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MCP-AI: Protocol-Driven Intelligence Framework for Autonomous Reasoning in Healthcare

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zag ElSayed · Craig Erickson · Ernest Pedapati
arXiv:2512.05365v1 Announce Type: new Abstract: Healthcare AI systems have historically faced challenges in merging contextual reasoning, long-term state management, and human-verifiable workflows into a cohesive framework. This paper introduces a completely innovative architecture and concept: combining the Model Context Protocol (MCP) with a specific clinical application, known as MCP-AI. This integration allows intelligent agents to reason over extended periods, collaborate securely, and adhere to authentic clinical logic, representing a significant shift away from traditional Clinical Decision Support Systems (CDSS) and prompt-based Large Language Models (LLMs). As healthcare systems become more complex, the need for autonomous, context-aware clinical reasoning frameworks has become urgent. We present MCP-AI, a novel architecture for explainable medical decision-making built upon the Model Context Protocol (MCP) a modular, executable specification for orchestrating generative and descriptive AI agents in real-time workflows. Each MCP file captures clinical objectives, patient context, reasoning state, and task logic, forming a reusable and auditable memory object. Unlike conventional CDSS or stateless prompt-based AI systems, MCP-AI supports adaptive, longitudinal, and collaborative reasoning across care settings. MCP-AI is validated through two use cases: (1) diagnostic modeling of Fragile X Syndrome with comorbid depression, and (2) remote coordination for Type 2 Diabetes and hypertension. In either scenario, the protocol facilitates physician-in-the-loop validation, streamlines clinical processes, and guarantees secure transitions of AI responsibilities between healthcare providers. The system connects with HL7/FHIR interfaces and adheres to regulatory standards, such as HIPAA and FDA SaMD guidelines. MCP-AI provides a scalable basis for interpretable, composable, and safety-oriented AI within upcoming clinical environments.
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The Missing Layer of AGI: From Pattern Alchemy to Coordination Physics

✇cs.AI, q-bio.NC updates on arXiv.org
By: Edward Y. Chang
arXiv:2512.05765v1 Announce Type: new Abstract: Influential critiques argue that Large Language Models (LLMs) are a dead end for AGI: "mere pattern matchers" structurally incapable of reasoning or planning. We argue this conclusion misidentifies the bottleneck: it confuses the ocean with the net. Pattern repositories are the necessary System-1 substrate; the missing component is a System-2 coordination layer that selects, constrains, and binds these patterns. We formalize this layer via UCCT, a theory of semantic anchoring that models reasoning as a phase transition governed by effective support (rho_d), representational mismatch (d_r), and an adaptive anchoring budget (gamma log k). Under this lens, ungrounded generation is simply an unbaited retrieval of the substrate's maximum likelihood prior, while "reasoning" emerges when anchors shift the posterior toward goal-directed constraints. We translate UCCT into architecture with MACI, a coordination stack that implements baiting (behavior-modulated debate), filtering (Socratic judging), and persistence (transactional memory). By reframing common objections as testable coordination failures, we argue that the path to AGI runs through LLMs, not around them.
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XR-DT: Extended Reality-Enhanced Digital Twin for Agentic Mobile Robots

✇cs.AI, q-bio.NC updates on arXiv.org
By: Tianyi Wang · Jiseop Byeon · Ahmad Yehia · Huihai Wang · Yiming Xu · Tianyi Zeng · Ziran Wang · Junfeng Jiao · Christian Claudel
arXiv:2512.05270v1 Announce Type: cross Abstract: As mobile robots increasingly operate alongside humans in shared workspaces, ensuring safe, efficient, and interpretable Human-Robot Interaction (HRI) has become a pressing challenge. While substantial progress has been devoted to human behavior prediction, limited attention has been paid to how humans perceive, interpret, and trust robots' inferences, impeding deployment in safety-critical and socially embedded environments. This paper presents XR-DT, an eXtended Reality-enhanced Digital Twin framework for agentic mobile robots, that bridges physical and virtual spaces to enable bi-directional understanding between humans and robots. Our hierarchical XR-DT architecture integrates virtual-, augmented-, and mixed-reality layers, fusing real-time sensor data, simulated environments in the Unity game engine, and human feedback captured through wearable AR devices. Within this framework, we design an agentic mobile robot system with a unified diffusion policy for context-aware task adaptation. We further propose a chain-of-thought prompting mechanism that allows multimodal large language models to reason over human instructions and environmental context, while leveraging an AutoGen-based multi-agent coordination layer to enhance robustness and collaboration in dynamic tasks. Initial experimental results demonstrate accurate human and robot trajectory prediction, validating the XR-DT framework's effectiveness in HRI tasks. By embedding human intention, environmental dynamics, and robot cognition into the XR-DT framework, our system enables interpretable, trustworthy, and adaptive HRI.
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Simulating Life Paths with Digital Twins: AI-Generated Future Selves Influence Decision-Making and Expand Human Choice

✇cs.AI, q-bio.NC updates on arXiv.org
By: Rachel Poonsiriwong · Chayapatr Archiwaranguprok · Constanze Albrecht · Peggy Yin · Nattavudh Powthavee · Hal Hershfield · Monchai Lertsutthiwong · Kavin Winson · Pat Pataranutaporn
arXiv:2512.05397v1 Announce Type: cross Abstract: Major life transitions demand high-stakes decisions, yet people often struggle to imagine how their future selves will live with the consequences. To support this limited capacity for mental time travel, we introduce AI-enabled digital twins that have ``lived through'' simulated life scenarios. Rather than predicting optimal outcomes, these simulations extend prospective cognition by making alternative futures vivid enough to support deliberation without assuming which path is best. We evaluate this idea in a randomized controlled study (N=192) using multimodal synthesis - facial age progression, voice cloning, and large language model dialogue - to create personalized avatars representing participants 30 years forward. Young adults 18 to 28 years old described pending binary decisions and were assigned to guided imagination or one of four avatar conditions: single-option, balanced dual-option, or expanded three-option with a system-generated novel alternative. Results showed asymmetric effects: single-sided avatars increased shifts toward the presented option, while balanced presentation produced movement toward both. Introducing a system-generated third option increased adoption of this new alternative compared to control, suggesting that AI-generated future selves can expand choice by surfacing paths that might otherwise go unnoticed. Participants rated evaluative reasoning and eudaimonic meaning-making as more important than emotional or visual vividness. Perceived persuasiveness and baseline agency predicted decision change. These findings advance understanding of AI-mediated episodic prospection and raise questions about autonomy in AI-augmented decisions.
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M4-RAG: A Massive-Scale Multilingual Multi-Cultural Multimodal RAG

✇cs.AI, q-bio.NC updates on arXiv.org
By: David Anugraha · Patrick Amadeus Irawan · Anshul Singh · En-Shiun Annie Lee · Genta Indra Winata
arXiv:2512.05959v1 Announce Type: cross Abstract: Vision-language models (VLMs) have achieved strong performance in visual question answering (VQA), yet they remain constrained by static training data. Retrieval-Augmented Generation (RAG) mitigates this limitation by enabling access to up-to-date, culturally grounded, and multilingual information; however, multilingual multimodal RAG remains largely underexplored. We introduce M4-RAG, a massive-scale benchmark covering 42 languages and 56 regional dialects and registers, comprising over 80,000 culturally diverse image-question pairs for evaluating retrieval-augmented VQA across languages and modalities. To balance realism with reproducibility, we build a controlled retrieval environment containing millions of carefully curated multilingual documents relevant to the query domains, approximating real-world retrieval conditions while ensuring consistent experimentation. Our systematic evaluation reveals that although RAG consistently benefits smaller VLMs, it fails to scale to larger models and often even degrades their performance, exposing a critical mismatch between model size and current retrieval effectiveness. M4-RAG provides a foundation for advancing next-generation RAG systems capable of reasoning seamlessly across languages, modalities, and cultural contexts.
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ToolMind Technical Report: A Large-Scale, Reasoning-Enhanced Tool-Use Dataset

✇cs.AI, q-bio.NC updates on arXiv.org
By: Chen Yang · Ran Le · Yun Xing · Zhenwei An · Zongchao Chen · Wayne Xin Zhao · Yang Song · Tao Zhang
arXiv:2511.15718v2 Announce Type: replace Abstract: Large Language Model (LLM) agents have developed rapidly in recent years to solve complex real-world problems using external tools. However, the scarcity of high-quality trajectories still hinders the development of stronger LLM agents. Most existing works on multi-turn dialogue synthesis validate correctness only at the trajectory level, which may overlook turn-level errors that can propagate during training and degrade model performance. To address these limitations, we introduce ToolMind, a large-scale, high-quality tool-agentic dataset with 160k synthetic data instances generated using over 20k tools and 200k augmented open-source data instances. Our data synthesis pipeline first constructs a function graph based on parameter correlations and then uses a multi-agent framework to simulate realistic user-assistant-tool interactions. Beyond trajectory-level validation, we employ fine-grained turn-level filtering to remove erroneous or suboptimal steps, ensuring that only high-quality reasoning traces are retained. This approach mitigates error amplification during training while preserving self-corrective reasoning signals essential for robust tool-use learning. Models fine-tuned on ToolMind show significant improvements over baselines on several benchmarks.
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The AI Productivity Index (APEX)

✇cs.AI, q-bio.NC updates on arXiv.org
By: Bertie Vidgen · Abby Fennelly · Evan Pinnix · Julien Benchek · Daniyal Khan · Zach Richards · Austin Bridges · Calix Huang · Ben Hunsberger · Isaac Robinson · Akul Datta · Chirag Mahapatra · Dominic Barton · Cass R. Sunstein · Eric Topol · Brendan Foody · Osvald Nitski
arXiv:2509.25721v4 Announce Type: replace-cross Abstract: We present an extended version of the AI Productivity Index (APEX-v1-extended), a benchmark for assessing whether frontier models are capable of performing economically valuable tasks in four jobs: investment banking associate, management consultant, big law associate, and primary care physician (MD). This technical report details the extensions to APEX-v1, including an increase in the held-out evaluation set from n = 50 to n = 100 cases per job (n = 400 total) and updates to the grading methodology. We present a new leaderboard, where GPT5 (Thinking = High) remains the top performing model with a score of 67.0%. APEX-v1-extended shows that frontier models still have substantial limitations when performing typical professional tasks. To support further research, we are open sourcing n = 25 non-benchmark example cases per role (n = 100 total) along with our evaluation harness.
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Designing LLM-based Multi-Agent Systems for Software Engineering Tasks: Quality Attributes, Design Patterns and Rationale

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yangxiao Cai · Ruiyin Li · Peng Liang · Mojtaba Shahin · Zengyang Li
arXiv:2511.08475v2 Announce Type: replace-cross Abstract: As the complexity of Software Engineering (SE) tasks continues to escalate, Multi-Agent Systems (MASs) have emerged as a focal point of research and practice due to their autonomy and scalability. Furthermore, through leveraging the reasoning and planning capabilities of Large Language Models (LLMs), the application of LLM-based MASs in the field of SE is garnering increasing attention. However, there is no dedicated study that systematically explores the design of LLM-based MASs, including the Quality Attributes (QAs) on which designers mainly focus, the design patterns used by designers, and the rationale guiding the design of LLM-based MASs for SE tasks. To this end, we conducted a study to identify the QAs that LLM-based MASs for SE tasks focus on, the design patterns used in the MASs, and the design rationale for the MASs. We collected 94 papers on LLM-based MASs for SE tasks as the source. Our study shows that: (1) Code Generation is the most common SE task solved by LLM-based MASs among ten identified SE tasks, (2) Functional Suitability is the QA on which designers of LLM-based MASs pay the most attention, (3) Role-Based Cooperation is the design pattern most frequently employed among 16 patterns used to construct LLM-based MASs, and (4) Improving the Quality of Generated Code is the most common rationale behind the design of LLM-based MASs. Based on the study results, we presented the implications for the design of LLM-based MASs to support SE tasks.
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Critical Appraisal Tools for Evaluating Artificial Intelligence in Clinical Studies: Scoping Review

✇Journal of Medical Internet Research
By: Juan B Cabello · Vicente Ruiz Garcia · Miguel Torralba · Miguel Maldonado Fernandez · Marimar Ubeda · Eukene Ansuategui · Luis Ramos-Ruperto · Jose I Emparanza · Iratxe Urreta · Maria Teresa Iglesias · Jose I Pijoan · Amanda Burls
Background: Health research that uses predictive and/or generative AI is rapidly growing. Just as in traditional clinical studies, the way in which AI studies are conducted can introduce systematic errors. Transmission of this AI evidence into clinical practice and research needs critical appraisal tools for clinical decision makers and researchers. Objective: To identify existing tools for critical appraisal of clinical studies that use artificial intelligence (AI) and examine the concepts and domains these tools explore. Methods: Inclusion criteria in PCC framework P: (population) Artificial intelligence clinical studies. C (Concept): tools for critical appraisal and associated constructs such as: quality, reporting, validity, risk of bias, and applicability. C (context): in clinical practice context. In addition, bias classification and Chatbot assessment studies were included. We searched in medical and engineering databases (MEDLINE, EMBASE, CINAHL, PsycINFO and IEEE). We included clinical primary research with tools for critical appraisal. Classic reviews and systematic reviews were included in first phase of screening. They were excluded in the secondary phase, after identifying new tools by forward snowballing. We excluded non-human, computer and mathematical research, and letters, opinion papers and editorials. We used Rayyan for screening. Data extraction was done by two observers and discrepancies were solved by discussion. The protocol was previously registered in OSF (https://doi.org/10.17605/OSF.IO/ETYDS). We adhered to the PRISMA extension for Scoping reviews and to the PRISMA-Search extension for Reporting Literature in Systematic Reviews. Results: We retrieved 4393 unique records for screening. After excluding 3803 records, 119 were selected for full-text screening. From these, 59 were excluded. After inclusion of 10 studies via other methods, a total of 70 records were finally included. 46 of them were reporting guidelines (15 tools for critical appraisal, 2 for quality of study and 2 for risk of bias). Nine papers ware focused on bias classification or mitigation. We found 15 Chatbots assessment studies or systematic reviews of Chatbots studies (6 and 9 respectively) which are a very heterogeneous group. Conclusions: The results picture a landscape of the evidence tools where reporting tools predominate, followed by critical appraisal and risk of bias tools, and few tools for risk of bias. The mismatch of bias in AI and epidemiology should be considered for critical appraisal, especially regarding fairness and the mitigation bias in the AI. Finally, Chatbot assessment studies is a vast and evolving field in which progress in design, reporting and critical appraisal is necessary and urgent. Clinical Trial: https://doi.org/10.17605/OSF.IO/ETYDS
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Exploring a Digital Health Solution to Collect and Manage Health-Related Needs for Patients Who Undergo Complex Surgery: Mixed Methods Study

✇Journal of Medical Internet Research
By: Naleef Fareed · Humza Asgher · Diamantis Tsilimigras · Odysseas P Chatzipanagiotou · Giovanni Catalano · Bridget Hartwell · Kathleen Bolton · Timothy M Pawlik
Background: Patients who undergo complex surgery (e.g., esophagectomy, liver resection) often experience substantial burden of health-related needs (medical, social, and behavioral health). A closed loop digital solution could facilitate the collection and resolution of health-related needs by care team members for patients who undergo complex surgery. A digital solution may facilitate adherence to a clear treatment plan and concomitantly reduce surgical complications and readmissions associated with unmet health-related needs, which remain persistent challenges across health care settings. Objective: To establish problems and gaps in the collection, integration, and management of health-related needs and identify a set of user specifications for a digital solution to collect and manage health-related needs, specifically medical, social, and behavioral needs for patients who undergo complex surgery. Methods: We applied the Double Diamond Framework and organized the study into two sequential phases: (1) qualitative methods to discover patients’ and care team members’ perspectives on health-related needs; (2) participatory design sessions to gain feedback and sentiment about ideal features of a digital solution. Both phases were conducted between December 2023 and March 2025. We supplemented both phases with analysis of electronic health record (EHR) data for patients who underwent complex surgery at our academic medical center (AMC). Results: Extensive themes emerged from interviews with patients (n=20) and care team members (n=24), capturing their health-related and surgical experiences as well as desired features for a proposed digital solution. Our swim lane diagram demonstrated four critical gaps in workflow: (1) heterogeneity in the approach to screening, monitoring, and managing health-related needs; (2) patients felt uncomfortable reporting health-related needs, particularly behavioral and social needs, to their care team; (3) lack of access to referral resources to resolve needs; and (4) the need for a closed loop intervention for patients and care team members. A subset of participants from Phase 1 (n=5 patients and n=9 care team members) provided feedback on preferred features, drawing from digital tools currently available in the EHR at our AMC. Among four existing EHR tools tested, there were notable variations in how patients and care team members felt about their potential use. Participants also provided extensive feedback for preferred components (e.g., goals and active plans) that should be available in an existing or custom digital solution to manage health-related needs. Findings from the qualitative interviews and design sessions were corroborated with EHR documentation. Conclusions: Digital solutions could provide a streamlined approach for collection and management of health-related needs in surgery, with the goal of addressing unmet needs and improving patient activation. This approach is critical to ensure patients, especially patients who undergo complex surgery, have positive health outcomes. We identified preferences for specific features in a proposed digital solution based on our systematic assessment that will inform future work.
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AI-driven transfer learning and classical molecular dynamics for strategic therapeutic repurposing and rational design of antiviral peptides targeting monkeypox virus DNA polymerase

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Leona Dcunha · Sonet Daniel Thomas · Levin John · Amritha Thaikkad · Dileep Francis · Rajesh Raju · Nik Hirani · Abhithaj Jayanandan

Comput Biol Med. 2025 Dec 7;200:111372. doi: 10.1016/j.compbiomed.2025.111372. Online ahead of print.

ABSTRACT

The emergence of monkeypox virus (MPXV) as a global health threat has necessitated the rapid identification of novel antiviral therapeutics. Currently, no FDA-approved drugs are specifically designed against the disease. We used an in-house deep learning pharmacophore model for screening a library of 1974 FDA-approved drugs targeting the active site of MPXV DNA polymerase. Three drugs exhibited the strongest binding affinities, outperforming the control drug, Cidofovir diphosphate, and forming stable interactions with key active site residues. Among them, Paromomycin emerged as the most favourable drug, demonstrating stable, persistent, and adaptable interactions in molecular dynamics simulation. In parallel, we developed a novel automated peptide-generating AI pipeline that integrates active-site residues with knowledge-guided amino acid selection to generate and evaluate synthetic peptides. Cysteine-Phenylalanine-Cysteine (CFC), together with a panel of candidates, emerged through rational balancing of physicochemical properties and drug-likeness for accelerated therapeutic discovery. Synthetic peptides were evaluated to further understand the binding efficacies with DNA polymerase. CFC peptide demonstrated strong binding affinity (-8.08 kcal/mol) through stable interactions with key catalytic residues ASP549, ARG634 and LYS661, while MMGBSA analysis confirmed favourable binding energy (-33.02 kcal/mol). Consistent results in MD simulations indicate functional binding without destabilisation. Although ADMET predictions for CFC revealed limitations in permeability and oral bioavailability, its favourable binding profile and reduced predicted toxicity support its potential as a novel antiviral lead.

PMID:41360016 | DOI:10.1016/j.compbiomed.2025.111372

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Explainable artificial intelligence and ensemble learning for hepatocellular carcinoma classification: State of the art, performance, and clinical implications

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Sami Akbulut · Cemil Colak

World J Hepatol. 2025 Nov 27;17(11):109494. doi: 10.4254/wjh.v17.i11.109494.

ABSTRACT

Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality globally, necessitating advanced diagnostic tools to improve early detection and personalized targeted therapy. This review synthesizes evidence on explainable ensemble learning approaches for HCC classification, emphasizing their integration with clinical workflows and multi-omics data. A systematic analysis [including datasets such as The Cancer Genome Atlas, Gene Expression Omnibus, and the Surveillance, Epidemiology, and End Results (SEER) datasets] revealed that explainable ensemble learning models achieve high diagnostic accuracy by combining clinical features, serum biomarkers such as alpha-fetoprotein, imaging features such as computed tomography and magnetic resonance imaging, and genomic data. For instance, SHapley Additive exPlanations (SHAP)-based random forests trained on NCBI GSE14520 microarray data (n = 445) achieved 96.53% accuracy, while stacking ensembles applied to the SEER program data (n = 1897) demonstrated an area under the receiver operating characteristic curve of 0.779 for mortality prediction. Despite promising results, challenges persist, including the computational costs of SHAP and local interpretable model-agnostic explanations analyses (e.g., TreeSHAP requiring distributed computing for metabolomics datasets) and dataset biases (e.g., SEER's Western population dominance limiting generalizability). Future research must address inter-cohort heterogeneity, standardize explainability metrics, and prioritize lightweight surrogate models for resource-limited settings. This review presents the potential of explainable ensemble learning frameworks to bridge the gap between predictive accuracy and clinical interpretability, though rigorous validation in independent, multi-center cohorts is critical for real-world deployment.

PMID:41358057 | PMC:PMC12679159 | DOI:10.4254/wjh.v17.i11.109494

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EIF3M as a pan-cancer biomarker: prognostic significance and immune infiltration association

✇(Multiomics OR Omics) AND (Pancreatic)
By: Zhilei Zhao · Jiaxing Chen · Yongqiang Cui · Zhihao Fu · Dongfeng Deng · Xiao Zhang

Front Mol Biosci. 2025 Nov 18;12:1697083. doi: 10.3389/fmolb.2025.1697083. eCollection 2025.

ABSTRACT

BACKGROUND: EIF3M, a core subunit of eukaryotic translation initiation factor 3, plays a pivotal role in protein synthesis by regulating the assembly of the 43S initiation complex. However, its biological functions in cancer remain poorly understood. To further investigate the clinical translational value and underlying mechanisms of EIF3M in tumors, this study conducted comprehensive bioinformatic analysis of EIF3M across various tumor types.

METHODS: We utilized publicly available databases to perform a comprehensive bioinformatics analysis of EIF3M's biological roles in oncogenesis, aiming to elucidate its pan-cancer expression patterns and prognostic significance. Furthermore, we conducted an integrative multi-omics analysis incorporating methylation profiling, co-expressed gene networks, targeted miRNA interactions, and tumor immune microenvironment infiltration to decipher the complex regulatory architecture and biological pathways mediated by EIF3M across cancer types. Finally, we used HCC cell lines for in vitro functional validation, determining how EIF3M expression modulates malignant phenotypic behaviors in hepatocellular carcinoma.

RESULTS: EIF3M was overexpressed in multiple cancers and correlated with advanced tumor stage and poor survival. Its dysregulation was primarily driven by gene amplification and regulated by promoter methylation and miRNAs. EIF3M functioned as a hub in cell cycle and transcriptional networks and was linked to an immunosuppressive microenvironment. In hepatocellular carcinoma models, EIF3M modulated tumor proliferation, migration, and activated oncogenic pathways like Wnt/β-catenin.

CONCLUSION: This study reveals that EIF3M expression correlates with immune infiltration and poor prognosis in multiple cancers. In vitro experiments in hepatocellular carcinoma models demonstrated that EIF3M critically regulates malignant cell behaviors. Collectively, our findings highlight EIF3M's value as a promising pan-cancer biomarker worthy of further investigation for its utility in prognosis prediction and as an indicator of immunotherapeutic response.

PMID:41341921 | PMC:PMC12669982 | DOI:10.3389/fmolb.2025.1697083

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Detecting Sociodemographic Biases in the Content and Quality of Large Language Model–Generated Nursing Care: Cross-Sectional Simulation Study

✇Journal of Medical Internet Research
By: Nan Bai · Yijing Yu · Chunyan Luo · Si Chen Zhou · Qing Wang · Huijing Zou · Qian Liu · Guanghui Fu · Wei Zhai · Qing Zhao · Jianqiang Li · Xinni Wei · Bing Xiang Yang
Background: Large language models (LLMs) are increasingly applied in healthcare. However, concerns remain that their nursing care recommendations may reflect patients’ sociodemographic attributes rather than clinical needs. Objective: To investigate potential biases in nursing care plans generated by LLMs, we focused on whether outputs differ systematically based on patients’ sociodemographic characteristics and assessed the implications for equitable nursing care. Methods: We utilized a standardized clinical scenario with GPT to generate care plans for 96 sociodemographic identity combinations, drawing on 9,600 tests. We conducted statistical analyses (t-tests and ANOVA) to analyze how text length and the frequency of physiological and psychological nursing terms varied across sociodemographic factors. Additionally, we utilized Python for data processing and visualization to ensure methodological rigor throughout the study. Results: The analysis revealed significant sociodemographic biases in LLMs-generated nursing care plans. Female patients received shorter care plans (t = 4.864, P
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Promoting Responsible DeepSeek Deployment in Health Care: Scoping Review Comparing Grey and White Literature

✇Journal of Medical Internet Research
By: Wang Jiang · Dan Wang · Yihang Zeng · Jiaqi Huang · Chang Xu · Chenxi Liu
Background: The rapid deployment of DeepSeek, an open-source large language model has sparked concerns of its impact on patient outcomes and safety. However, little is known about how DeepSeek is used and regulated in these facilities. Objective: This study aimed to 1) systematically review the characteristics of deployed DeepSeek in the top 100 hospitals in China; and 2) compare performances and risks from hospital disclosure with research evidence. Methods: We performed a scoping review of gray and white literature, collecting data from the top 100 Chinese hospitals. We extracted basic characteristics of DeepSeek, its aim, evaluation approach, performance, risk and hospital regulation. A coding framework was developedcovering LLMs application scenario, evaluation dimension and source of risk. Results: We identified a total of 58 DeepSeek models in 48 out of the top 100 Chinese hospitals as well as 27 studies. We observed deployed DeepSeek mainly intended to assist clinical decision making, such as patient diagnosis and treatment recommendation. However, only 36.2% hospital-deployed models clearly indicated a pre-deployment assessment, 22.4% presented assessment results, and 8.6% identified potential risks and countermeasures. We found poor transparency in hospital reporting, with none presenting evaluation details. Hospitals were likely to report DeepSeek’s higher performance and fewer risks. Conclusions: The irresponsible deployment of DeepSeek in Chinese leading hospitals poses potential risks to patient outcomes and safety. We highlight the urgent need that existing regulations should be expanded to the downstream developers and users and hospitals need to perform a more rigorous validation and transparent reporting.
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Listening to Patients’ Voices on the Use of AI in Health Care: Cross-Sectional Study

✇Journal of Medical Internet Research
By: Ranganathan Chandrasekaran · Lavanya Takale · Evangelos Moustakas
Background: Artificial intelligence (AI) holds great promise in transforming healthcare delivery. However, successful implementation of AI projects in healthcare depends on patients' acceptance and trust. There is only limited empirical research examining public perceptions, particularly on the use of personal health data in AI applications in healthcare. Objective: To examine public knowledge and comfort levels with AI use in healthcare, including use of personal health data with and without consent, and to assess how sociodemographic factors, digital literacy, and health conditions influence these perceptions. Methods: We analyzed data from 6,904 Canadian adults who participated in the 2023 Canadian Digital Health Survey. AI-related knowledge and comfort levels were measured using ordinal scales. Sociodemographic characteristics, digital health literacy, and self-reported chronic health conditions were included as predictors. Ordinal logistic regression models were used to assess associations between these factors and AI-related attitudes. Results: 42.3% reported moderate knowledge of AI, while only 7.8% described themselves as very knowledgeable. Overall, 44.6% were comfortable with AI use in healthcare, increasing to 64.7% when personal health data were used with consent, but decreasing when used without consent (52.6% uncomfortable). Respondents were most comfortable with AI use for epidemic tracking and workflow management, and less so for clinical tasks. Fully weighted ordinal logistic regression models indicated that men (OR=1.57, p<.001 non-citizens higher-income respondents p those with graduate education higher digital health literacy and more chronic conditions exhibited greater odds of reporting ai knowledge. for comfort use in healthcare aged men .001 or comfort. lower-income white reported lower levels. using personal data consent adults were less comfortable than showed while other racial groups without contrast black conclusions: the findings point to enhancing transparent policies ethical governance as key increasing public trust ai-driven healthcare.>
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EIF3M as a pan-cancer biomarker: prognostic significance and immune infiltration association

✇Omics in Hepatocellular
By: Zhilei Zhao · Jiaxing Chen · Yongqiang Cui · Zhihao Fu · Dongfeng Deng · Xiao Zhang

Front Mol Biosci. 2025 Nov 18;12:1697083. doi: 10.3389/fmolb.2025.1697083. eCollection 2025.

ABSTRACT

BACKGROUND: EIF3M, a core subunit of eukaryotic translation initiation factor 3, plays a pivotal role in protein synthesis by regulating the assembly of the 43S initiation complex. However, its biological functions in cancer remain poorly understood. To further investigate the clinical translational value and underlying mechanisms of EIF3M in tumors, this study conducted comprehensive bioinformatic analysis of EIF3M across various tumor types.

METHODS: We utilized publicly available databases to perform a comprehensive bioinformatics analysis of EIF3M's biological roles in oncogenesis, aiming to elucidate its pan-cancer expression patterns and prognostic significance. Furthermore, we conducted an integrative multi-omics analysis incorporating methylation profiling, co-expressed gene networks, targeted miRNA interactions, and tumor immune microenvironment infiltration to decipher the complex regulatory architecture and biological pathways mediated by EIF3M across cancer types. Finally, we used HCC cell lines for in vitro functional validation, determining how EIF3M expression modulates malignant phenotypic behaviors in hepatocellular carcinoma.

RESULTS: EIF3M was overexpressed in multiple cancers and correlated with advanced tumor stage and poor survival. Its dysregulation was primarily driven by gene amplification and regulated by promoter methylation and miRNAs. EIF3M functioned as a hub in cell cycle and transcriptional networks and was linked to an immunosuppressive microenvironment. In hepatocellular carcinoma models, EIF3M modulated tumor proliferation, migration, and activated oncogenic pathways like Wnt/β-catenin.

CONCLUSION: This study reveals that EIF3M expression correlates with immune infiltration and poor prognosis in multiple cancers. In vitro experiments in hepatocellular carcinoma models demonstrated that EIF3M critically regulates malignant cell behaviors. Collectively, our findings highlight EIF3M's value as a promising pan-cancer biomarker worthy of further investigation for its utility in prognosis prediction and as an indicator of immunotherapeutic response.

PMID:41341921 | PMC:PMC12669982 | DOI:10.3389/fmolb.2025.1697083

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Harnessing human-AI collaboration for an AI roadmap that moves beyond pilots

✇MIT Technology Review
By: MIT Technology Review Insights

The past year has marked a turning point in the corporate AI conversation. After a period of eager experimentation, organizations are now confronting a more complex reality: While investment in AI has never been higher, the path from pilot to production remains elusive. Three-quarters of enterprises remain stuck in experimentation mode, despite mounting pressure to convert early tests into operational gains.

“Most organizations can suffer from what we like to call PTSD, or process technology skills and data challenges,” says Shirley Hung, partner at Everest Group. “They have rigid, fragmented workflows that don’t adapt well to change, technology systems that don’t speak to each other, talent that is really immersed in low-value tasks rather than creating high impact. And they are buried in endless streams of information, but no unified fabric to tie it all together.”

The central challenge, then, lies in rethinking how people, processes, and technology work together.

Across industries as different as customer experience and agricultural equipment, the same pattern is emerging: Traditional organizational structures—centralized decision-making, fragmented workflows, data spread across incompatible systems—are proving too rigid to support agentic AI. To unlock value, leaders must rethink how decisions are made, how work is executed, and what humans should uniquely contribute.

“It is very important that humans continue to verify the content. And that is where you’re going to see more energy being put into,” Ryan Peterson, EVP and chief product officer at Concentrix.

Much of the conversation centered on what can be described as the next major unlock: operationalizing human-AI collaboration. Rather than positioning AI as a standalone tool or a “virtual worker,” this approach reframes AI as a system-level capability that augments human judgment, accelerates execution, and reimagines work from end to end. That shift requires organizations to map the value they want to create; design workflows that blend human oversight with AI-driven automation; and build the data, governance, and security foundations that make these systems trustworthy.

“My advice would be to expect some delays because you need to make sure you secure the data,” says Heidi Hough, VP for North America aftermarket at Valmont. “As you think about commercializing or operationalizing any piece of using AI, if you start from ground zero and have governance at the forefront, I think that will help with outcomes.”

Early adopters are already showing what this looks like in practice: starting with low-risk operational use cases, shaping data into tightly scoped enclaves, embedding governance into everyday decision-making, and empowering business leaders, not just technologists, to identify where AI can create measurable impact. The result is a new blueprint for AI maturity grounded in reengineering how modern enterprises operate.

“Optimization is really about doing existing things better, but reimagination is about discovering entirely new things that are worth doing,” says Hung.

Watch the webcast.

This webcast is produced in partnership with Concentrix.

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

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Hydrogel Formulations to Investigate Lung Cancer Mechanism

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Aysel Saskara · Yagmur Arslan · Ozlem Yesil-Celiktas

Thorac Res Pract. 2025 Dec 1;26(Suppl 1):10-11. doi: 10.4274/ThoracResPract.2025.s004.

ABSTRACT

INTRODUCTION: Lung cancer remains a leading cause of cancer-related deaths worldwide, largely due to late diagnosis and the complexity of the tumor microenvironment (TME).1 A key factor in lung cancer is the extracellular matrix (ECM), a 3D network composed of structural proteins, glycoproteins, proteoglycans, and growth factors that together regulate cell adhesion, proliferation, and signaling. ECM architecture and its changes are closely related to cancer mechanisms.2 Thus, physiological models that recapitulate ECM composition and mechanics are essential. 2D cultures fail to replicate the organization and biochemical and mechanical signals of the TME, whereas microfluidic platforms offer dynamic, 3D cell culture systems hydrogel-integrated.3 A broad range of biomaterials (synthetic, semi-synthetic, natural) is used to recapitulate the dynamics of ECM. Natural hydrogels such as collagen, gelatin methacrylate (GelMA), Matrigel, alginate, fibrin, and decellularized ECM (dECM) are widely used due to their inherent bioactivity and ability to support cell adhesion and proliferation.4 Synthetic hydrogels, such as polyethylene glycol (PEG) and polyacrylamide, provide tunable stiffness and control over matrix composition, while semi-synthetic hybrids (e.g., PEG-GelMA, GelMA-dECM) combine biological cues with structural stability (Figure 1).5.

MATERIAL AND METHODS: Cancer cell lines A549 (adenocarcinoma), H1299 (non-small cell lung cancer), and H460 (large cell carcinoma) are frequently used in cancer modelling. Co-culture systems integrate fibroblasts, endothelial cells, and immune cells (e.g., macrophages) to simulate the TME and study cell-matrix-cell interactions. Patient-derived organoids preserve tumor heterogeneity, genetic mutations, and drug response profiles, representing a personalized in vitro cancer model. Tumor spheroids embedded in hydrogels recapitulate diffusion gradients and are used to evaluate drug penetration and metastasis. Further, they can be integrated into microfluidic platforms or well plates. As a next step, it is important to investigate ECM rheology, determine mechanical structure and cytokine expression levels, and further validate cell-matrix interactions.6,7.

RESULTS: These models have shown that tumor cells embedded in hydrogels exhibit enhanced invasive behavior and increased expression of matrix metalloproteinases (enzymes responsible for ECM degradation and remodeling). From a biomechanical perspective, rheological analyses revealed that cancer-associated hydrogels typically exhibit higher storage modulus than healthy matrices, reflecting a stiffer microenvironment and increased collagen levels.8.

CONCLUSION: Collectively, recent findings underscore the central role of the ECM and hydrogel-based systems in modeling lung cancer progression. The development of tissue-specific, mechanically tunable, and microfluidic-integrated hydrogels has transformed in vitro modeling from static 2D monolayers to dynamic, physiologically relevant 3D systems. Increased stiffness is now recognized as a key regulator of cancer cell fate, governing proliferation, EMT, and metastasis through mechanotransduction pathways such as YAP/TAZ and integrin-FAK signaling.9 Despite significant progress, variability in dECM composition and crosslinking chemistry still challenges reproducibility and bioactivity. Moreover, current hydrogel-based models often lack immune cell components and vascular complexity, limiting their ability to fully emulate the native TME. Future efforts should integrate dECM-based hydrogels with organoid and microfluidic systems, supported by multi-omics profiling, to achieve patient-specific and physiologically relevant lung cancer models.

PMID:41340224 | PMC:PMC12673191 | DOI:10.4274/ThoracResPract.2025.s004

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