Addressing racial and phenotypic bias in human neuroscience methods

EK Webb, JA Etter, JA Kwasa - Nature neuroscience, 2022 - nature.com
Despite their premise of objectivity, neuroscience tools for physiological data collection,
such as electroencephalography and functional near-infrared spectroscopy, introduce racial …

[HTML][HTML] Implicit bias in healthcare: clinical practice, research and decision making

DP Gopal, U Chetty, P O'Donnell, C Gajria… - Future healthcare …, 2021 - Elsevier
Bias is the evaluation of something or someone that can be positive or negative, and implicit
or unconscious bias is when the person is unaware of their evaluation. This is particularly …

12 plagues of AI in healthcare: a practical guide to current issues with using machine learning in a medical context

S Doyen, NB Dadario - Frontiers in digital health, 2022 - frontiersin.org
The healthcare field has long been promised a number of exciting and powerful applications
of Artificial Intelligence (AI) to improve the quality and delivery of health care services. AI …

Artificial intelligence and clinical anatomical education: Promises and perils

MD Lazarus, M Truong, P Douglas… - Anatomical Sciences …, 2024 - Wiley Online Library
Anatomy educators are often at the forefront of adopting innovative and advanced
technologies for teaching, such as artificial intelligence (AI). While AI offers potential new …

Skin color in dermatology textbooks: an updated evaluation and analysis

A Adelekun, G Onyekaba, JB Lipoff - Journal of the American Academy of …, 2021 - jaad.org
REFERENCES 1. Lee MP, Sobanko JF, Shin TM, et al. Evolution of excisional surgery
practices for melanoma in the United States. JAMA Dermatol. 2019; 155 (11): 1244-1251. 2 …

Transparent medical image AI via an image–text foundation model grounded in medical literature

C Kim, SU Gadgil, AJ DeGrave, JA Omiye, ZR Cai… - Nature Medicine, 2024 - nature.com
Building trustworthy and transparent image-based medical artificial intelligence (AI) systems
requires the ability to interrogate data and models at all stages of the development pipeline …

Towards transparency in dermatology image datasets with skin tone annotations by experts, crowds, and an algorithm

M Groh, C Harris, R Daneshjou, O Badri… - Proceedings of the ACM …, 2022 - dl.acm.org
While artificial intelligence (AI) holds promise for supporting healthcare providers and
improving the accuracy of medical diagnoses, a lack of transparency in the composition of …

The resurgence of medical education in sociology: A return to our roots and an agenda for the future

TM Jenkins, K Underman, AH Vinson… - Journal of Health …, 2021 - journals.sagepub.com
From 1940 to 1980, studies of medical education were foundational to sociology, but
attention shifted away from medical training in the late 1980s. Recently, there has been a …

Skin tone analysis for representation in educational materials (star-ed) using machine learning

GA Tadesse, C Cintas, KR Varshney, P Staar… - NPJ Digital …, 2023 - nature.com
Images depicting dark skin tones are significantly underrepresented in the educational
materials used to teach primary care physicians and dermatologists to recognize skin …

Representation of dark skin images of common dermatologic conditions in educational resources: a cross-sectional analysis

SM Alvarado, H Feng - Journal of the American Academy of Dermatology, 2021 - jaad.org
Eight commonly used resources (6 textbooks and 2 web-based resources) and 65
conditions were selected for review. For each condition, images were categorized as light …