A new report co‑authored by UCLA Health cautions that while AI has the potential to greatly enhance neurological diagnosis and treatment — for conditions like stroke, seizures, and brain tumors — it may also worsen health inequities. The authors argue that the very datasets needed for training AI systems often lack sufficient representation from marginalized or underdiagnosed populations, putting those groups at risk of further exclusion in care.
Despite these risks, the report also highlights promising applications for AI, particularly in resource-constrained settings. With the right tools, AI could help clinicians detect neurological conditions earlier through clinical notes, tailor medication instructions in a patient’s native language, and support clinics in enrolling underrepresented groups into research studies — potentially narrowing gaps in access.
To address these challenges, the researchers lay out three central principles. First, development must involve diverse community advisory boards to ensure AI tools are culturally and linguistically relevant. Second, neurologists and healthcare providers should receive AI training that alerts them to biases and limitations in algorithm outputs.
Third, robust governance mechanisms are essential: the report recommends independent oversight structures to continuously monitor AI performance, investigate failures, and give patients the right to report issues or request deletion of their data. These measures, the authors argue, are crucial to ensure that AI becomes a tool for equity rather than a new barrier in neurological healthcare.
