AnatomiX, an Anatomy-Aware Grounded Multimodal Large Language Model for Chest X-Ray Interpretation
arXiv:2601.03191v3 Announce Type: replace-cross Abstract: Multimodal medical large language models have shown substantial progress in chest X-ray interpretation but continue to face challenges in spatial reasoning and anatomical understanding. Although existing grounding techniques improve overall performance, they often fail to establish a true anatomical correspondence, resulting in incorrect anatomical understanding in the medical domain. To address this gap, we introduce AnatomiX, a multitask multimodal large language model for anatomically grounded chest X-ray interpretation. Inspired by
The proliferation of powerful multimodal large language models and increasing demand for advanced medical diagnostics converge, enabling more sophisticated AI applications in healthcare.
This development represents a significant step towards more accurate and reliable AI-driven medical diagnostics, potentially reducing errors and improving patient outcomes in critical areas like chest X-ray interpretation.
AI models for medical imaging are evolving from general interpretation to anatomically grounded understanding, leading to higher trustworthiness and practical utility in clinical settings.
- · AI developers in healthcare
- · Healthcare providers
- · Radiologists
- · Medical AI startups
- · Developers of less-grounded medical AI
- · Diagnostic imaging companies without strong AI integration
Improved diagnostic accuracy and efficiency in medical imaging interpretation.
Accelerated adoption of AI in clinical decision-making, leading to new medical protocols and training requirements.
The development of a new 'gold standard' for medical image AI, heavily emphasizing anatomical grounding and interpretability, potentially impacting regulatory frameworks.
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Read at arXiv cs.LG