Equity, Function, and Data: A Review of Social and Functional Representation in AI Datasets for Traumatic Brain Injury

Abstract

Traumatic brain injury (TBI) is a leading cause of long-term disability worldwide, and each person’s recovery looks different. Artificial intelligence (AI) offers promising tools to project individual outcomes. However, these models are impacted by the quality and inclusiveness of the dataset on which they are trained, having major implications for clinical value. This scoping review evaluated publicly available datasets that use AI modeling to predict outcomes from TBI. It examined how the literature derived from these datasets captures functional and social variables. Following PRISMA guidelines, 24 studies were identified, yielding 19 distinct datasets. While most datasets emphasized biomedical and injury severity metrics, few incorporated communication, cognition, and relevant social determinants of health. Nearly all studies included age and sex, but fewer than half reported race or ethnicity, and only a small subset integrated broader contextual indicators. Results suggest that outcome modeling continues to rely heavily on global scales, with limited use of domain-specific measurements. Another limiting factor is poor use of longitudinal measures, often not extending follow-up past the six-month post-injury time. These findings point to a need for inclusive, functionally rich, and ethically transparent data practices to aid AI systems in promoting equitable and clinically meaningful care.

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