25bc3.html https://pubmed.ncbi.nlm.nih.gov/41963506/#2026-04-11#pubmed-ai Sat, 11 Apr 2026 23:09:08 +0800 This research evaluates the factors influencing the behavioural intention (BI) to adopt large language models (LLMs) among dental students in education, clinical decision support (CDS), and research, using the original unified theory of acceptance and use of technology (UTAUT) model, representing the first application of this model in this specific context. LLM adoption among Saudi dental students is unstructured and unregulated, making empirical evidence on adoption factors an educational and… ../papers/pubmed-cfbd39a5af79.html https://pubmed.ncbi.nlm.nih.gov/41951933/#2026-04-09#pubmed-ai Thu, 09 Apr 2026 14:51:56 +0800 Category fluency tasks involve producing words constrained by a semantic field (animals). Subcategory fluency involves producing words from categories that are semantically related to a superordinate category but form a restricted set of items (farm animals). Here, we study whether people produce different patterns of words in category versus subcategory fluency by looking at differences in the total number of words produced, the properties of the words produced (e.g., frequency) and how people… ../papers/pubmed-2ac8d2f3d4a5.html https://pubmed.ncbi.nlm.nih.gov/41951911/#2026-04-09#pubmed-ai Thu, 09 Apr 2026 14:51:56 +0800 Engineering proteins with desired functions remains challenging and usually requires multiple rounds of screening and selection. Here, we present Sequence Display, a platform that generates large-scale protein sequence-activity datasets in a single round. Sequence Display enables multiplexed assessment of individual variant activity within a single experiment, offering a robust approach to mapping detailed sequence-function relationships. We demonstrate the platform's broad applicability by gen… ../papers/pubmed-0be57f95f498.html https://pubmed.ncbi.nlm.nih.gov/41951858/#2026-04-09#pubmed-ai Thu, 09 Apr 2026 14:51:56 +0800 Large Language Models (LLMs) are increasingly deployed in medicine. However, their utility for non-generative clinical prediction is under-evaluated, and they are often assumed to be inferior to specialized models, creating potential for misuse and misunderstanding. To address this, our ClinicRealm benchmark systematically evaluates 15 GPT-style LLMs, 5 BERT-style models, and 11 traditional methods on unstructured clinical notes and structured Electronic Health Records (EHR) across predictive p… ../papers/pubmed-b4af6d31f5b6.html https://pubmed.ncbi.nlm.nih.gov/41951757/#2026-04-09#pubmed-ai Thu, 09 Apr 2026 14:51:56 +0800 Researchers and clinicians are increasingly looking to leverage artificial intelligence (AI) and digital tools to improve psychiatric care. Of particular promise is addressing the youth mental health crisis. Yet, the introduction of AI-enabled digital technologies for psychiatric treatment of young adults raises a host of ethical, legal, and societal issues (ELSI). To provide guidance in addressing these issues, we convened a two-day meeting at the Radcliffe Institute for Advanced Study at Harv… ../papers/pubmed-ad25e76e37b0.html https://pubmed.ncbi.nlm.nih.gov/41951755/#2026-04-09#pubmed-ai Thu, 09 Apr 2026 14:51:56 +0800 Human stem-cell-based embryo models (hSCBEMs) offer unprecedented opportunities for basic and translational research. However, the rapid pace of scientific developments in the field challenges the slower, traditional modes of ethics evaluation. To facilitate responsible research and governance, and ensure public trust, we propose using 'embedded ethics' as a purpose-anchored, dynamic, iterative and integrative approach where ethicists and scientists engage in continuous dialogue to ethically as…