Joint KDD 2021 Health Day and 2021 KDD Workshop on Applied Data Science for Healthcare

State of XAI and trustworthiness in Health

 Location: Virtual
 Workshop Date: August 14-18, 2021
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Healthcare is, traditionally, a knowledge-driven enterprise with an enormous amount of data - both structured and unstructured. These data can impact positively on the development of data-driven health care including precision medicine and precision public health. In recent years, large scale medical/clinical datasets, such as “omics” data and radiology reports are increasingly available. We have also witnessed an increasing number of successful AI/ML applications using such datasets to address problems such as drug repurposing and preliminary screening of radiology reports. To facilitate the adoption of such AI/ML in practice, we have simultaneously witnessed an increasing adoption/innovation of using explainability methods to analyze/present AI for Health. In this deep learning era, What is the current status of AI/ML applications in healthcare? What are the standard methods of explaining such AI models for healthcare? What are the roles of causality in AI/ML practices? What are the state-of-the-art developments in causal AI in health and health care domains? What are the limitations and how are the different facets of trust and explanations (see figure 1 below) being addressed in practice? Can knowledge-backed AI lead to more robust and interpretable models? How do data scientists and physicians apply this knowledge in collaboration and via human-centered AI approaches to further the field and improve healthcare? How are regulatory requirements for transparency and trustworthiness of models and data privacy being defined and how can they be fulfilled? After witnessing so many great achievements from deep learning lately, we propose to invite world-leading experts from both data science and healthcare to discuss and debate the path forward for practical applications of AI/ML in healthcare, including demos, early work, and critiques on the current state and the path forward for explainability and trustworthiness in AI. More specifically, we plan to attract high-quality original research from emerging areas with significant implications in healthcare and invite open discussions on controversial yet crucial topics regarding healthcare transformation

Previous Iterations

  • DSHealth 2020: 2020 KDD Workshop on Applied Data Science for Healthcare: Trustable and Actionable AI for Healthcare
  • DSHealth 2019: 2019 KDD Workshop on Applied Data Science for Healthcare: Bridging the Gap between Data and Knowledge
  • MLMH 2018: 2018 KDD Workshop on Machine Learning for Medicine and Healthcare

Invited Speakers

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Mihaela Van Der Schaar
John Humphrey Plummer Professor of Machine Learning, AI, and Medicine at University of Cambridge
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Alexej Gossmann
Staff Fellow (Mathematical Statistician) at FDA
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Fernando Schwarz
Global Head of Data Science, Merck
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Leo Celi
Principal Research Scientist, MIT; Co-Director, MIT Sana; Staff Physician, Beth Israel Deaconess Medical Center; Assoc. Prof, Harvard Medical School
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Gunther Jansen
Head of PHC analytics, Roche
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William Kassler
CMO, Palantir Technology Inc.
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Gretchen Purcell Jackson
Vice President and Chief Science Officer, IBM, Watson Health
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Dave DeCaprio
CTO and Co-founder at

Call for Papers

We invite full papers, as well as work-in-progress on the application of data science in healthcare. Topics may include, but not limited to, the following topics (For more information see workshop overview) with special focus on papers that are aimed at addressing the state of explainable and trustworthiness of AI in healthcare.

  • Applications of XAI in Healthcare
  • Critique of XAI in Healthcare
  • User driven XAI in Healthcare
  • Trustworthiness
  • Uncertainty quantification and communication, e.g. out-of-distribution detection
  • Domain generalization and adaptation, e.g. multi-site studies
  • Data access, sharing and privacy, e.g. via federated learning
  • Human-in-the-loop-learning and human-centered AI
  • Synthetic data for healthcare research
  • Interpretable healthcare
  • Actionable Insights
  • Behavioral studies
  • Ethical AI and accountability
  • Regulatory aspects of AI in healthcare
  • Nudging and implications
  • Multidisciplinary studies on Healthcare
  • Demos of practical applications

Papers must be submitted in PDF format to easychair and formatted according to the new Standard ACM Conference Proceedings Template . Authors are encouraged to use the Overleaf template . Papers must be a maximum length of 4 pages, excluding references.

The program committee will select the papers based on originality, presentation, and technical quality for spotlight and/or poster presentation.

Selected papers will be invited to publish in a special issue of Artificial Intelligence in Medicine journal

Key Dates

  • Paper Submission opens: Apr 15, 2021
  • Paper Submission deadline: May 20, 2021 Jun 01, 2021
  • Acceptance Notice: Jun 20, 2021 Jun 25, 2021
  • Workshop Date: Aug 14-18, 2021

All deadlines correspond to 11:59 PM Hawaii Standard Time ( HST).