#radiology-ai News & Analysis

4 articles tagged with #radiology-ai. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

4 articles

AIBullisharXiv – CS AI · May 47/10

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RadLite: Multi-Task LoRA Fine-Tuning of Small Language Models for CPU-Deployable Radiology AI

Researchers demonstrate that small language models (3-4B parameters) can achieve strong multi-task radiology performance through LoRA fine-tuning, enabling deployment on consumer-grade CPUs without GPUs. The RadLite system, trained on 162K samples across 9 radiology tasks, shows dramatic performance improvements over zero-shot baselines and can be quantized to 1.8-2.4GB for practical clinical deployment.

AIBullisharXiv – CS AI · May 17/10

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RIHA: Report-Image Hierarchical Alignment for Radiology Report Generation

Researchers propose RIHA, a novel transformer-based framework that generates radiology reports from medical images by performing hierarchical alignment between visual and textual features across multiple levels. The method outperforms existing approaches on benchmark chest X-ray datasets by treating reports as structured documents rather than flat sequences, improving both clinical accuracy and natural language quality.

AINeutralarXiv – CS AI · Jun 96/10

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RadOT-Eval: Auditable Structured-Evidence Transport for Radiology Report Evaluation

RadOT-Eval is a new AI framework that uses optimal transport algorithms to automatically evaluate radiology report generation by decomposing reports into structured clinical evidence units and detecting specific error types like omissions, hallucinations, and polarity reversals. The method achieves higher correlation with clinician-annotated errors than existing metrics and LLM-based evaluators, providing an auditable approach for quality assurance in high-stakes medical AI applications.

AINeutralarXiv – CS AI · Jun 46/10

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Multi-Granularity 3D Kidney Lesion Characterization from CT Volumes

Researchers developed LesionDETR, a deep learning model that characterizes kidney lesions in CT scans at the individual lesion level rather than patient or organ level, predicting lesion type, size, enhancement, and attenuation. The model achieved strong performance on bilateral abnormality detection (AUC 0.799-0.817) but revealed that rare solid lesions remain challenging, suggesting data collection rather than architectural improvements are needed next.