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SciEvalKit: An Open-source Evaluation Toolkit for Scientific General Intelligence

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yiheng Wang · Yixin Chen · Shuo Li · Yifan Zhou · Bo Liu · Hengjian Gao · Jiakang Yuan · Jia Bu · Wanghan Xu · Yuhao Zhou · Xiangyu Zhao · Zhiwang Zhou · Fengxiang Wang · Haodong Duan · Songyang Zhang · Jun Yao · Han Deng · Yizhou Wang · Jiabei Xiao · Jiaqi Liu · Encheng Su · Yujie Liu · Weida Wang · Junchi Yao · Shenghe Zheng · Haoran Sun · Runmin Ma · Xiangchao Yan · Bo Zhang · Dongzhan Zhou · Shufei Zhang · Peng Ye · Xiaosong Wang · Shixiang Tang · Wenlong Zhang · Lei Bai
arXiv:2512.22334v1 Announce Type: new Abstract: We introduce SciEvalKit, a unified benchmarking toolkit designed to evaluate AI models for science across a broad range of scientific disciplines and task capabilities. Unlike general-purpose evaluation platforms, SciEvalKit focuses on the core competencies of scientific intelligence, including Scientific Multimodal Perception, Scientific Multimodal Reasoning, Scientific Multimodal Understanding, Scientific Symbolic Reasoning, Scientific Code Generation, Science Hypothesis Generation and Scientific Knowledge Understanding. It supports six major scientific domains, spanning from physics and chemistry to astronomy and materials science. SciEvalKit builds a foundation of expert-grade scientific benchmarks, curated from real-world, domain-specific datasets, ensuring that tasks reflect authentic scientific challenges. The toolkit features a flexible, extensible evaluation pipeline that enables batch evaluation across models and datasets, supports custom model and dataset integration, and provides transparent, reproducible, and comparable results. By bridging capability-based evaluation and disciplinary diversity, SciEvalKit offers a standardized yet customizable infrastructure to benchmark the next generation of scientific foundation models and intelligent agents. The toolkit is open-sourced and actively maintained to foster community-driven development and progress in AI4Science.
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DarkPatterns-LLM: A Multi-Layer Benchmark for Detecting Manipulative and Harmful AI Behavior

✇cs.AI, q-bio.NC updates on arXiv.org
By: Sadia Asif · Israel Antonio Rosales Laguan · Haris Khan · Shumaila Asif · Muneeb Asif
arXiv:2512.22470v1 Announce Type: new Abstract: The proliferation of Large Language Models (LLMs) has intensified concerns about manipulative or deceptive behaviors that can undermine user autonomy, trust, and well-being. Existing safety benchmarks predominantly rely on coarse binary labels and fail to capture the nuanced psychological and social mechanisms constituting manipulation. We introduce \textbf{DarkPatterns-LLM}, a comprehensive benchmark dataset and diagnostic framework for fine-grained assessment of manipulative content in LLM outputs across seven harm categories: Legal/Power, Psychological, Emotional, Physical, Autonomy, Economic, and Societal Harm. Our framework implements a four-layer analytical pipeline comprising Multi-Granular Detection (MGD), Multi-Scale Intent Analysis (MSIAN), Threat Harmonization Protocol (THP), and Deep Contextual Risk Alignment (DCRA). The dataset contains 401 meticulously curated examples with instruction-response pairs and expert annotations. Through evaluation of state-of-the-art models including GPT-4, Claude 3.5, and LLaMA-3-70B, we observe significant performance disparities (65.2\%--89.7\%) and consistent weaknesses in detecting autonomy-undermining patterns. DarkPatterns-LLM establishes the first standardized, multi-dimensional benchmark for manipulation detection in LLMs, offering actionable diagnostics toward more trustworthy AI systems.
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Lessons from Neuroscience for AI: How integrating Actions, Compositional Structure and Episodic Memory could enable Safe, Interpretable and Human-Like AI

✇cs.AI, q-bio.NC updates on arXiv.org
By: Rajesh P. N. Rao · Vishwas Sathish · Linxing Preston Jiang · Matthew Bryan · Prashant Rangarajan
arXiv:2512.22568v1 Announce Type: new Abstract: The phenomenal advances in large language models (LLMs) and other foundation models over the past few years have been based on optimizing large-scale transformer models on the surprisingly simple objective of minimizing next-token prediction loss, a form of predictive coding that is also the backbone of an increasingly popular model of brain function in neuroscience and cognitive science. However, current foundation models ignore three other important components of state-of-the-art predictive coding models: tight integration of actions with generative models, hierarchical compositional structure, and episodic memory. We propose that to achieve safe, interpretable, energy-efficient, and human-like AI, foundation models should integrate actions, at multiple scales of abstraction, with a compositional generative architecture and episodic memory. We present recent evidence from neuroscience and cognitive science on the importance of each of these components. We describe how the addition of these missing components to foundation models could help address some of their current deficiencies: hallucinations and superficial understanding of concepts due to lack of grounding, a missing sense of agency/responsibility due to lack of control, threats to safety and trustworthiness due to lack of interpretability, and energy inefficiency. We compare our proposal to current trends, such as adding chain-of-thought (CoT) reasoning and retrieval-augmented generation (RAG) to foundation models, and discuss new ways of augmenting these models with brain-inspired components. We conclude by arguing that a rekindling of the historically fruitful exchange of ideas between brain science and AI will help pave the way towards safe and interpretable human-centered AI.
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Why AI Safety Requires Uncertainty, Incomplete Preferences, and Non-Archimedean Utilities

✇cs.AI, q-bio.NC updates on arXiv.org
By: Alessio Benavoli · Alessandro Facchini · Marco Zaffalon
arXiv:2512.23508v1 Announce Type: new Abstract: How can we ensure that AI systems are aligned with human values and remain safe? We can study this problem through the frameworks of the AI assistance and the AI shutdown games. The AI assistance problem concerns designing an AI agent that helps a human to maximise their utility function(s). However, only the human knows these function(s); the AI assistant must learn them. The shutdown problem instead concerns designing AI agents that: shut down when a shutdown button is pressed; neither try to prevent nor cause the pressing of the shutdown button; and otherwise accomplish their task competently. In this paper, we show that addressing these challenges requires AI agents that can reason under uncertainty and handle both incomplete and non-Archimedean preferences.
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Enhancing Medical Data Analysis through AI-Enhanced Locally Linear Embedding: Applications in Medical Point Location and Imagery

✇cs.AI, q-bio.NC updates on arXiv.org
By: Hassan Khalid · Muhammad Mahad Khaliq · Muhammad Jawad Bashir
arXiv:2512.22182v1 Announce Type: cross Abstract: The rapid evolution of Artificial intelligence in healthcare has opened avenues for enhancing various processes, including medical billing and transcription. This paper introduces an innovative approach by integrating AI with Locally Linear Embedding (LLE) to revolutionize the handling of high-dimensional medical data. This AI-enhanced LLE model is specifically tailored to improve the accuracy and efficiency of medical billing systems and transcription services. By automating these processes, the model aims to reduce human error and streamline operations, thereby facilitating faster and more accurate patient care documentation and financial transactions. This paper provides a comprehensive mathematical model of AI-enhanced LLE, demonstrating its application in real-world healthcare scenarios through a series of experiments. The results indicate a significant improvement in data processing accuracy and operational efficiency. This study not only underscores the potential of AI-enhanced LLE in medical data analysis but also sets a foundation for future research into broader healthcare applications.
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When Algorithms Manage Humans: A Double Machine Learning Approach to Estimating Nonlinear Effects of Algorithmic Control on Gig Worker Performance and Wellbeing

✇cs.AI, q-bio.NC updates on arXiv.org
By: Arunkumar V · Nivethitha S · Sharan Srinivas · Gangadharan G. R
arXiv:2512.22290v1 Announce Type: cross Abstract: A central question for the future of work is whether person centered management can survive when algorithms take on managerial roles. Standard tools often miss what is happening because worker responses to algorithmic systems are rarely linear. We use a Double Machine Learning framework to estimate a moderated mediation model without imposing restrictive functional forms. Using survey data from 464 gig workers, we find a clear nonmonotonic pattern. Supportive HR practices improve worker wellbeing, but their link to performance weakens in a murky middle where algorithmic oversight is present yet hard to interpret. The relationship strengthens again when oversight is transparent and explainable. These results show why simple linear specifications can miss the pattern and sometimes suggest the opposite conclusion. For platform design, the message is practical: control that is only partly defined creates confusion, but clear rules and credible recourse can make strong oversight workable. Methodologically, the paper shows how Double Machine Learning can be used to estimate conditional indirect effects in organizational research without forcing the data into a linear shape.
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AI-Generated Code Is Not Reproducible (Yet): An Empirical Study of Dependency Gaps in LLM-Based Coding Agents

✇cs.AI, q-bio.NC updates on arXiv.org
By: Bhanu Prakash Vangala · Ali Adibifar · Tanu Malik · Ashish Gehani
arXiv:2512.22387v1 Announce Type: cross Abstract: The rise of Large Language Models (LLMs) as coding agents promises to accelerate software development, but their impact on generated code reproducibility remains largely unexplored. This paper presents an empirical study investigating whether LLM-generated code can be executed successfully in a clean environment with only OS packages and using only the dependencies that the model specifies. We evaluate three state-of-the-art LLM coding agents (Claude Code, OpenAI Codex, and Gemini) across 300 projects generated from 100 standardized prompts in Python, JavaScript, and Java. We introduce a three-layer dependency framework (distinguishing between claimed, working, and runtime dependencies) to quantify execution reproducibility. Our results show that only 68.3% of projects execute out-of-the-box, with substantial variation across languages (Python 89.2%, Java 44.0%). We also find a 13.5 times average expansion from declared to actual runtime dependencies, revealing significant hidden dependencies.
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The body is not there to compute: Comment on "Informational embodiment: Computational role of information structure in codes and robots" by Pitti et al

✇cs.AI, q-bio.NC updates on arXiv.org
By: Matej Hoffmann
arXiv:2512.22868v1 Announce Type: cross Abstract: Applying the lens of computation and information has been instrumental in driving the technological progress of our civilization as well as in empowering our understanding of the world around us. The digital computer was and for many still is the leading metaphor for how our mind operates. Information theory (IT) has also been important in our understanding of how nervous systems encode and process information. The target article deploys information and computation to bodies: to understand why they have evolved in particular ways (animal bodies) and to design optimal bodies (robots). In this commentary, I argue that the main role of bodies is not to compute.
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Viability and Performance of a Private LLM Server for SMBs: A Benchmark Analysis of Qwen3-30B on Consumer-Grade Hardware

✇cs.AI, q-bio.NC updates on arXiv.org
By: Alex Khalil · Guillaume Heilles · Maria Parraga · Simon Heilles
arXiv:2512.23029v1 Announce Type: cross Abstract: The proliferation of Large Language Models (LLMs) has been accompanied by a reliance on cloud-based, proprietary systems, raising significant concerns regarding data privacy, operational sovereignty, and escalating costs. This paper investigates the feasibility of deploying a high-performance, private LLM inference server at a cost accessible to Small and Medium Businesses (SMBs). We present a comprehensive benchmarking analysis of a locally hosted, quantized 30-billion parameter Mixture-of-Experts (MoE) model based on Qwen3, running on a consumer-grade server equipped with a next-generation NVIDIA GPU. Unlike cloud-based offerings, which are expensive and complex to integrate, our approach provides an affordable and private solution for SMBs. We evaluate two dimensions: the model's intrinsic capabilities and the server's performance under load. Model performance is benchmarked against academic and industry standards to quantify reasoning and knowledge relative to cloud services. Concurrently, we measure server efficiency through latency, tokens per second, and time to first token, analyzing scalability under increasing concurrent users. Our findings demonstrate that a carefully configured on-premises setup with emerging consumer hardware and a quantized open-source model can achieve performance comparable to cloud-based services, offering SMBs a viable pathway to deploy powerful LLMs without prohibitive costs or privacy compromises.
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Multi-agent Self-triage System with Medical Flowcharts

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yujia Liu · Sophia Yu · Hongyue Jin · Jessica Wen · Alexander Qian · Terrence Lee · Mattheus Ramsis · Gi Won Choi · Lianhui Qin · Xin Liu · Edward J. Wang
arXiv:2511.12439v2 Announce Type: replace Abstract: Online health resources and large language models (LLMs) are increasingly used as a first point of contact for medical decision-making, yet their reliability in healthcare remains limited by low accuracy, lack of transparency, and susceptibility to unverified information. We introduce a proof-of-concept conversational self-triage system that guides LLMs with 100 clinically validated flowcharts from the American Medical Association, providing a structured and auditable framework for patient decision support. The system leverages a multi-agent framework consisting of a retrieval agent, a decision agent, and a chat agent to identify the most relevant flowchart, interpret patient responses, and deliver personalized, patient-friendly recommendations, respectively. Performance was evaluated at scale using synthetic datasets of simulated conversations. The system achieved 95.29% top-3 accuracy in flowchart retrieval (N=2,000) and 99.10% accuracy in flowchart navigation across varied conversational styles and conditions (N=37,200). By combining the flexibility of free-text interaction with the rigor of standardized clinical protocols, this approach demonstrates the feasibility of transparent, accurate, and generalizable AI-assisted self-triage, with potential to support informed patient decision-making while improving healthcare resource utilization.
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Taming Data Challenges in ML-based Security Tasks: Lessons from Integrating Generative AI

✇cs.AI, q-bio.NC updates on arXiv.org
By: Shravya Kanchi · Neal Mangaokar · Aravind Cheruvu · Sifat Muhammad Abdullah · Shirin Nilizadeh · Atul Prakash · Bimal Viswanath
arXiv:2507.06092v3 Announce Type: replace-cross Abstract: Machine learning-based supervised classifiers are widely used for security tasks, and their improvement has been largely focused on algorithmic advancements. We argue that data challenges that negatively impact the performance of these classifiers have received limited attention. We address the following research question: Can developments in Generative AI (GenAI) address these data challenges and improve classifier performance? We propose augmenting training datasets with synthetic data generated using GenAI techniques to improve classifier generalization. We evaluate this approach across 7 diverse security tasks using 6 state-of-the-art GenAI methods and introduce a novel GenAI scheme called Nimai that enables highly controlled data synthesis. We find that GenAI techniques can significantly improve the performance of security classifiers, achieving improvements of up to 32.6% even in severely data-constrained settings (only ~180 training samples). Furthermore, we demonstrate that GenAI can facilitate rapid adaptation to concept drift post-deployment, requiring minimal labeling in the adjustment process. Despite successes, our study finds that some GenAI schemes struggle to initialize (train and produce data) on certain security tasks. We also identify characteristics of specific tasks, such as noisy labels, overlapping class distributions, and sparse feature vectors, which hinder performance boost using GenAI. We believe that our study will drive the development of future GenAI tools designed for security tasks.
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Generating Verifiable Chain of Thoughts from Exection-Traces

✇cs.AI, q-bio.NC updates on arXiv.org
By: Shailja Thakur · Vaibhav Saxena · Rohan Kulkarni · Shivdeep Singh · Parameswaran Selvam · Hima Patel · Hiroshi Kanayama
arXiv:2512.00127v2 Announce Type: replace-cross Abstract: Teaching language models to reason about code execution remains a fundamental challenge. While Chain-of-Thought (CoT) prompting has shown promise, current synthetic training data suffers from a critical weakness: the reasoning steps are often plausible-sounding explanations generated by teacher models, not verifiable accounts of what the code actually does. This creates a troubling failure mode where models learn to mimic superficially convincing but logically flawed reasoning patterns. We address this by grounding CoT generation directly in program execution traces. Our pipeline instruments code to capture its dynamic behavior, then narrates these execution traces into natural language and factually-grounded rationales that are verifiable by design. This execution-grounded approach ensures every reasoning step reflects what the program computes, eliminating logical hallucinations at the source. We evaluate our method on code reasoning tasks, code generation and explanation tasks from HumanEval. Models trained on our bi-directional trace-grounded data achieve substantial improvements on reasoning tasks, with gains of up to 30 points on output prediction and 28 points on input prediction over base models, alongside competitive explanation and code generation performance. https://github.ibm.com/IBM-Research-AI/Verified-Code-CoT
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Metabolic signatures in gastroenteropancreatic neuroendocrine neoplasms: unraveling diagnostic and prognostic insights

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Reydson Alcides de Lima-Souza · Tayná Figueiredo Maciel · Moisés Willian Aparecido Gonçalves · Fernanda Cristina Poscai Ribeiro · Rodrigo A Maioral · João Figueira Scarini · Heloisa P Soraes · Gary Chris Fillmore · Erika Said Abu Egal

Front Endocrinol (Lausanne). 2025 Dec 11;16:1676021. doi: 10.3389/fendo.2025.1676021. eCollection 2025.

ABSTRACT

Gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs) are a heterogeneous group of tumors characterized by diverse biological behaviors and variable clinical outcomes. Recent advances have highlighted the important role of metabolic reprogramming in tumorigenesis, progression, and therapeutic resistance in GEP-NENs. In this review, we synthesize the current evidence on metabolic biomarkers and altered metabolic pathways-particularly those involving glucose, lipid, and amino acid metabolism. Key biomarkers such as GLUT-1, FASN, and enzymes involved in ferroptosis, cholesterol biosynthesis, and amino acid catabolism demonstrate strong associations with tumor aggressiveness, hypoxia, and mTOR signaling. Moreover, metabolomic profiling and functional studies suggest that metabolic markers may inform prognosis and predict response to targeted therapies such as Everolimus. Although promising, the clinical translation of these markers is still limited and requires further validation in large, subtype-specific cohorts. Our findings highlight the importance of integrating metabolic profiling into the diagnostic and therapeutic landscape of GEP-NENs. Future research should prioritize biomarker standardization, multi-omics integration, and the development of metabolism-based therapeutic strategies tailored to tumor subtype and differentiation grade.

PMID:41458541 | PMC:PMC12738315 | DOI:10.3389/fendo.2025.1676021

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An integrated bioinformatics and multi-omics investigation of the sirtuin family to identify their prognostic importance in human cancers

✇Omics in Hepatocellular
By: Md Shahedur Rahman · Rizone Al Hasib · Md Rezanur Rahman · Polash Kumar Biswas · Abu Reza · Munzura Khatun · Mohammad Abu Hena Mostofa Jamal

Tumour Biol. 2025 Jan-Dec;47:14230380251410470. doi: 10.1177/14230380251410470. Epub 2025 Dec 24.

ABSTRACT

BackgroundIn recent years, the significance of sirtuins in cancer biology has become increasingly evident, but their molecular mechanisms and prognostic impacts remain elusive.ObjectiveThe present study aimed to investigate the differential expression of the sirtuin gene family across cancers and to evaluate their prognostic value.MethodsWe used various bioinformatics databases and methodologies, including Oncomine, GEPIA, OncoDB, cBioPortal, R2 Kaplan-Meier Scanner, STRING, etc., to determine the expression pattern of the sirtuin family genes, along with their mutations and prognostic values in human cancers.ResultsIn the current study, SIRT1, SIRT2, SIRT4, and SIRT5 were downregulated in lymphoma, whereas SIRT6 and SIRT7 were overexpressed. In breast cancer, SIRT3, SIRT5, and SIRT7 were overexpressed, and in terms of kidney cancer, higher expression of SIRT2, SIRT3, and SIRT5 was observed. In contrast, for leukemia, bladder, and brain cancers, most sirtuin family members showed reduced expression. We found that most mutations occurred in uterine cancer, chRCC (chromophobe renal cell carcinoma), DLBCL (diffuse large B-cell lymphoma), melanoma, pRCC (papillary renal cell carcinoma), and esophageal cancer. Moreover, we identified the relevant functional proteins through protein-protein interaction analysis to evaluate copy number alterations (CNAs) in sirtuins. The most frequent alterations were amplifications and deep deletions. Survival analysis demonstrated that SIRT1 and SIRT2 overexpression correlated with improved overall survival in low-grade glioma but predicted poorer outcomes in ovarian cancer. Downregulation of SIRT1, SIRT3, and SIRT5 was associated with better prognosis in DLBCL, while SIRT3 and SIRT4 upregulation predicted favorable survival in testicular germ cell tumors. SIRT6 overexpression was linked to favorable prognosis in esophageal carcinoma and sarcoma, while unfavorable outcomes were observed in hepatocellular carcinoma and cholangiocarcinoma. SIRT7 upregulation was significantly associated with reduced survival in esophageal, liver, and uterine cancers, but surprisingly correlated with improved outcomes in urothelial carcinoma and cervical squamous cell carcinoma.ConclusionsTogether, this multi-omics analysis reveals the correlation and prognostic values of sirtuins across multiple types of human cancers and suggests that sirtuins may serve as promising biomarkers for different cancers.

PMID:41439701 | DOI:10.1177/14230380251410470

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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
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.
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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

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
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Comparison of liquid biopsy-based technologies for cancer screening

✇MRD
By: Andreas P Papanastasiou · Miyo K Chatanaka · Eleftherios P Diamandis

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

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