This paper examines how LLMs, when improperly deployed in healthcare, could reinforce existing inequities due to biased training data or inconsistent outputs.
It proposes mitigation strategies such as diverse dataset inclusion, fairness audits, and equity-focused benchmarks. Emphasizing the importance of responsible GenAI development, it also explores systemic impacts on underserved communities.
This makes the article vital reading for those building Gen AI for public good, ensuring technologies like medical chatbots or diagnostic tools don’t deepen health disparities across populations.
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