RadAgent generates stepwise, tool-augmented chest CT reports with traceable decisions, improving accuracy, robustness, and adding a 37% faithfulness score absent in standard 3D VLMs.
E.et al.Generalist foundation models from a multimodal dataset for 3d computed tomography
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
LoRA fine-tuning of 3-4B SLMs on 162K multi-task radiology data yields strong performance deployable on consumer CPUs at 4-8 tokens/second.
EXACT pre-trains a vision model on 25k CT-report pairs with anatomy-aware weak supervision to output explainable anomaly-aware maps that improve diagnosis, localization, and report generation over prior 3D medical models.
citing papers explorer
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RadAgent: A tool-using AI agent for stepwise interpretation of chest computed tomography
RadAgent generates stepwise, tool-augmented chest CT reports with traceable decisions, improving accuracy, robustness, and adding a 37% faithfulness score absent in standard 3D VLMs.
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RadLite: Multi-Task LoRA Fine-Tuning of Small Language Models for CPU-Deployable Radiology AI
LoRA fine-tuning of 3-4B SLMs on 162K multi-task radiology data yields strong performance deployable on consumer CPUs at 4-8 tokens/second.
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EXACT: an explainable anomaly-aware vision foundation model for analysis of 3D chest CT
EXACT pre-trains a vision model on 25k CT-report pairs with anatomy-aware weak supervision to output explainable anomaly-aware maps that improve diagnosis, localization, and report generation over prior 3D medical models.