Medical Imaging AI
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ReclAIm: A Multi-Agent Framework for Monitoring and Correcting Performance Decline in Medical Imaging AI
Announce Type: replace Abstract: Purpose: To develop and evaluate a multi-agent framework (ReclAIm) for automated monitoring, detection, and correction of performance decline in medical image classification models. Materials and Methods: ReclAIm is a large language model-based multi-agent system that operates through natural language interaction. A master agent coordinating three task-specific agents performed performance evaluation and triggered fine-tuning when substantial performance...
Michigan candidate blasted for AI-altered image that has him looking extra buff
Michigan candidate blasted for AI-altered image that has him looking extra buff Candidates across the country were soon posting AI-enhanced images of them appearing with hulking physiques - Bookmark - CommentsGo to comments An AI image showing an extra-buff candidate for U.S. Senate in Michigan went viral this week, prompting equal parts mockery and imitation. On Tuesday, an image surfaced on social media of former congressman Mike Rogers walking at the front of a parade of supporters. In...
Label tree semantic losses for rich multi-class medical image segmentation
arXiv:2507.15777v4 Announce Type: replace Abstract: Rich and accurate medical image segmentation is poised to underpin the next generation of AI-defined clinical practice by delineating critical anatomy for pre-operative planning, guiding real-time intra-operative navigation, and supporting precise post-operative assessment. However, commonly used learning methods for medical and surgical imaging segmentation tasks penalise all errors equivalently and thus fail to exploit any inter-class...
Causal Transfer in Medical Image Analysis
arXiv:2603.24388v2 Announce Type: replace Abstract: Medical imaging models frequently fail when deployed across hospitals, scanners, populations, or imaging protocols due to domain shift, limiting their clinical reliability. While transfer learning and domain adaptation address such shifts statistically, they often rely on spurious correlations that break under changing conditions. On the other hand, causal inference provides a principled way to identify invariant mechanisms that remain...
J-RAS: Mutual Adaptation for Medical Image Segmentation via Contrastive Retrieval-Augmented Joint Optimization
Announce Type: replace Abstract: Manual medical image segmentation by clinicians, though accurate, is time-consuming and variable across experts, whereas AI-based models automate this process but often underperform with limited data and domain shifts. Inspired by how pathology trainees acquire disease recognition skills through guided comparison with expert-annotated slides and histopathology atlas reference images, we propose Joint Retrieval-Augmented Segmentation (J-RAS). This framework...
AutoMedBench: Towards Medical AutoResearch with Agentic AI Models
arXiv:2606.01961v1 Announce Type: new Abstract: Autonomous agents are increasingly expected to support end-to-end medical-AI research workflows, moving beyond isolated prediction tasks or short-form clinical question answering. However, existing medical agent benchmarks primarily evaluate final outputs, providing limited visibility into agent behavior within the research process. To address this gap, we present AutoMedBench, a workflow-aware benchmark for autonomous medical-AI research...
AutoMedBench: Towards Medical AutoResearch with Agentic AI Models
arXiv:2606.01961v2 Announce Type: replace Abstract: Autonomous agents are increasingly expected to support end-to-end medical-AI research workflows, moving beyond isolated prediction tasks or short-form clinical question answering. However, existing medical agent benchmarks primarily evaluate final outputs, providing limited visibility into agent behavior within the research process. To address this gap, we present AutoMedBench, a workflow-aware benchmark for autonomous medical-AI research...
UniMedVL: Unifying Medical Multimodal Understanding and Generation through Observation-Knowledge-Analysis
Announce Type: replace Abstract: Medical workflows routinely combine reading images with producing visual and textual outputs, making both image understanding and generation central to medical AI. Most existing systems, however, address these abilities in isolated models, losing the shared knowledge that a unified architecture could exploit. To bridge this gap, we present UniMedVL, the first unified medical model that seamlessly integrates multimodal understanding and generation capabilities...
Benchmarking Uncertainty and its Disentanglement in multi-label Chest X-Ray Classification
arXiv:2508.04457v2 Announce Type: replace-cross Abstract: Reliable uncertainty quantification is crucial for trustworthy decision-making and the deployment of AI models in medical imaging. While prior work has explored the ability of neural networks to quantify predictive, epistemic, and aleatoric uncertainties using an information-theoretical approach in synthetic or well defined data settings like natural image classification, its applicability to real life medical diagnosis tasks remains...
Noise-Aware Visual Representation Learning for Medical Visual Question Answering
arXiv:2606.05535v1 Announce Type: new Abstract: Medical visual question answering (Med-VQA) has strong potential for clinical decision support by enabling AI models to interpret medical images and answer clinically relevant queries. Recent approaches typically connect off-the-shelf vision encoders with large language models (LLMs) through lightweight mapping networks to reduce computational cost. However, these methods often overlook the importance of handling noise and small irrelevant...