Call for Papers

Author Instructions:

We invite submissions describing innovative machine learning research focused on bridging the gap between machine learning research and application in clinical practice. Authors are invited to submit works that fit anywhere within the broad overview of our workshop.

We are specifically interested (but are not limited to) the following areas:

• Procedures that bring humans-in-the-loop for auditing ML healthcare systems to improve human performance, machine performance, or both.

• Methods that are robust to changes in population, distribution shifts, or other types of biases.

• Properties of ML methods/systems to be fulfilled to successfully deploy them in the clinic where the feasibility of these properties should also be taken into account.

• Analyses of how to assess the failure modes of ML models for healthcare and reduce over-reliance.

• Developing methods for improved interpretability of ML predictions in the context of healthcare.

• Translational and implementational aspects: challenges and lessons learned from integrating an ML system into clinical workflow.

In order to make theoretical machine learning research actionable in clinical practice, we especially encourage contributions from application areas that tackle minority data groups and thus, have their own unique challenges; for example, paediatrics, critical care (ICU), rare diseases like Alzheimers, HIV, fertility, and others. To incentivise high-quality submissions of novel work, which should be compelling and highly relevant to research and clinics, we will be awarding Best Paper and Top Reviewer Awards.

Important Dates:

• Submission Deadline: Sep 17, 2021 extended to Oct 7, 2021, Anywhere on Earth

• Author Notification: Oct 22, 2021

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