Rethinking Objectivity in Clinical AI: A Qualitative Study of Concussion Evaluators

Concussion evaluation technologies have increasingly emphasized objectivity, leveraging AI and machine learning to formalize and standardize diagnostic processes. However, this orientation toward objectivity has not been evaluated via stakeholder engagement, to see if this is what they want . In this paper, we present findings from interviews with 11 concussion evaluators, examining how they make decisions, use healthcare technology, and envision the role of AI in their workflows. Our study surfaces the tensions between evaluators’ values, such as flexibility and coordination, and the assumptions built into existing decision support systems. We show how evaluators seek tools that preserve clinical agency, support collaboration across fragmented care settings, and complement rather than override their interpretive expertise. Drawing on these insights, we propose design considerations for developing AI-based systems that align with concussion evaluation’s subjective and distributed nature. This work contributes to CSCW by challenging objectivity as a default design goal and advocating for stakeholder-centered AI in healthcare.