Evaluating Peer Online Forums to Support Health: Ethical and Practical Challenges

By: Fiona Lobban · Neil Caton · Zoe Glossop · Jade Haines · Gemma Hayward · Connor Heapy · Rose Johnston · Steve Jones · Chris Lodge · Karen Machin · Paul Marshall · Tamara Rakic · Paul Rayson · Heather Robinson · Elena Semino · John Vidler

25 December 2025 at 01:30

Many people use peer online forums to seek support for health-related problems. More research is needed to understand the impacts of forum use, and how these are generated. However, there are significant ethical and practical challenges with the methods available to do the required research. We examine the key challenges associated with conducting each of the most commonly used online data collection methods: surveys, interviews, forum post analysis; and triangulation of these methods. Based on our learning from the Improving Peer Online Forums (iPOF) study, an inter-disciplinary realist informed mixed methods evaluation of peer online forums, we outline strategies that can be used to address key issues pertaining to assessing important outcomes, facilitating participation, validating participants (users who consent to take part in one or more parts of the study), protecting anonymity, gaining consent, managing risk, multi-stakeholder engagement, and triangulation. We share this learning to support researchers, reviewers, and ethics committees faced with deciding how best to address these challenges. We highlight the need for ongoing open, transparent discussion to ensure the research field keeps pace with evolving technology design and societal attitudes to online data use.

Context matching is not reasoning when performing generalized clinical evaluation of generative language models

npj Digital Medicine

By: Andrew Wen · Qiuhao Lu · Yu-Neng Chuang · Guanchu Wang · Jiayi Yuan · Jiamu Zhang · Liwei Wang · Sunyang Fu · Kurt D. Miller · Heling Jia · Steven D. Bedrick · William R. Hersh · Kirk E. Roberts · Xia Hu · Hongfang Liu

27 December 2025 at 08:00

npj Digital Medicine, Published online: 27 December 2025; doi:10.1038/s41746-025-02253-2

Context matching is not reasoning when performing generalized clinical evaluation of generative language models

Cancer in a drop: Liquid biopsy highlights from the World Conference on Lung Cancer (WCLC) 2025

MRD

By: Nadia Ghazali · Ana Ortega-Franco · Diego de Miguel-Perez · Erick F Saldanha · Konstantinos Venetis · Pasquale Pisapia · Surbhi Singhal · Benjamin A Bleiberg · Carolina Reduzzi · Roberto Borea

24 December 2025 at 19:00

J Liq Biopsy. 2025 Nov 29;10:100449. doi: 10.1016/j.jlb.2025.100449. eCollection 2025 Dec.

ABSTRACT

The role of liquid biopsy in oncological care continues to expand, with multiple studies presented at the International Association for the Study of Lung Cancer (IASLC) 2025 World Conference on Lung Cancer (WCLC 2025). This review summarizes recent advances in liquid biopsy for thoracic oncology, encompassing both non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). In early detection and screening, proteomic profiling has identified potential biomarkers predictive of future lung cancer risk. The integration of proteomics with clinical and imaging data can improve pulmonary nodule malignancy prediction. In resectable NSCLC, tumour-informed whole-genome sequencing (WGS) assay demonstrated high sensitivity for minimal residual disease (MRD) detection, with MRD clearance following neoadjuvant osimertinib or chemo-immunotherapy associated with favorable outcomes. In advanced NSCLC, longitudinal liquid biopsy analyses reveal dynamic subclonal evolution driving early treatment resistance. Circulating tumor DNA (ctDNA) clearance following targeted therapy in MET exon 14 skipping and BRAF-mutated tumors was associated with improved clinical outcomes. Emerging biomarkers such as ctDNA tumour fraction and circulating microRNA signatures are promising for radiotherapy stratification and prediction of immunotherapy-related toxicities. In SCLC, MRD monitoring enables earlier detection of disease progression and supports ctDNA-guided selection of patients for consolidation immunotherapy following chemotherapy. Overall, these advances demonstrate the expanding role of liquid biopsy in improving early detection, guiding treatment, and improving disease monitoring in lung cancer.

PMID:41438843 | PMC:PMC12720026 | DOI:10.1016/j.jlb.2025.100449

The year data centers went from backend to center stage

TechCrunch

By: Lucas Ropek

25 December 2025 at 04:16

Data centers are no longer the boring tech issue they once were.

AI Needs Physics More Than Physics Needs AI

cs.AI, q-bio.NC updates on arXiv.org

By: Peter Coveney · Roger Highfield

19 December 2025 at 13:00

arXiv:2512.16344v1 Announce Type: new Abstract: Artificial intelligence (AI) is commonly depicted as transformative. Yet, after more than a decade of hype, its measurable impact remains modest outside a few high-profile scientific and commercial successes. The 2024 Nobel Prizes in Chemistry and Physics recognized AI's potential, but broader assessments indicate the impact to date is often more promotional than technical. We argue that while current AI may influence physics, physics has significantly more to offer this generation of AI. Current architectures - large language models, reasoning models, and agentic AI - can depend on trillions of meaningless parameters, suffer from distributional bias, lack uncertainty quantification, provide no mechanistic insights, and fail to capture even elementary scientific laws. We review critiques of these limits, highlight opportunities in quantum AI and analogue computing, and lay down a roadmap for the adoption of 'Big AI': a synthesis of theory-based rigour with the flexibility of machine learning.

Distributional AGI Safety

cs.AI, q-bio.NC updates on arXiv.org

By: Nenad Toma\v{s}ev · Matija Franklin · Julian Jacobs · S\'ebastien Krier · Simon Osindero

19 December 2025 at 13:00

arXiv:2512.16856v1 Announce Type: new Abstract: AI safety and alignment research has predominantly been focused on methods for safeguarding individual AI systems, resting on the assumption of an eventual emergence of a monolithic Artificial General Intelligence (AGI). The alternative AGI emergence hypothesis, where general capability levels are first manifested through coordination in groups of sub-AGI individual agents with complementary skills and affordances, has received far less attention. Here we argue that this patchwork AGI hypothesis needs to be given serious consideration, and should inform the development of corresponding safeguards and mitigations. The rapid deployment of advanced AI agents with tool-use capabilities and the ability to communicate and coordinate makes this an urgent safety consideration. We therefore propose a framework for distributional AGI safety that moves beyond evaluating and aligning individual agents. This framework centers on the design and implementation of virtual agentic sandbox economies (impermeable or semi-permeable), where agent-to-agent transactions are governed by robust market mechanisms, coupled with appropriate auditability, reputation management, and oversight to mitigate collective risks.

AI4EOSC: a Federated Cloud Platform for Artificial Intelligence in Scientific Research

19 December 2025 at 13:00

arXiv:2512.16455v1 Announce Type: cross Abstract: In this paper, we describe a federated compute platform dedicated to support Artificial Intelligence in scientific workloads. Putting the effort into reproducible deployments, it delivers consistent, transparent access to a federation of physically distributed e-Infrastructures. Through a comprehensive service catalogue, the platform is able to offer an integrated user experience covering the full Machine Learning lifecycle, including model development (with dedicated interactive development environments), training (with GPU resources, annotation tools, experiment tracking, and federated learning support) and deployment (covering a wide range of deployment options all along the Cloud Continuum). The platform also provides tools for traceability and reproducibility of AI models, integrates with different Artificial Intelligence model providers, datasets and storage resources, allowing users to interact with the broader Machine Learning ecosystem. Finally, it is easily customizable to lower the adoption barrier by external communities.

Plausibility as Failure: How LLMs and Humans Co-Construct Epistemic Error

19 December 2025 at 13:00

arXiv:2512.16750v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as epistemic partners in everyday reasoning, yet their errors remain predominantly analyzed through predictive metrics rather than through their interpretive effects on human judgment. This study examines how different forms of epistemic failure emerge, are masked, and are tolerated in human AI interaction, where failure is understood as a relational breakdown shaped by model-generated plausibility and human interpretive judgment. We conducted a three round, multi LLM evaluation using interdisciplinary tasks and progressively differentiated assessment frameworks to observe how evaluators interpret model responses across linguistic, epistemic, and credibility dimensions. Our findings show that LLM errors shift from predictive to hermeneutic forms, where linguistic fluency, structural coherence, and superficially plausible citations conceal deeper distortions of meaning. Evaluators frequently conflated criteria such as correctness, relevance, bias, groundedness, and consistency, indicating that human judgment collapses analytical distinctions into intuitive heuristics shaped by form and fluency. Across rounds, we observed a systematic verification burden and cognitive drift. As tasks became denser, evaluators increasingly relied on surface cues, allowing erroneous yet well formed answers to pass as credible. These results suggest that error is not solely a property of model behavior but a co-constructed outcome of generative plausibility and human interpretive shortcuts. Understanding AI epistemic failure therefore requires reframing evaluation as a relational interpretive process, where the boundary between system failure and human miscalibration becomes porous. The study provides implications for LLM assessment, digital literacy, and the design of trustworthy human AI communication.

Constitutional Law and AI Governance: Constraints on Model Licensing and Research Classification

cs.AI, q-bio.NC updates on arXiv.org

By: Alex Mark · Aaron Scher

19 December 2025 at 13:00

arXiv:2509.05361v2 Announce Type: replace-cross Abstract: Transformative AI systems may pose unprecedented catastrophic risks, but the U.S. Constitution places significant constraints on the government's ability to govern this technology. This paper examines how the First Amendment, administrative law, and the Fourteenth Amendment shape the legal vulnerability of two regulatory proposals: model licensing and AI research classification. While the First Amendment may provide some degree of protection for model algorithms or outputs, this protection does not foreclose regulation. Policymakers must also consider administrative legal requirements, due to both agency review and authority. Finally, while substantive due process and equal protection pose minimal obstacles, procedural due process requires the government to clearly define when developers vest a legal interest in their models. Given this analysis, effective AI governance requires careful implementation to avoid these legal challenges.

First, do NOHARM: towards clinically safe large language models

cs.AI, q-bio.NC updates on arXiv.org

By: David Wu · Fateme Nateghi Haredasht · Saloni Kumar Maharaj · Priyank Jain · Jessica Tran · Matthew Gwiazdon · Arjun Rustagi · Jenelle Jindal · Jacob M. Koshy · Vinay Kadiyala · Anup Agarwal · Bassman Tappuni · Brianna French · Sirus Jesudasen · Christopher V. Cosgriff · Rebanta Chakraborty · Jillian Caldwell · Susan Ziolkowski · David J. Iberri · Robert Diep · Rahul S. Dalal · Kira L. Newman · Kristin Galetta · J. Carl Pallais · Nancy Wei · Kathleen M. Buchheit · David I. Hong · Ernest Y. Lee · Allen Shih · Vartan Pahalyants · Tamara B. Kaplan · Vishnu Ravi · Sarita Khemani · April S. Liang · Daniel Shirvani · Advait Patil · Nicholas Marshall · Kanav Chopra · Joel Koh · Adi Badhwar · Liam G. McCoy · David J. H. Wu · Yingjie Weng · Sumant Ranji · Kevin Schulman · Nigam H. Shah · Jason Hom · Arnold Milstein · Adam Rodman · Jonathan H. Chen · Ethan Goh

19 December 2025 at 13:00

arXiv:2512.01241v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are routinely used by physicians and patients for medical advice, yet their clinical safety profiles remain poorly characterized. We present NOHARM (Numerous Options Harm Assessment for Risk in Medicine), a benchmark using 100 real primary care-to-specialist consultation cases to measure frequency and severity of harm from LLM-generated medical recommendations. NOHARM covers 10 specialties, with 12,747 expert annotations for 4,249 clinical management options. Across 31 LLMs, potential for severe harm from LLM recommendations occurs in up to 22.2% (95% CI 21.6-22.8%) of cases, with harm of omission accounting for 76.6% (95% CI 76.4-76.8%) of errors. Safety performance is only moderately correlated (r = 0.61-0.64) with existing AI and medical knowledge benchmarks. The best models outperform generalist physicians on safety (mean difference 9.7%, 95% CI 7.0-12.5%), and a diverse multi-agent approach improves safety compared to solo models (mean difference 8.0%, 95% CI 4.0-12.1%). Therefore, despite strong performance on existing evaluations, widely used AI models can produce severely harmful medical advice at nontrivial rates, underscoring clinical safety as a distinct performance dimension necessitating explicit measurement.

DrugRAG: Enhancing Pharmacy LLM Performance Through A Novel Retrieval-Augmented Generation Pipeline

cs.AI, q-bio.NC updates on arXiv.org

By: Houman Kazemzadeh · Kiarash Mokhtari Dizaji · Seyed Reza Tavakoli · Farbod Davoodi · MohammadReza KarimiNejad · Parham Abed Azad · Ali Sabzi · Armin Khosravi · Siavash Ahmadi · Mohammad Hossein Rohban · Glolamali Aminian · Tahereh Javaheri

18 December 2025 at 13:00

arXiv:2512.14896v1 Announce Type: cross Abstract: Objectives: To evaluate large language model (LLM) performance on pharmacy licensure-style question-answering (QA) tasks and develop an external knowledge integration method to improve their accuracy. Methods: We benchmarked eleven existing LLMs with varying parameter sizes (8 billion to 70+ billion) using a 141-question pharmacy dataset. We measured baseline accuracy for each model without modification. We then developed a three-step retrieval-augmented generation (RAG) pipeline, DrugRAG, that retrieves structured drug knowledge from validated sources and augments model prompts with evidence-based context. This pipeline operates externally to the models, requiring no changes to model architecture or parameters. Results: Baseline accuracy ranged from 46% to 92%, with GPT-5 (92%) and o3 (89%) achieving the highest scores. Models with fewer than 8 billion parameters scored below 50%. DrugRAG improved accuracy across all tested models, with gains ranging from 7 to 21 percentage points (e.g., Gemma 3 27B: 61% to 71%, Llama 3.1 8B: 46% to 67%) on the 141-item benchmark. Conclusion: We demonstrate that external structured drug knowledge integration through DrugRAG measurably improves LLM accuracy on pharmacy tasks without modifying the underlying models. This approach provides a practical pipeline for enhancing pharmacy-focused AI applications with evidence-based information.

Leveraging LLMs for Structured Data Extraction from Unstructured Patient Records

cs.AI, q-bio.NC updates on arXiv.org

By: Mitchell A. Klusty · Elizabeth C. Solie · Caroline N. Leach · W. Vaiden Logan · Lynnet E. Richey · John C. Gensel · David P. Szczykutowicz · Bryan C. McLellan · Emily B. Collier · Samuel E. Armstrong · V. K. Cody Bumgardner

17 December 2025 at 13:00

arXiv:2512.13700v1 Announce Type: new Abstract: Manual chart review remains an extremely time-consuming and resource-intensive component of clinical research, requiring experts to extract often complex information from unstructured electronic health record (EHR) narratives. We present a secure, modular framework for automated structured feature extraction from clinical notes leveraging locally deployed large language models (LLMs) on institutionally approved, Health Insurance Portability and Accountability Act (HIPPA)-compliant compute infrastructure. This system integrates retrieval augmented generation (RAG) and structured response methods of LLMs into a widely deployable and scalable container to provide feature extraction for diverse clinical domains. In evaluation, the framework achieved high accuracy across multiple medical characteristics present in large bodies of patient notes when compared against an expert-annotated dataset and identified several annotation errors missed in manual review. This framework demonstrates the potential of LLM systems to reduce the burden of manual chart review through automated extraction and increase consistency in data capture, accelerating clinical research.

Enhancing Transparency and Traceability in Healthcare AI: The AI Product Passport

cs.AI, q-bio.NC updates on arXiv.org

By: A. Anil Sinaci · Senan Postaci · Dogukan Cavdaroglu · Machteld J. Boonstra · Okan Mercan · Kerem Yilmaz · Gokce B. Laleci Erturkmen · Folkert W. Asselbergs · Karim Lekadir

17 December 2025 at 13:00

arXiv:2512.13702v1 Announce Type: cross Abstract: Objective: To develop the AI Product Passport, a standards-based framework improving transparency, traceability, and compliance in healthcare AI via lifecycle-based documentation. Materials and Methods: The AI Product Passport was developed within the AI4HF project, focusing on heart failure AI tools. We analyzed regulatory frameworks (EU AI Act, FDA guidelines) and existing standards to design a relational data model capturing metadata across AI lifecycle phases: study definition, dataset preparation, model generation/evaluation, deployment/monitoring, and passport generation. MLOps/ModelOps concepts were integrated for operational relevance. Co-creation involved feedback from AI4HF consortium and a Lisbon workshop with 21 diverse stakeholders, evaluated via Mentimeter polls. The open-source platform was implemented with Python libraries for automated provenance tracking. Results: The AI Product Passport was designed based on existing standards and methods with well-defined lifecycle management and role-based access. Its implementation is a web-based platform with a relational data model supporting auditable documentation. It generates machine- and human-readable reports, customizable for stakeholders. It aligns with FUTURE-AI principles (Fairness, Universality, Traceability, Usability, Robustness, Explainability), ensuring fairness, traceability, and usability. Exported passports detail model purpose, data provenance, performance, and deployment context. GitHub-hosted backend/frontend codebases enhance accessibility. Discussion and Conclusion: The AI Product Passport addresses transparency gaps in healthcare AI, meeting regulatory and ethical demands. Its open-source nature and alignment with standards foster trust and adaptability. Future enhancements include FAIR data principles and FHIR integration for improved interoperability, promoting responsible AI deployment.

Graph AI generates neurological hypotheses validated in molecular, organoid, and clinical systems

cs.AI, q-bio.NC updates on arXiv.org

By: Ayush Noori · Joaqu\'in Polonuer · Katharina Meyer · Bogdan Budnik · Shad Morton · Xinyuan Wang · Sumaiya Nazeen · Yingnan He · I\~naki Arango · Lucas Vittor · Matthew Woodworth · Richard C. Krolewski · Michelle M. Li · Ninning Liu · Tushar Kamath · Evan Macosko · Dylan Ritter · Jalwa Afroz · Alexander B. H. Henderson · Lorenz Studer · Samuel G. Rodriques · Andrew White · Noa Dagan · David A. Clifton · George M. Church · Sudeshna Das · Jenny M. Tam · Vikram Khurana · Marinka Zitnik

17 December 2025 at 13:00

arXiv:2512.13724v1 Announce Type: cross Abstract: Neurological diseases are the leading global cause of disability, yet most lack disease-modifying treatments. We present PROTON, a heterogeneous graph transformer that generates testable hypotheses across molecular, organoid, and clinical systems. To evaluate PROTON, we apply it to Parkinson's disease (PD), bipolar disorder (BD), and Alzheimer's disease (AD). In PD, PROTON linked genetic risk loci to genes essential for dopaminergic neuron survival and predicted pesticides toxic to patient-derived neurons, including the insecticide endosulfan, which ranked within the top 1.29% of predictions. In silico screens performed by PROTON reproduced six genome-wide $\alpha$-synuclein experiments, including a split-ubiquitin yeast two-hybrid system (normalized enrichment score [NES] = 2.30, FDR-adjusted $p

TF-MCL: Time-frequency Fusion and Multi-domain Cross-Loss for Self-supervised Depression Detection

cs.AI, q-bio.NC updates on arXiv.org

By: Li-Xuan Zhao · Chen-Yang Xu · Wen-Qiang Li · Bo Wang · Rong-Xing Wei · Qing-Hao Menga

17 December 2025 at 13:00

arXiv:2512.13736v1 Announce Type: cross Abstract: In recent years, there has been a notable increase in the use of supervised detection methods of major depressive disorder (MDD) based on electroencephalogram (EEG) signals. However, the process of labeling MDD remains challenging. As a self-supervised learning method, contrastive learning could address the shortcomings of supervised learning methods, which are unduly reliant on labels in the context of MDD detection. However, existing contrastive learning methods are not specifically designed to characterize the time-frequency distribution of EEG signals, and their capacity to acquire low-semantic data representations is still inadequate for MDD detection tasks. To address the problem of contrastive learning method, we propose a time-frequency fusion and multi-domain cross-loss (TF-MCL) model for MDD detection. TF-MCL generates time-frequency hybrid representations through the use of a fusion mapping head (FMH), which efficiently remaps time-frequency domain information to the fusion domain, and thus can effectively enhance the model's capacity to synthesize time-frequency information. Moreover, by optimizing the multi-domain cross-loss function, the distribution of the representations in the time-frequency domain and the fusion domain is reconstructed, thereby improving the model's capacity to acquire fusion representations. We evaluated the performance of our model on the publicly available datasets MODMA and PRED+CT and show a significant improvement in accuracy, outperforming the existing state-of-the-art (SOTA) method by 5.87% and 9.96%, respectively.

A data-physics hybrid generative model for patient-specific post-stroke motor rehabilitation using wearable sensor data

cs.AI, q-bio.NC updates on arXiv.org

By: Yanning Dai · Chenyu Tang · Ruizhi Zhang · Wenyu Yang · Yilan Zhang · Yuhui Wang · Junliang Chen · Xuhang Chen · Ruimou Xie · Yangyue Cao · Qiaoying Li · Jin Cao · Tao Li · Hubin Zhao · Yu Pan · Arokia Nathan · Xin Gao · Peter Smielewski · Shuo Gao

17 December 2025 at 13:00

arXiv:2512.14329v1 Announce Type: cross Abstract: Dynamic prediction of locomotor capacity after stroke is crucial for tailoring rehabilitation, yet current assessments provide only static impairment scores and do not indicate whether patients can safely perform specific tasks such as slope walking or stair climbing. Here, we develop a data-physics hybrid generative framework that reconstructs an individual stroke survivor's neuromuscular control from a single 20 m level-ground walking trial and predicts task-conditioned locomotion across rehabilitation scenarios. The system combines wearable-sensor kinematics, a proportional-derivative physics controller, a population Healthy Motion Atlas, and goal-conditioned deep reinforcement learning with behaviour cloning and generative adversarial imitation learning to generate physically plausible, patient-specific gait simulations for slopes and stairs. In 11 stroke survivors, the personalized controllers preserved idiosyncratic gait patterns while improving joint-angle and endpoint fidelity by 4.73% and 12.10%, respectively, and reducing training time to 25.56% relative to a physics-only baseline. In a multicentre pilot involving 21 inpatients, clinicians who used our locomotion predictions to guide task selection and difficulty obtained larger gains in Fugl-Meyer lower-extremity scores over 28 days of standard rehabilitation than control clinicians (mean change 6.0 versus 3.7 points). These findings indicate that our generative, task-predictive framework can augment clinical decision-making in post-stroke gait rehabilitation and provide a template for dynamically personalized motor recovery strategies.

Automated Multitier Tagging of Chinese Online Health Education Resources Using a Large Language Model: Development and Validation Study

Journal of Medical Internet Research

By: Jialin Meng · Ruiming Dai · Xiaolan Huang · Yi Gu · Shixing Yan · Xiaoke Wang · Jingrong Gao · Tian-Tian Zhang

18 December 2025 at 06:00

Background: Precision health promotion, which aims to tailor health messages to individual needs, is hampered by the lack of structured metadata in vast digital health resource libraries. This bottleneck prevents scalable, personalized content delivery and exacerbates information overload for the public. Objective: This study aimed to develop, deploy, and validate an automated tagging system using a large language model (LLM) to create the foundational metadata infrastructure required for tailored health communication at scale. Methods: We developed a comprehensive, 3-tier health promotion taxonomy (10 primary, 34 secondary, and 90,562 tertiary tags) using a hybrid Delphi and corpus-mining methodology. We then constructed a hybrid inference pipeline by fine-tuning a Baichuan2-7B LLM with low-rank adaptation for initial tag generation. This was then refined by a domain-specific named entity recognition model and standardized against a vector database. The system’s performance was evaluated against manual annotations from nonexpert staff on a test set of 1000 resources. We used a “no gold standard” framework, comparing the artificial intelligence–human (A-H) interrater reliability (IRR) with a supplemental human-human (H-H) IRR baseline and expert adjudication for cases where artificial intelligence provided additional tags (“AI Additive”). Results: The A-H agreement was moderate (Cohen κ=0.54, 95% CI 0.53-0.56; Jaccard similarity coefficient=0.48, 95% CI 0.46-0.50). Critically, this was higher than the baseline nonexpert H-H agreement (Cohen κ=0.32, 95% CI 0.29-0.35; Jaccard similarity coefficient=0.35, 95% CI 0.27-0.43). A granular analysis of disagreements revealed that in 15.9% (159/1000) of the cases, the “AI Additive” tags were not identified by human annotators. Expert adjudication of these cases confirmed that the “AI Additive” tags were correct and relevant with a precision of 90% (45/50; 95% CI 78.2%-96.7%). Conclusions: A fine-tuned LLM, integrated into a hybrid pipeline, can function as a powerful augmentation tool for health content annotation. The system’s consistency (A-H κ=0.54) was found to be superior to the baseline human workflow (H-H κ=0.32). By moving beyond simple automation to reliably identify relevant health topics missed by manual annotators with high, expert-validated accuracy, this study provides a robust technical and methodological blueprint for implementing artificial intelligence to enhance precision health communication in public health settings.

Single-cell and spatial transcriptomic characterization of pulmonary pleomorphic carcinoma

Omics In Lung

By: Atsushi Matsuoka · Kazuhiko Shien · Shuta Tomida · Masayoshi Ohki · Kazuya Hisamatsu · Ryota Fujiwara · Kosei Ishimura · Ryunosuke Fujii · Tomoaki Higashihara · Naohiro Hayashi · Kazuhiro Okada · Ryo Yoshichika · Fumiaki Mukohara · Mao Yoshikawa · Yuma Fukumoto · Ken Suzawa · Yasuaki Tomioka · Shin Tanaka · Kentaroh Miyoshi · Mikio Okazaki · Seiichiro Sugimoto · Yusuke Otani · Atsushi Tanaka · Hirofumi Inoue · Yosuke Togashi · Hidetaka Yamamoto · Daisuke Ennishi · Shinichi Toyooka

16 December 2025 at 19:00

Commun Biol. 2025 Dec 16;8(1):1773. doi: 10.1038/s42003-025-09162-w.

ABSTRACT

Pulmonary pleomorphic carcinoma (PPC) is a rare subtype of lung cancer that comprises both epithelial and sarcomatoid components. The molecular basis of PPC, including the cellular dynamics of its components, remains largely unknown. To elucidate potential therapeutic targets for PPC, we perform a multi-omics analysis incorporating digital spatial profiling and single-cell RNA sequencing (scRNA-seq). PPC exhibits diverse driver gene alterations, including MET exon 14 skipping mutation (METex14) and ALK fusion. In spatial transcriptomics, MET gene and protein are overexpressed exclusively within the epithelial component and not in the sarcomatoid component, even in patients harboring METex14. Epithelial-mesenchymal transition (EMT)-related transcriptional changes, along with extracellular matrix (ECM) remodeling between the epithelial and sarcomatoid components, are observed. scRNA-seq identifies cell populations within the epithelial component that contribute to the malignant transformation and differentiation of the sarcomatoid component. They are characterized by an intermediate EMT state with ECM remodeling signature, suggesting their potential as novel therapeutic targets for PPC.

PMID:41402584 | PMC:PMC12708732 | DOI:10.1038/s42003-025-09162-w

Totalitarian Technics: The Hidden Cost of AI Scribes in Healthcare

cs.AI, q-bio.NC updates on arXiv.org

By: Hugh Brosnahan

16 December 2025 at 13:00

arXiv:2512.11814v1 Announce Type: cross Abstract: Artificial intelligence (AI) scribes, systems that record and summarise patient-clinician interactions, are promoted as solutions to administrative overload. This paper argues that their significance lies not in efficiency gains but in how they reshape medical attention itself. Offering a conceptual analysis, it situates AI scribes within a broader philosophical lineage concerned with the externalisation of human thought and skill. Drawing on Iain McGilchrist's hemisphere theory and Lewis Mumford's philosophy of technics, the paper examines how technology embodies and amplifies a particular mode of attention. AI scribes, it contends, exemplify the dominance of a left-hemispheric, calculative mindset that privileges the measurable and procedural over the intuitive and relational. As this mode of attention becomes further embedded in medical practice, it risks narrowing the field of care, eroding clinical expertise, and reducing physicians to operators within an increasingly mechanised system.

Explainable AI as a Double-Edged Sword in Dermatology: The Impact on Clinicians versus The Public

cs.AI, q-bio.NC updates on arXiv.org

By: Xuhai Xu · Haoyu Hu · Haoran Zhang · Will Ke Wang · Reina Wang · Luis R. Soenksen · Omar Badri · Sheharbano Jafry · Elise Burger · Lotanna Nwandu · Apoorva Mehta · Erik P. Duhaime · Asif Qasim · Hause Lin · Janis Pereira · Jonathan Hershon · Paulius Mui · Alejandro A. Gru · No\'emie Elhadad · Lena Mamykina · Matthew Groh · Philipp Tschandl · Roxana Daneshjou · Marzyeh Ghassemi

16 December 2025 at 13:00

arXiv:2512.12500v1 Announce Type: cross Abstract: Artificial intelligence (AI) is increasingly permeating healthcare, from physician assistants to consumer applications. Since AI algorithm's opacity challenges human interaction, explainable AI (XAI) addresses this by providing AI decision-making insight, but evidence suggests XAI can paradoxically induce over-reliance or bias. We present results from two large-scale experiments (623 lay people; 153 primary care physicians, PCPs) combining a fairness-based diagnosis AI model and different XAI explanations to examine how XAI assistance, particularly multimodal large language models (LLMs), influences diagnostic performance. AI assistance balanced across skin tones improved accuracy and reduced diagnostic disparities. However, LLM explanations yielded divergent effects: lay users showed higher automation bias - accuracy boosted when AI was correct, reduced when AI erred - while experienced PCPs remained resilient, benefiting irrespective of AI accuracy. Presenting AI suggestions first also led to worse outcomes when the AI was incorrect for both groups. These findings highlight XAI's varying impact based on expertise and timing, underscoring LLMs as a "double-edged sword" in medical AI and informing future human-AI collaborative system design.

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