Workshop #1: Assessing credibility of in silico trials for regulatory purposes

Organisers: Marco Viceconti, University of Bologna; Liesbet Geris, University of Liege

Short Description: The use of modelling and simulation to evaluate the safety and efficacy of new medical products, usually referred to as In Silico Trials,  is moving from the research labs to a concrete industrial reality.  Probably the biggest barrier in this translation from research to innovation is the lack of a specific regulatory science to evaluate the credibility of this predictive model when used to assess medical products such as drugs or medical devices.  This workshop provides an update on the latest developments in the field, by some of the leading experts.

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Workshop #2: Trends, challenges and opportunities for the Hospital of the Future

Organisers: Maria Teresa Arredondo and Giuseppe Fico, Universidad Politécnica de Madrid (UPM), Spain

Short Description: New global challenges are demonstrating that high levels of collaboration are required for interconnected societies to face emergencies and crisis. In this context, rethinking and reshaping the role of hospitals as physical and virtual spaces is a fundamental need that National Healthcare System are called to lead. However, hospital management is not data-driven yet, clinical and logistic processes work substantially in silos.

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Workshop #3: Big data and artificial intelligence in cancer imaging

Organisers: Prof. Karim Lekadir, Universitat de Barcelona, Spain; Prof. Luis Martí-Bonmatí, La Fe University, Spain; Prof. Manolis Tsiknakis, FORTH, Greece; Mrs. Gianna Tsakou, Athens R&D Lab of Maggioli S.P.A., Italy

Short Description: Artificial Intelligence (AI) offers substantial opportunities for healthcare, supporting better diagnosis, treatment, prevention and personalised care. Analysis of health images is one of the most promising fields for applying AI in healthcare, contributing to better prediction, diagnosis and treatment of cancer. In order to develop and test reliable AI applications in the field, access to large-volume of high- quality data is needed.

The organizers of the workshop are the coordinators of the four projects that have recently been funded by the EC in the context of a relevant H2020 call (H2020-SC1-FA-DTS-2019-1 – AI for Health Imaging) CHAIMELEON (, EuCanImage (, INCISIVE (, ProCancer-I ( and the PRIMAGE ( a relevant R&D project funded in a previous H2020 call.

The projects are seeking to establish large interoperable repositories of health images, enabling the development, testing and validation of AI–based health imaging solutions to improve diagnosis, disease prediction and follow-up of the most common forms of cancer. The ongoing collaboration and information exchange among the projects so far has highlighted several areas where common approaches and consensus building should be seeked by the five projects and even beyond by the wider scientific community, in order to achieve interoperability. Similarly, it has led to the identification of significant technical and methodological challenges that need to be addressed in designing and exploiting such interoperable cancer imaging data spaces.

The workshop will provide a detailed report of the collaboration opportunities and challenges identified so far; it will also present current approaches towards consensus building and critically discuss alternatives.  Particular emphasis will be given to the issues related to the evaluation of AI-based diagnostic imaging algorithms including robustness, trust and explainability.

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Workshop #4: Integrating Gender perspective in Science, Technology and Innovation

Organisers: Maria Fernanda Cabrera-Umpierrez, Life Supporting Technologies – Universidad Politecnica de Madrid (Spain); Yolanda Ursa, INMARK (Spain)

Short Description: The goal of this workshop is to address the challenge of integrating the gender perspective in science, technology, and innovation (STI) in international cooperation and work towards gender equality discussing about a strategy to promote equality in scientific careers, gender balance in decision making and the integration of the gender dimension in R&I content.

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Workshop #5: Machine Learning based decision support system for early-stage prediction of complications and risk stratification of COVID 19 patients

Organisers: Luca Romeo, Michele Bernardini, Emanuele Frontoni, Dep. of Information Engineering (DII), Università Politecnica delle Marche, Ancona (Italy); Jonathan Montomoli, Dep. of Intensive Care, Hospital Infermi, Rimini Dep. of Intensive Care Medicine, Erasmus medical Center, Rotterdam, Netherlands; Maggie Cheng, Illinois Institute of Technology, USA; Farshad Firouzi, Duke University, USA

Short Description: During the COVID-19 emergency, intensive care achieved its limit, and the doctors were forced to choose their ICU patients who have the best chance for survival. This worldwide emergency highlighted the need to define a predictive care model capable of providing an accurate estimate of resources and preventive medicine. The analytical capability of machine learning (ML) methods has proven to be extremely accurate and in some cases superior to classical statistical approaches for solving this task. This WS aims to cover all aspects related to ML methodologies for providing risk profiles of the individual patients from which a different intensity of care can be deduced.

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Workshop #6: Real-world digital mobility assessment

Organisers: Andrea Cereatti, Politecnico di Torino; Silvia Del Din, Newcastle University; Felix Kluge, Friedrich-Alexander University Erlangen-Nürnberg

Short Description: Mobility is impaired in various chronic health conditions. The ongoing development of digital measures for mobility assessment using wearable inertial sensor systems aims at capturing real-world walking performance. This enables monitoring of health status, disease progression, and evaluation of interventions in a patient’s ecological environment. However, walking assessment in non-standardized environments poses challenges in terms of technology, usability, and validity of digital measures that can be used for disease stage quantification with the ultimate goal of regulatory approval.

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Workshop #7: AI4US: Unlocking the potential of Artificial Intelligence for Ultrasound image processing

Organisers: Sara Moccia, PhD – The BioRobotics Institute and Department of Excellence in Robotics and AI, Scuola Superiore Sant’Anna, Pisa, Italy –; Prof. Emilio Filippucci, MD, PhD – Rheumatology Unit, Department of Clinical and Molecular Sciences, “Carlo Urbani” Hospital, Jesi, Italy –; Maria Chiara Fiorentino – Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy –; Prof. Emanuele Frontoni, PhD – Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy –

Short Description: The goal of the AI4US workshop is to group expert AI researchers in US-image analysis to discuss the most recent research work and highlight current challenges and needs. AI4US aims at bridging the gap among universities, hospitals, enterprises, and stakeholders to draw a roadmap for future AI applications in the field.

The intended audience of AI4US ranges from PhD students and resident doctors that are approaching the challenges of US image analysis with AI, to experienced researchers that may be interested in knowing the latest breakthrough research. Graduate students in biomedical engineering, computer science and medicine may benefit from the invited speakers’ presentations highlighting the current research work being done on AI for US image analysis.

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Workshop #8: Soldier Digital Phenotyping

Organisers: Karl E. Friedl & Reed W. Hoyt, U.S. Army Research Institute of Environmental Medicine, Natick, Massachusetts, USA

Short Description: Real time physiological monitoring of individual soldiers (digital phenotyping) provides actionable information from wearable sensors, standoff detection, and contextual data (internet of soldier things) to inform virtual teammates (humanized technology), protect soldier health and performance, and provide decision support tools.  The development of sensor systems and algorithms for use in extreme and austere environments will be discussed.

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Clich HERE for the final agenda.

Workshop #9: Predicting quality of life with multimodal data

Organisers: Valeria De Luca, Novartis Institutes for Biomedical Research; Ieuan Clay, Evidation Health

Short Description: The field of digital health has become a multi-billion dollar market, powering a paradigm shift in the continuous capture of multimodal data including activity, sleep, heart rate variability and contextual information. Novel machine learning applications are pioneering the conversion of these multimodal data into measures of quality of life, capturing symptoms like fatigue, stress, and depression. These insights will result in better understanding of the patient’s lived experience and better medicines.

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