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Received — 28 December 2025 ⏭ Favourites

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

✇Journal of Medical Internet Research
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

A novel evaluation benchmark for medical LLMs illuminating safety and effectiveness in clinical domains

✇npj Digital Medicine
By: Shirui Wang · Zhihui Tang · Huaxia Yang · Qiuhong Gong · Tiantian Gu · Hongyang Ma · Yongxin Wang · Wubin Sun · Zeliang Lian · Kehang Mao · Yinan Jiang · Zhicheng Huang · Lingyun Ma · Wenjie Shen · Yajie Ji · Yunhui Tan · Chunbo Wang · Yunlu Gao · Qianling Ye · Rui Lin · Mingyu Chen · Lijuan Niu · Zhihao Wang · Peng Yu · Mengran Lang · Yue Liu · Huimin Zhang · Haitao Shen · Long Chen · Qiguang Zhao · Si-Xuan Liu · Lina Zhou · Hua Gao · Dongqiang Ye · Lingmin Meng · Youtao Yu · Naixin Liang · Jianxiong Wu
26 December 2025 at 08:00

npj Digital Medicine, Published online: 26 December 2025; doi:10.1038/s41746-025-02277-8

A novel evaluation benchmark for medical LLMs illuminating safety and effectiveness in clinical domains

Comparison of liquid biopsy-based technologies for cancer screening

✇MRD
By: Andreas P Papanastasiou · Miyo K Chatanaka · Eleftherios P Diamandis
27 December 2025 at 19:00

Crit Rev Clin Lab Sci. 2025 Dec 27:1-12. doi: 10.1080/10408363.2025.2606357. Online ahead of print.

ABSTRACT

Circulating plasma DNA has found important applications in diverse medical fields, including prenatal testing, transplantation, and especially cancer. Many companies have developed products for detecting minimal residual disease, selecting or monitoring therapy, assessing prognosis, and confirming diagnosis. One major application is in screening asymptomatic individuals for the presence of cancer. Screening may facilitate better clinical outcomes through earlier interventions. Collectively, these technologies are widely known as "liquid biopsies". After the extraction of free DNA from the circulation, it is analyzed by various molecular techniques to explore differences between DNA originating from normal cells and cancer cells. Circulating plasma DNA originating from tumors (ctDNA) is expected to harbor the same molecular changes as tumor tissue itself. Thus, ctDNA is considered a surrogate of cancer tissue, but without the need to perform invasive biopsies to obtain it. Many new diagnostic companies have taken advantage of this new biomarker and developed technologies for screening for one or multiple cancers. We previously estimated the amount of ctDNA in circulation, which is admixed with DNA originating from normal cells. We concluded that since only a small fraction of the whole plasma (3 liters) is used for testing (3 to 4 mL), it is possible that the retrieved ctDNA may not be enough for cancer diagnosis in all patients. This problem is more acute with small tumors. Here, we mention some companies in the "liquid biopsy" arena and analyze their clinical data to establish if their tests are close to entering the clinic. We conclude from this analysis that current data do not support the use of these technologies for population screening due to many false negative and false positive results.

PMID:41454842 | DOI:10.1080/10408363.2025.2606357

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

Received — 19 December 2025 ⏭ Favourites
  • ✇MIT Technology Review
  • Take our quiz on the year in health and biotechnology Jessica Hamzelou
    In just a couple of weeks, we’ll be bidding farewell to 2025. And what a year it has been! Artificial intelligence is being incorporated into more aspects of our lives, weight-loss drugs have expanded in scope, and there have been some real “omg” biotech stories from the fields of gene therapy, IVF, neurotech, and more.    As always, the team at MIT Technology Review has been putting together our 2026 list of breakthrough technologies. That will be published in the new year (watch this space)
     
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Take our quiz on the year in health and biotechnology

✇MIT Technology Review
By: Jessica Hamzelou
19 December 2025 at 00:59

In just a couple of weeks, we’ll be bidding farewell to 2025. And what a year it has been! Artificial intelligence is being incorporated into more aspects of our lives, weight-loss drugs have expanded in scope, and there have been some real “omg” biotech stories from the fields of gene therapy, IVF, neurotech, and more.   

As always, the team at MIT Technology Review has been putting together our 2026 list of breakthrough technologies. That will be published in the new year (watch this space). In the meantime, my colleague Antonio Regalado has compiled his traditional list of the year’s worst technologies.

I’m inviting you to put your own memory to the test. Just how closely have you been paying attention to the Checkup emails that have been landing in your inbox this year?!

This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here.

The Principle of Proportional Duty: A Knowledge-Duty Framework for Ethical Equilibrium in Human and Artificial Systems

✇cs.AI, q-bio.NC updates on arXiv.org
By: Timothy Prescher
19 December 2025 at 13:00
arXiv:2512.15740v1 Announce Type: new Abstract: Traditional ethical frameworks often struggle to model decision-making under uncertainty, treating it as a simple constraint on action. This paper introduces the Principle of Proportional Duty (PPD), a novel framework that models how ethical responsibility scales with an agent's epistemic state. The framework reveals that moral duty is not lost to uncertainty but transforms: as uncertainty increases, Action Duty (the duty to act decisively) is proportionally converted into Repair Duty (the active duty to verify, inquire, and resolve uncertainty). This dynamic is expressed by the equation D_total = K[(1-HI) + HI * g(C_signal)], where Total Duty is a function of Knowledge (K), Humility/Uncertainty (HI), and Contextual Signal Strength (C_signal). Monte Carlo simulations demonstrate that systems maintaining a baseline humility coefficient (lambda > 0) produce more stable duty allocations and reduce the risk of overconfident decision-making. By formalizing humility as a system parameter, the PPD offers a mathematically tractable approach to moral responsibility that could inform the development of auditable AI decision systems. This paper applies the framework across four domains, clinical ethics, recipient-rights law, economic governance, and artificial intelligence, to demonstrate its cross-disciplinary validity. The findings suggest that proportional duty serves as a stabilizing principle within complex systems, preventing both overreach and omission by dynamically balancing epistemic confidence against contextual risk.
  • ✇cs.AI, q-bio.NC updates on arXiv.org
  • AI Needs Physics More Than Physics Needs AI Peter Coveney · Roger Highfield
    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 signific
     
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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.

Machine learning-based predictions of healthcare contacts following emergency hospitalisation using electronic health records

✇npj Digital Medicine
By: Konstantin Georgiev · Dimitrios Doudesis · Joanne McPeake · Nicholas L. Mills · Susan D. Shenkin · Jacques D. Fleuriot · Atul Anand
17 December 2025 at 08:00

npj Digital Medicine, Published online: 17 December 2025; doi:10.1038/s41746-025-02138-4

Machine learning-based predictions of healthcare contacts following emergency hospitalisation using electronic health records

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.

Data-Chain Backdoor: Do You Trust Diffusion Models as Generative Data Supplier?

✇cs.AI, q-bio.NC updates on arXiv.org
By: Junchi Lu · Xinke Li · Yuheng Liu · Qi Alfred Chen
19 December 2025 at 13:00
arXiv:2512.15769v1 Announce Type: cross Abstract: The increasing use of generative models such as diffusion models for synthetic data augmentation has greatly reduced the cost of data collection and labeling in downstream perception tasks. However, this new data source paradigm may introduce important security concerns. This work investigates backdoor propagation in such emerging generative data supply chains, namely Data-Chain Backdoor (DCB). Specifically, we find that open-source diffusion models can become hidden carriers of backdoors. Their strong distribution-fitting ability causes them to memorize and reproduce backdoor triggers during generation, which are subsequently inherited by downstream models, resulting in severe security risks. This threat is particularly concerning under clean-label attack scenarios, as it remains effective while having negligible impact on the utility of the synthetic data. Furthermore, we discover an Early-Stage Trigger Manifestation (ESTM) phenomenon: backdoor trigger patterns tend to surface more explicitly in the early, high-noise stages of the diffusion model's reverse generation process before being subtly integrated into the final samples. Overall, this work reveals a previously underexplored threat in generative data pipelines and provides initial insights toward mitigating backdoor risks in synthetic data generation.

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

✇cs.AI, q-bio.NC updates on arXiv.org
By: Ignacio Heredia · \'Alvaro L\'opez Garc\'ia · Germ\'an Molt\'o · Amanda Calatrava · Valentin Kozlov · Alessandro Costantini · Viet Tran · Mario David · Daniel San Mart\'in · Marcin P{\l}\'ociennik · Marta Obreg\'on Ruiz · Sa\'ul Fernandez · Judith S\'ainz-Pardo D\'iaz · Miguel Caballer · Caterina Alarc\'on Mar\'in · Stefan Dlugolinsky · Martin \v{S}eleng · Lisana Berberi · Khadijeh Alibabaei · Borja Esteban Sanchis · Pedro Castro · Giacinto Donvito · Diego Aguirre · Sergio Langarita · Vicente Rodriguez · Leonhard Duda · Andr\'es Heredia Canales · Susana Rebolledo Ruiz · Jo\~ao Machado · Giang Nguyen · Fernando Aguilar G\'omez · Jaime D\'iez
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.

XTC, A Research Platform for Optimizing AI Workload Operators

✇cs.AI, q-bio.NC updates on arXiv.org
By: Pompougnac Hugo · Guillon Christophe · Noiry Sylvain · Dutilleul Alban · Iooss Guillaume · Rastello Fabrice
19 December 2025 at 13:00
arXiv:2512.16512v1 Announce Type: cross Abstract: Achieving high efficiency on AI operators demands precise control over computation and data movement. However, existing scheduling languages are locked into specific compiler ecosystems, preventing fair comparison, reuse, and evaluation across frameworks. No unified interface currently decouples scheduling specification from code generation and measurement. We introduce XTC, a platform that unifies scheduling and performance evaluation across compilers. With its common API and reproducible measurement framework, XTC enables portable experimentation and accelerates research on optimization strategies.

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

✇cs.AI, q-bio.NC updates on arXiv.org
By: Claudia Vale Oliveira (DigiMedia · University of Aveiro · Aveiro · Portugal) · Nelson Zagalo (DigiMedia · University of Aveiro · Aveiro · Portugal) · Filipe Silva (DigiMedia · University of Aveiro · Aveiro · Portugal) · Anabela Brandao (DigiMedia · University of Aveiro · Aveiro · Portugal) · Syeda Faryal Hussain Khurrum (DigiMedia · University of Aveiro · Aveiro · Portugal) · Joaquim Santos (DigiMedia · University of Aveiro · Aveiro · Portugal)
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.

Multi-Modality Collaborative Learning for Sentiment Analysis

✇cs.AI, q-bio.NC updates on arXiv.org
By: Shanmin Wang · Chengguang Liu · Qingshan Liu
19 December 2025 at 13:00
arXiv:2501.12424v2 Announce Type: replace-cross Abstract: Multimodal sentiment analysis (MSA) identifies individuals' sentiment states in videos by integrating visual, audio, and text modalities. Despite progress in existing methods, the inherent modality heterogeneity limits the effective capture of interactive sentiment features across modalities. In this paper, by introducing a Multi-Modality Collaborative Learning (MMCL) framework, we facilitate cross-modal interactions and capture enhanced and complementary features from modality-common and modality-specific representations, respectively. Specifically, we design a parameter-free decoupling module and separate uni-modality into modality-common and modality-specific components through semantics assessment of cross-modal elements. For modality-specific representations, inspired by the act-reward mechanism in reinforcement learning, we design policy models to adaptively mine complementary sentiment features under the guidance of a joint reward. For modality-common representations, intra-modal attention is employed to highlight crucial components, playing enhanced roles among modalities. Experimental results, including superiority evaluations on four databases, effectiveness verification of each module, and assessment of complementary features, demonstrate that MMCL successfully learns collaborative features across modalities and significantly improves performance. The code can be available at https://github.com/smwanghhh/MMCL.

Towards Practical Alzheimer's Disease Diagnosis: A Lightweight and Interpretable Spiking Neural Model

✇cs.AI, q-bio.NC updates on arXiv.org
By: Changwei Wu · Yifei Chen · Yuxin Du · Jinying Zong · Jie Dong · Mingxuan Liu · Feiwei Qin · Yong Peng · Jin Fan · Changmiao Wang
19 December 2025 at 13:00
arXiv:2506.09695v3 Announce Type: replace-cross Abstract: Early diagnosis of Alzheimer's Disease (AD), particularly at the mild cognitive impairment stage, is essential for timely intervention. However, this process faces significant barriers, including reliance on subjective assessments and the high cost of advanced imaging techniques. While deep learning offers automated solutions to improve diagnostic accuracy, its widespread adoption remains constrained due to high energy requirements and computational demands, particularly in resource-limited settings. Spiking neural networks (SNNs) provide a promising alternative, as their brain-inspired design is well-suited to model the sparse and event-driven patterns characteristic of neural degeneration in AD. These networks offer the potential for developing interpretable, energy-efficient diagnostic tools. Despite their advantages, existing SNNs often suffer from limited expressiveness and challenges in stable training, which reduce their effectiveness in handling complex medical tasks. To address these shortcomings, we introduce FasterSNN, a hybrid neural architecture that combines biologically inspired Leaky Integrate-and-Fire (LIF) neurons with region-adaptive convolution and multi-scale spiking attention mechanisms. This approach facilitates efficient, sparse processing of 3D MRI data while maintaining high diagnostic accuracy. Experimental results on benchmark datasets reveal that FasterSNN delivers competitive performance with significantly enhanced efficiency and training stability, highlighting its potential for practical application in AD screening. Our source code is available at https://github.com/wuchangw/FasterSNN.

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.
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