Introduction
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AI has the potential to generate a revolution in the field of healthcare by enabling accurate, fast and reliable analyses of data at an unprecedented scale
both in the clinics and in the industry. Leveraged properly, AI can thus allow to better meet patient needs by developing new medical devices, drugs, and
personalized treatments while simultaneously freeing up time for clinical staff to nourish the profound human connection between caregivers and patients.
Moreover, AI promises to democratize the healthcare system by spreading basic services to low-income or remote areas through telemedicine.

Notwithstanding the terrific progress achieved in the last two decades, many AI projects related to medicine struggle to make their way to deployment and
sustainable productivity because of the limited availability of high-quality annotated data. The scarcity of useful information is often exacerbated in
medicine, medical engineering, and healthcare in general because labelling requires highly-specialized staff, patient privacy must be respected, ethnic
differences and rare diseases adequately represented. Despite the incredible advances of the last few years in facilitating data collection and annotation,
learning representations, and detecting different types of bias, basic observations on implications for practitioners are often lacking, new ingenious
ideas are flourishing, and recommendations for healthcare are far from established.

Topics of interest include:

# Publication of datasets relevant to healthcare, including text, images, audio and structured data.
# Hardware and software tools for enabling data acquisition in low-resource or restricted environments, such as federated annotations and pseudonymization techniques.
# Tools to produce or evaluate high-quality clinical annotations and consensus diagnoses.
# Critical analysis of iterative procedures to clean up or refine annotations, as well as guidelines to assess the uncertainty on metric scores.
# Anonymization methods for intra- and inter-institutional data exchange.
# Technical solutions to work in the presence of legal concerns, for instance, federated learning and i2b2.
# Works on learning representations or transfer learning, focusing on improving model generalization across different patient cohorts, data acquisition conditions, medical expert evaluations, etc.
# Studies that compare or combine learning from nature with learning from human experts.
# Works on unsupervised, self-supervised, semi-supervised, or few-shot learning aimed, e.g. at reducing the need for annotations by specialists.
# Methods to deal with strongly imbalanced datasets such as those including rare diseases or very small pathological features in medical image collections.
# Strategies to handle scarcity of subsets in large datasets, i.e. “filling the gaps”.
# Works on using public or artificially-generated datasets to improve the performance of machine-learning models in healthcare or to mitigate (patient) privacy issues.
# Case studies linked to the practical deployment of AI in a clinical setting or in medical devices with limited data, as well as to the construction of pipelines or databases for addressing data scarcity.
# Insightful, original analyses of reasons for the failure of AI projects in healthcare and work-in-progress reports of efforts related to the themes listed above.

We welcome the submission of original research reports on the workshop's topics of interest. The maximum length of papers is fixed to 6 pages, including references. We especially encourage the contribution of case studies, work in progress, position papers, and critical analyses of failed projects.

Important Dates
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Paper submission deadline: May 13, 2022
Decision notification: June 3, 2022
Camera-ready submission: June 17, 2022

Organizers
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Workshop Organizers:
# Simone Lionetti, Lucerne University of Applied Sciences and Arts
# Marc Pouly, Lucerne University of Applied Sciences and Arts
# Alexander Navarini, University of Basel
# Philipp Tschandl, Medical University of Vienna

Program Committee:
# Catarina Barata, University of Lisbon
# Tim vor der Brück, Lucerne University of Applied Sciences and Arts
# Nicolas Deutschmann, IBM Research
# Koustav Ghosal, Accenture
# Matthew Groh, MIT Media Lab
# Fabian Ille, Lucerne University of Applied Sciences and Arts
# Thomas Koller, Lucerne University of Applied Sciences and Arts
# Toni Mancini, “Sapienza” Università di Roma
# Federico Mari, “Foro Italico” Università di Roma
# David Monaghan, Trinity College Dublin
# Javier Montoya, Zurich University of Applied Sciences
# Elif Ozkirimli, Roche
# Marianna Rapsomaniki, IBM Research
# Christoph Rinner, Medical University of Vienna
# Veronica Rotemberg, Memorial Sloan Kettering Cancer Center
# Robin Sandkühler, University of Basel
# Frank-Peter Schilling, Zurich University of Applied Sciences
# Philipp Schütz, Lucerne University of Applied Sciences and Arts
# Andreas Streich, Lucerne University of Applied Sciences and Arts