SpiroLLM: Finetuning Pretrained LLMs to Understand Spirogram Time Series with Clinical Validation in COPD Reporting

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.