Canada

Healthcare systems are struggling with sustainability and accessibility to populations, worldwide. As resources are finite, the concern with identifying treatments that bring the higher value to patients and to society is rising a global movement of Value-based Healthcare (VBHC). Measuring outcomes and costs to achieve those outcomes is a starting point to measure value in Health. Remote patient monitoring technologies and data science are very relevant to capture outcomes that are relevant to patients and to make healthcare more accessible and sustainable, as also more accountable.I will present the topic of VBHC and introduce to opportunities for biomedical engineers to contribute for the development of technologies of high-value in healthcare. Two case-studies will be presented related to the contexts of cardiac surgery and emergency hospital services.

BIOSTEC 2022

Value-based Healthcare: Contributions from Biomedical Engineering

AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). The aim of the joint conference is to provide a single, high-profile, internationally-respected archival forum for scientific research in the theory and practice of autonomous agents and multi-agent systems.

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AAMAS 2020

AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). 

keynote

The purpose of BIOSTEC is to bring together researchers and practitioners, including engineers, biologists, health professionals and informatics/computer scientists, interested in both theoretical advances and applications of information systems, artificial intelligence, signal processing, electronics and other engineering tools in knowledge areas related to biology and medicine.

Achieving a pragmatic, or even an ecological validation (Cabitza and Zeitoun, 2019) of medical AI systems that nevertheless exhibit very high (statistical) accuracy has been observed to be more complicated than initially expected (Coiera et al. 2018): in fact, most of the challenges that make technically sound systems perform poorly in real-world settings lie in the so called “last mile of implementation” (Coiera, 2019). This evocative concept expresses the semantic difference between developing medical machine learning (or medical AI) and the mere application of machine learning techniques to medical data. Moreover, we will make the point that the space bewtween machine learning development and clinical practice, is not a flat and regular path, but rather presents two chasms: the chasm of human trust, and the chasm of machine experience. The former one requires to focus on usability and explainability, while the latter ones requires data governance and to focus on data work, including practice of “data awareness” and “data hygiene”. I will discuss these notions, and report about some researches I personally conducted while trying to bridge the above chasms with mixed fortunes: what we recognize as still open problems are exciting opportunities to look at a seemingly established field from a fresh perspective (the interactionist perspective) and develop solutions that focus on the utility of the technology rather than following the mirage of accuracy.

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Wirewalking over Two Medical AI Chasms: Results and Open Problems in Making "Valid AI" Also Useful in Medical Practice

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