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Performance of a Large Language Model for Predicting Clinical and Radiographic Outcomes in Knee Osteoarthritis: Single-Center Prospective Observational Study
- Truvace record
- TRV-2026-2879raw data (JSON)
- Version
- v1Published
- First published
- Evidence type
- Observational study
- Sources
- 1 sourcePeer-reviewed
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Background: Large language models (LLMs) can extract information from unstructured clinical text, but it remains unclear whether their ability to assess current status translates into reliable predictions of longitudinal outcomes. Objective: This study aimed to evaluate DeepSeek's ability to assess baseline clinical and radiographic status in knee osteoarthritis (KOA), its performance in predicting 6-month outcomes, and its interrun reproducibility. Methods: This study used a single-center, prospective observati…
The problem
In a prospective study of 111 knee osteoarthritis patients, DeepSeek's 6-month WOMAC score predictions erred by 5.92 to 6.58 points on average, versus 3.86 for a rheumatologist and 5.43 for a simple no-change model.
What this doesn’t fix
The study was single-center, with 111 patients analysed, and evaluated only one DeepSeek configuration, so results may not carry over to other settings or models.
Sources
1 on file- JMIR Medical Informaticsdoi:10.2196/107092
Cite this record
v1Truvace Impact Record TRV-2026-2879, v1: “Performance of a Large Language Model for Predicting Clinical and Radiographic Outcomes in Knee Osteoarthritis: Single-Center Prospective Observational Study.” Truvace, 2026-11-13. /record/TRV-2026-2879 (accessed on the day you copy it). sha256 7d9ac12ce8bca9e1…
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