arXiv:2606.01049v1 Announce Type: new Abstract: Large-scale biomedical image-text datasets extracted from scientific literature provide valuable resources for medical multimodal model training. These datasets are commonly organized as image-caption pairs; however, figure captions are often short, context-dependent, and only partially informative without the surrounding article text. At the same time, large-scale automatic extraction introduces structural noise such as missing captions, residual markup, duplicated context, and incoherent multi-paragraph figure descriptions. We revisit data cons
The proliferation of multimodal AI models highlights the acute need for high-quality, structured training data, especially in specialized domains like biomedicine where current approaches are suboptimal.
Improving the quality and structure of biomedical training data directly enhances the performance and reliability of AI models in critical applications like medical diagnosis and drug discovery, impacting healthcare outcomes and R&D efficiency.
The proposed 'PMC-InterCPT' approach suggests a methodological improvement for leveraging existing biomedical literature more effectively, potentially leading to more robust and accurate multimodal AI systems.
- · Biomedical AI developers
- · Healthcare sector
- · Pharmaceutical R&D
- · Patients
- · Developers relying solely on noisy datasets
- · Inefficient data curation methods
Improved multimodal AI models for biomedical applications become more accessible and reliable.
Accelerated discovery of new drugs or diagnostic tools due to more capable AI research assistants.
Enhanced AI-driven personalized medicine leading to better patient outcomes and reduced healthcare costs globally.
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Read at arXiv cs.CL