NLP & ML6 min read
AI & Neuroscience
An AI model's scrambled neurons just recreated a stroke's exact damage to speech
Researchers perturbed LLaVA 1.6 to simulate aphasic picture-naming errors and matched individual patient profiles in up to seven error categories for 79.5% of cases. Six out of seven error types emerged naturally across different perturbation configurations, suggesting general-purpose multimodal models can function as digital twins for post-stroke language deficits.
2026-07-24