Health equity assessment of machine learning performance (HEAL): a framework and dermatology AI model case study-Reference-Cited by-同舟云学术

Health equity assessment of machine learning performance (HEAL): a framework and dermatology AI model case study

Published:2024-04 Issue: Volume:70 Page:102479
ISSN:2589-5370
Container-title:eClinicalMedicine
language:en
Short-container-title:eClinicalMedicine

Author:

Schaekermann Mike,Spitz Terry^ORCID,Pyles Malcolm,Cole-Lewis Heather,Wulczyn Ellery,Pfohl Stephen R.^ORCID,Martin Donald,Jaroensri Ronnachai,Keeling Geoff,Liu Yuan,Farquhar Stephanie,Xue Qinghan,Lester Jenna,Hughes Cían,Strachan Patricia,Tan Fraser,Bui Peggy,Mermel Craig H.,Peng Lily H.,Matias Yossi,Corrado Greg S.,Webster Dale R.,Virmani Sunny,Semturs Christopher^ORCID,Liu Yun^ORCID,Horn Ivor,Cameron Chen Po-Hsuan^ORCID

Funder

Google LLC

Publisher

Elsevier BV

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1. The Impact of Artificial Intelligence on Health Equity in Dermatology;Current Dermatology Reports;2024-07-10

2. Technology innovation to reduce health inequality in skin diagnosis and to improve patient outcomes for people of color: a thematic literature review and future research agenda;Frontiers in Artificial Intelligence;2024-06-13

3. Exploring the analysis capabilities and clinical application potential of the Claude 3 Opus in different dermatologic images: the development of a large-scale multimodal model to assist in dermatology clinical practice (Preprint);2024-06-12

4. Advancing LGBTQ+ inclusion in STEM education and AI research;Patterns;2024-06