ArticleJournal of imaging informatics in medicine2025
Innovating Challenges and Experiences in Emory Health AI Bias Datathon: Experience Report.
Article in Journal of imaging informatics in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Demographic Confounding in Voice-Based Parkinson Disease Screening: Methodological Analysis of the Bridge2AI Voice Dataset.Journal of medical Internet research · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.