2 citations · 2 across the 3 of their papers we have counts for
3 papers
AI-Powered Detection of Inappropriate Language in Medical School Curricula
Chiman Salavati, Shannon Song, Scott A. Hale +3
The use of inappropriate language -- such as outdated, exclusionary, or non-patient-centered terms -- medical instructional materials can significantly influence clinical training,…
Towards Fairer Health Recommendations: finding informative unbiased samples via Word Sense Disambiguation
Gavin Butts, Pegah Emdad, Jethro Lee +5
There have been growing concerns around high-stake applications that rely on models trained with biased data, which consequently produce biased predictions, often harming the most…
Reducing Biases towards Minoritized Populations in Medical Curricular Content via Artificial Intelligence for Fairer Health Outcomes
Chiman Salavati, Shannon Song, Willmar Sosa Diaz +4
Biased information (recently termed bisinformation) continues to be taught in medical curricula, often long after having been debunked. In this paper, we introduce BRICC, a firstin…