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Posted: 2025-04-09 04:19:02 UTC
This article contains some claims that remain unverified. While much of the content may be accurate, exercise care when relying on this information.
This article contains some claims that remain unverified. While much of the content may be accurate, exercise care when relying on this information.
Status
Last Updated
2025-04-09 04:19:18 UTC
Verified By
Rollup News
A study using 9 large language models showed that treatment recommendations varied based on sociodemographic features in simulated clinical cases.
LLMs exhibit bias in clinical recommendations based on sociodemographic factors.
Treatment, referral, and follow-up suggestions differ based on patient characteristics.
The study highlights potential ethical concerns in AI-driven healthcare.
Ensuring fairness and equity in AI-driven healthcare.
Mitigating biases present in large language models.
Addressing ethical concerns related to AI in clinical decision-making.