Scholarlink

<p>This paper presents an in-depth analysis of the Emory Health AI (Artificial Intelligence) Bias Datathon held in August 2023, providing insights into the experiences gained during the event. The datathon, focusing on health-related issues, attracted diverse participants, including professionals, researchers, and students from various backgrounds. The paper discusses the preparation, organization, and execution of the datathon, detailing the registration process, team formulation, dataset creation, and logistical aspects. We also explore the achievements and personal experiences of participants, highlighting their resilience, dedication, and innovative contributions. The findings include a breakdown of participant demographics, responses to post-event surveys, and participant backgrounds. Observing the trends, we believe the lessons learned, and the overall impact of the Emory Health AI Bias Datathon on the participants and the field of health data science will contribute significantly in organizing future datathons.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Innovating Challenges and Experiences in Emory Health AI Bias Datathon: Experience Report

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年2月25日

查看全文
  • Atika Rahman Paddo,
  • Saptarshi Purkayastha,
  • Janice Newsome,
  • Hari Trivedi,
  • Judy Wawira Gichoya

摘要

This paper presents an in-depth analysis of the Emory Health AI (Artificial Intelligence) Bias Datathon held in August 2023, providing insights into the experiences gained during the event. The datathon, focusing on health-related issues, attracted diverse participants, including professionals, researchers, and students from various backgrounds. The paper discusses the preparation, organization, and execution of the datathon, detailing the registration process, team formulation, dataset creation, and logistical aspects. We also explore the achievements and personal experiences of participants, highlighting their resilience, dedication, and innovative contributions. The findings include a breakdown of participant demographics, responses to post-event surveys, and participant backgrounds. Observing the trends, we believe the lessons learned, and the overall impact of the Emory Health AI Bias Datathon on the participants and the field of health data science will contribute significantly in organizing future datathons.