A Comparative Study in Surgical AI: Potential and Limitations of Data, Compute, and Scaling
Recent Artificial Intelligence (AI) models have matched or exceeded human experts in several benchmarks of biomedical task performance, but surgical benchmarks in particular are often missing from prominent medical benchmark suites. Since surgery requires integrating disparate tasks, generally-capable AI models could be particularly attractive as a collaborative tool if performance could be improved. On the one hand, the canonical approach of scaling architecture size and training data is attractive, especially since there are millions of hours of surgical video data generated per year. On the other hand, preparing surgical data for AI training requires significantly higher levels of prof...
| Authors | Kirill Skobelev, Eric Fithian, Yegor Baranovski et al. |
|---|---|
| Primary category | cs.AI |
| Published | 28 March 2026 |
| Last updated at source | 3 September 2026 |
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