Cervical spondylotic myelopathy (CSM) refers to spinal cord compression from arthritis in the neck and is the leading cause of spinal cord dysfunction in older adults. This new approach is opening new opportunities for earlier treatment. The findings were published online, in npj Digital Medicine.

The WashU study also shows smaller, targeted models generalize better than large foundation models for early detection of cervical myelopathy. The research team used seven different AI models to analyze large datasets containing electronic health record data of more than 2 million people with and without CSM. The models examined patterns of health care interactions, such as tests and diagnoses, recorded in electronic health records to spot patients whose medical histories resemble those already diagnosed with CSM, helping to flag individuals who may be at higher risk.

Using both a large external dataset and a smaller dataset from a St. Louis–based health system, the team trained models to predict CSM risk as early as 30 months before a clinical diagnosis, said Warner, who works in the lab of Chenyang Lu, the Fullgraf Professor and director of the AI for Health Institute and co-senior author of the study.

The team evaluated both large foundation models, or “out-of-the-box” systems pretrained on extensive clinical datasets, and smaller, specialized models that incorporate clinical insight and focus only on the most relevant variables.

The foundation models demonstrated superior performance during internal validation on a large, heterogeneous dataset, whereas the smaller, clinically derived model trained from scratch showed better generalizability and more consistent performance across external health care systems.

Conclusion

The team emphasizes on the importance of AI and also identified the challenges. According to researchers, one of the biggest challenges for AI-based prediction models in clinical medicine is generalizability.  A model may perform well in one hospital system but fail in others. For complex conditions like CSM, the study team found that large models trained on millions of patients did not generalize as well as smaller, clinically tailored models. This underscores the importance of embedding clinical insight into AI solutions for healthcare. Clinical knowledge remains essential for developing robust and trustworthy AI tools.