EBAIC 2024

In conjunction with IEEE ICHI 2024
June 3rd, 2024, Orlando, Florida

Calls for Papers/Participation

As artificial intelligence (AI) continues to transform healthcare, there is a pressing need to address any ethical challenges and biases inherent in data-driven AI models, thus we can advance safe, trustworthy, and responsible AI in clinical settings. The consequences of biased AI can range from misdiagnoses to unequal access to medical resources and healthcare disparities. With the healthcare community relying more on shared AI-driven decision-making, it's crucial to focus on the ethics of these advanced technologies. Healthcare scientists, academic institutes, industry experts, and policymakers need to actively work together to build solid platforms to tackle potential biases in AI-powered healthcare. This ensures that AI in healthcare becomes reliable, fostering a future where it enhances the well-being of diverse patient populations.

Topics of Interest

We welcome original research on the ethics and bias of AI in clinical applications, covering various AI techniques such as natural language processing, medical imaging, deep learning, predictive and/or descriptive modeling, Internet of Things, mobile health, data quality, and more. Clinical applications can range from decision support systems and translational research to consumer applications and robotics. Additional topics of interest include AI for health equity, transparency in AI techniques, data and algorithmic bias, fairness metrics, and practical solutions to mitigate bias in clinical applications. We also invite limited position papers, particularly from communities historically affected by AI bias or health disparities, as well as institutions actively addressing bias impact in clinical settings.

Program at a Glance

(1) Scientific Session

Oral Presentations & Posters

Submission Types

Regular Papers: 10 pages with up to 2 extra pages for references/appendices. Will describe mature ideas, where a substantial amount of implementation, experimentation, or data collection and analysis has been completed.
Short Papers: 6 pages with up to 1 extra page for references/appendices. Will describe innovative ideas, where preliminary implementation and validation work have been conducted.
Poster Submissions: 2 pages with up to 1 extra page for references/appendices. Will present innovative ideas, late-breaking work, concepts, work-in-progress, early stages of research, and preliminary results from implementation and validations to academic and industrial audience.
Position Papers: 4 pages with up to 2 extra pages for references/appendices. Will present an arguable opinion about AI ethics/bias and its impact.

(2) Tutorial/Hackathon Session

The purpose of the tutorial/hackathon session is to raise awareness of the problem of bias in clinical data and AI algorithms with the ultimate goal of creating innovative approaches that can help reduce or eliminate bias in clinical data and AI. Participants may be students, researchers, and data scientists who are interested in applying AI to clinical applications. Complete this form (link coming soon...) to register the Tutorial/Hackathon Session.

Track I: Ethical Principals for Generative AI in Healthcare
Track II: Medical Imaging Informatics
Track III: Fairlearn: An open-source package to improve fairness of AI