May 29, 2024Journal of Clinical Oncology

Using an AI trial screening tool to assess language as a barrier for enrollment of underrepresented minorities in cancer clinical trials.

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Authors

LCLaura CorveseUtrecht University CWChristina WeißYale University SHSharon HuieYale University

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Implication

Retrospective analysis reveals lower cancer trial enrollment among non-English speaking patients, highlighting language as a key barrier despite automated screening identification.

Key Points

Eligible non-English speaking patients show significantly reduced cancer clinical trial enrollment compared to English-speaking peers despite automated AI eligibility matching.
Enrolled patients were only 1.59% non-English speaking, with aggregate enrollment rates reaching 5.6% for non-English speakers versus 29.4% for English speakers (p=0.0285).
Retrospective analysis using an artificial intelligence patient matching tool across 13,466 individuals highlights severe language barriers and the need for targeted interventions.

Cite This Study

Corvese et al. (2024) studied this question.

synapsesocial.com/papers/68e67e28b6db64358760838b https://doi.org/10.1200/jco.2024.42.16_suppl.e13586

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Implication

Retrospective analysis reveals lower cancer trial enrollment among non-English speaking patients, highlighting language as a key barrier despite automated screening identification.

Using an AI trial screening tool to assess language as a barrier for enrollment of underrepresented minorities in cancer clinical trials.

Discussion

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Implication

Key Points

Cite This Study

Using an AI trial screening tool to assess language as a barrier for enrollment of underrepresented minorities in cancer clinical trials.

Discussion

Member takes

Implication

Key Points

Cite This Study

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