166 articles tagged with #medical-ai. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
AINeutralarXiv – CS AI · Mar 35/104
🧠Researchers developed a conformal prediction framework for Large Language Models used in medical entity extraction, testing on FDA drug labels and radiology reports. The study found that model calibration varies significantly across clinical domains, with models being underconfident on structured data but overconfident on free-text reports, achieving 90% target coverage with 9-13% rejection rates.
AINeutralarXiv – CS AI · Mar 34/103
🧠Researchers developed a two-stage method using Structural Causal Models in latent space to generate high-quality 3D brain MRI counterfactuals, addressing the challenge of limited training data in medical imaging. The approach combines VQ-VAE encoding with causal modeling to produce diverse, high-fidelity brain MRI data beyond the original training distribution.
AINeutralarXiv – CS AI · Mar 34/103
🧠Researchers propose a Manifold Residual (MR) block to address overfitting in few-shot Whole Slide Image classification by preserving the low-dimensional manifold geometry of pathology foundation model features. The geometry-aware approach achieves state-of-the-art results with fewer parameters by using a fixed random matrix as geometric anchor and a trainable low-rank residual pathway.
AINeutralarXiv – CS AI · Feb 274/105
🧠Researchers developed a new AI-powered surrogate model for ECG simulations that combines geometry encoding with neural networks to predict lead-field gradients. The method achieves high accuracy (5° mean angular error, <2.5% relative error) while reducing computational costs and data requirements compared to traditional full-order models.
AIBullishGoogle Research Blog · Sep 245/104
🧠AfriMed-QA introduces a new benchmark for evaluating large language models' performance in global health contexts, specifically focusing on African healthcare scenarios. This research addresses the need for culturally relevant AI assessment tools in medical applications for underrepresented regions.
AINeutralGoogle Research Blog · Aug 124/105
🧠The article discusses enabling physician-centered oversight for AMIE, a generative AI system, focusing on medical applications of artificial intelligence. However, the article body provided is incomplete with only 'Generative AI' mentioned, limiting detailed analysis.
AIBullishGoogle Research Blog · Aug 64/104
🧠Research demonstrates the ability to predict insulin resistance using wearable device data combined with routine blood biomarkers. This represents an advancement in personalized healthcare monitoring through AI-driven analysis of continuous health data.
AINeutralGoogle Research Blog · Apr 305/103
🧠The article discusses benchmarking Large Language Models (LLMs) for applications in global health, focusing on evaluating AI performance in healthcare contexts. This represents ongoing efforts to assess and improve generative AI capabilities for critical health applications worldwide.
AIBullisharXiv – CS AI · Mar 34/105
🧠Researchers developed OSF, a family of sleep foundation models trained on 166,500 hours of sleep data from nine public sources. The study reveals key insights about scaling and pre-training for sleep AI models, achieving state-of-the-art performance across nine datasets for sleep and disease prediction tasks.
AIBullisharXiv – CS AI · Mar 34/106
🧠AdURA-Net is a new AI framework designed for medical image analysis that addresses uncertainty in clinical decision-making for thoracic disease classification. The system uses adaptive dilated convolution and a dual head loss function to handle uncertain diagnostic labels in medical datasets like CheXpert and MIMIC-CXR.
AINeutralarXiv – CS AI · Mar 34/104
🧠Researchers propose TAP-SLF, a parameter-efficient framework for adapting Vision Foundation Models to multiple ultrasound medical imaging tasks simultaneously. The method uses task-aware prompting and selective layer fine-tuning to achieve effective performance while avoiding overfitting on limited medical data.
AINeutralarXiv – CS AI · Mar 34/104
🧠Researchers have developed OPGAgent, a multi-tool AI system for analyzing dental panoramic X-rays that outperforms current vision language models. The system uses specialized perception modules and a consensus mechanism to provide more accurate and auditable dental imaging interpretation across multiple diagnostic tasks.
AINeutralarXiv – CS AI · Mar 34/104
🧠Researchers developed Geometry OR Tracker, a two-stage pipeline system that improves 3D tracking accuracy in operating rooms by first correcting camera calibration issues, then performing robust tracking in a unified world frame. The system reduces cross-view depth disagreement by over 30x compared to raw calibration, enabling better surgeon behavior recognition and motion analysis.
AINeutralarXiv – CS AI · Mar 34/104
🧠Researchers developed a new multi-task AI framework for breast ultrasound analysis that simultaneously performs lesion segmentation and tissue classification. The system uses multi-level decoder interaction and uncertainty-aware coordination to achieve 74.5% lesion IoU and 90.6% classification accuracy on the BUSI dataset.
AINeutralarXiv – CS AI · Mar 24/106
🧠Researchers propose SegReg, a latent-space regularization framework for medical image segmentation that improves model generalization and continual learning capabilities. The method operates on U-Net feature maps and demonstrates consistent improvements across prostate, cardiac, and hippocampus segmentation tasks without adding extra parameters.
AIBullisharXiv – CS AI · Mar 24/105
🧠Researchers have developed R2GenCSR, a new AI framework for generating radiology reports that uses Mamba architecture instead of Transformers to reduce computational complexity while maintaining performance. The system leverages context retrieval and large language models to produce high-quality medical reports from X-ray images.
