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SpiroLLM: Finetuning Pretrained LLMs to Understand Spirogram Time Series with Clinical Validation in COPD Reporting
arXiv – CS AI|Shuhao Mei, Yongchao Long, Xiaoyu Xiao, Shan Cao, Xiaobo Han, Shijia Geng, Jinbo Sun, Yuxi Zhou, Shenda Hong||4 views
🤖AI Summary
Researchers developed SpiroLLM, the first multimodal large language model capable of understanding spirogram time series data for COPD diagnosis. Using data from 234,028 UK Biobank individuals, the model achieved 0.8977 diagnostic AUROC and maintained 100% valid response rate even with missing data, far outperforming text-only models.
Key Takeaways
- →SpiroLLM is the first LLM capable of understanding spirogram time series data for respiratory disease diagnosis.
- →The model achieved 0.8977 diagnostic AUROC using a dataset of 234,028 individuals from UK Biobank.
- →SpiroLLM maintained 100% valid response rate with missing data compared to 13.4% for text-only models.
- →The multimodal approach combines morphological features from respiratory curves with numerical PFT values.
- →This establishes a new paradigm for interpretable clinical decision support tools in healthcare AI.
#spirollm#healthcare-ai#multimodal-llm#copd-diagnosis#clinical-ai#uk-biobank#respiratory-disease#medical-llm#spirogram#pulmonary-function
Read Original →via arXiv – CS AI
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