“In one case that experts described as 'really dangerous', Google wrongly advised people with pancreatic cancer to avoid high-fat foods.”

“AI misses nearly one-third of breast cancers, study finds”

“Article URL: https://surgehq.ai/blog/lmarena-is-a-plague-on-ai”

“Lots of companies have also deprecated their internally built solution to switch over, dealing with GPU infra and onboarding docker containers not being a very exciting problem when the company you work for is trying to cure cancer.”

“T3出行全量业务成功迁移至腾讯云，创行业最大规模纪录 (T3 Mobility's full business successfully migrated to Tencent Cloud, setting an industry record for the largest scale)”

“A tool for spotting pancreatic cancer in routine CT scans has had promising results, one example of how China is racing to apply A.I. to medicine's tough problems.”

“In one case described by experts as "really dangerous," Google advised people with pancreatic cancer to avoid high-fat foods, which is the exact opposite of what should be recommended and could jeopardize a patient's chances of tolerating chemotherapy or surgery.”

“Bernie Sanders and Ron DeSantis speak out against data center boom. It’s a bad sign for AI industry.”

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“The optimal pipeline (ANOVA feature selection with a support vector machine) achieved a mean validation accuracy of 0.79.”

“The proposed approach estimates study-specific sampling weights using auxiliary information and calibrates the estimating equations to obtain the full set of model parameters.”

“Virtual-Eyes improves RAD-DINO slice-level AUC from 0.576 to 0.610 and patient-level AUC from 0.646 to 0.683 (mean pooling) and from 0.619 to 0.735 (max pooling), with improved calibration (Brier score 0.188 to 0.112).”

“The model reaching 96.23% accuracy, 95.58% F1-score and 94.83% specificity.”

“MedGemma-4b-it model, fine-tuned using Low-Rank Adaptation (LoRA), demonstrated superior diagnostic capability by achieving a mean test accuracy of 80.37% compared to 69.58% for the untuned GPT-4.”

“If there isn’t something else out there and it’s really just LLM‘s then I’m not sure how the world can improve much with a confidently incorrect faster way to Google that tells you not to worry”

“The study highlights the potential of deep learning methods in enhancing the diagnosis of respiratory diseases such as COVID-19, lung cancer, and pneumonia from chest x-rays.”

“The paper reports high Dice Similarity Coefficients (DSC) for whole tumor (WT), enhancing tumor (ET), and tumor core (TC) across multiple BraTS datasets, indicating improved segmentation accuracy.”

“The paper introduces a multi-view latent representation learning framework based on variational autoencoders (VAE) to integrate complementary radiomic features derived from post-contrast T1-weighted (T1Gd) and Fluid-Attenuated Inversion Recovery (FLAIR) magnetic resonance imaging (MRI).”

“The article's source is ArXiv, indicating a pre-print publication.”

“UltraLBM-UNet is a novel model for skin lesion segmentation.”

“By reframing LLMs as knowledge curation engines rather than black-box predictors, this work demonstrates a scalable, interpretable, and workflow-compatible pathway for advancing AI-driven decision support in oncology.”

“The purpose of implementing the system of sealing records of administrative violations of public security is to carry out necessary control and standardization of information on administrative violations of public security, and to reduce and avoid the situation of 'being punished once and restricted for life'.”

“Spatially resolved survival modelling from routine histology crosslinked with molecular profiling reveals prognostic epithelial-immune axes in stage II/III colorectal cancer.”

“DGSAN leverages a dual-graph spatiotemporal attention network.”

“The context mentions the article is sourced from ArXiv, indicating it is a pre-print research paper.”

“The research focuses on robust breast cancer segmentation in multi-center DCE-MRI.”

“The study uses Multiple Instance Learning (MIL).”

“RNACOREX matches the predictive power of advanced AI systems—while offering something rare in modern analytics: clear, interpretable explanations.”

“The research focuses on agent-based output drift detection for breast cancer response prediction within a multisite clinical decision support system.”

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“The article explores interpretable link prediction within the context of AI-driven cancer research.”

“The article introduces a novel benchmark dataset for mammography image registration called MGRegBench.”

“The study focuses on explainability of CNN-based prostate cancer classification.”

“WDFFU-Mamba is a model for breast tumor segmentation in ultrasound images.”

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“The article likely discusses using LLMs to enhance cancer survival prediction.”

“The article's context focuses on using AI to assess peritoneal carcinosis during diagnostic laparoscopy for advanced ovarian cancer.”

“The article's source is ArXiv.”

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“The study likely employs statistical methods to compare the performance of different models, potentially including metrics like accuracy, precision, recall, and F1-score. It would also likely delve into the interpretability of the ML models, assessing how well the models' decisions can be understood and explained.”

“The study is sourced from ArXiv.”

“The article focuses on hierarchical analysis of cell inter-relations in multiplex microscopy for lung cancer prognosis.”

“The article's source is ArXiv, indicating it is likely a pre-print of a scientific research paper.”

“The research focuses on the prediction of IDH1 mutations in low-grade glioma.”

“The study utilizes a hybrid deep learning framework.”