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https://ceur-ws.org/Vol-3060/xpreface.pdf
Towards Smarter Health Care: Can Artificial
Intelligence Help?
Peter J. F. Lucas1 , Fabio A. Stella2
1
Department of Data Science, Faculty of EEMCS, University of Twente, Enschede, the Netherlands
2
Department of Informatics, Systems and Communication, University of Milan-Bicocca, Milan, Italy
1. Towards digital health care and health data science
Health care and medicine were one of the first areas where artificial intelligence (AI) was applied,
although initially with little impact on health care itself. Most of the impact of early AI in
Medicine (AIME) research was in terms of the development of new AI methods. Now, with the
increasing availability of health-care data, there is renewed interest in AIME, however, this time
with the promise of having impact on health care.
In particular, with the introduction of electronic health records (EHRs), health care is finally
catching up with the rest of society where digitization of core processes has become the
norm. The EHR has increased the availability of observational health-care data, that are highly
heterogeneous in nature and demand complex methods for their analysis. How to deal with
such health-care data and what can be achieved by their analyses is seen by many as a big
challenge.
2. Biomedical knowledge is key
At the same time, artificial intelligence research has made considerable progress, in particular in
tackling real-world problems. At the moment, a large number of AI researchers are focusing their
research on low-hanging fruit, such as applying deep learning methods to clinical diagnostic
imaging. However, medicine and health care are much more than just interpreting digital
images: it involves highly complex decision making to ensure that the trajectory a patient with
a disease needs to take for achieving a diagnosis, treatment, recovery, and final outcome is
optimal in some sense. As a consequence, researchers have to draw methods from the entire
field of AI, not just deep learning. In addition, health care data is usually problematic because of
failure of systematic coding, use of free text to describe essential aspects of the disease follow-up,
missing data, and lots of coding mistakes.
SMARTERCARE-2021: Workshop Towards Smarter Health Care: Can Artificial Intelligence Help?, November 29–11,
2021, Anywhere
Envelope-Open peter.lucas@utwente.nl (P. J. F. Lucas); fabio.stella@unimib.it (F. A. Stella)
GLOBE http://www.cs.ru.nl/~peterl/ (P. J. F. Lucas); https://www.unimib.it/fabio-antonio-stella (F. A. Stella)
Orcid 0000-0001-5454-2428 (P. J. F. Lucas); 0000-0002-1394-0507 (F. A. Stella)
© 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
Health care and medicine are built upon a rich body of knowledge, concerning the patho-
physiology of diseases, molecular, genetic, cytological, and histological characterization of
stages of a disease, described by temporal and spatial disease patterns. For example to help
patients with a chronic diseases managing their disorder and to prevent exacerbations, one
needs knowledge about common causes of an exacerbation, typical symptoms and signs, and
effective treatment to prevent or suppress the worsening of these signs and symptoms. Much
of this clinical knowledge is evidence based, based on research but unable to guarantee optimal
outcome. Nevertheless, clinical decisions on disease management are based on the best available
evidence and it makes sense to incorporate such knowledge when building AI solutions.
3. Scope of the workshop
Clinicians and health care researchers have recently spotted the potential of AI for clinical
decision making, clearly inspired by success stories from the popular press and novel health
care projects by Big Tech. This has created a new enthusiasm for medical AI in the health-care
community. The workshop builds on the rationale that learning from scratch is not possible at
the current state of the art, while model-based and knowledge-based methods have been shown
to support effectively analysis of data to address complex decision making problems in both
static and dynamic settings. Validity and usability, as well as ethical and legal implications, of
decision making based on models are also important issues in health care, in the sense that
models completely learnt from data are ill-justified, cannot be explained, and therefore hard
to accept by the health care community. The workshop offers a venue for researchers and
practitioners to show how model-based artificial intelligence, theory, models and algorithms
can provide help physicians and clinicians to make actionable and effective decisions.
Medicine and health care require highly complex decision making to ensure that the trajectory
a patient with a disease needs to take for diagnosis, treatment, recovery, and finally outcome is
optimal in some sense. As a consequence, researchers have to draw methods from the entire
field of AI. On the other hand, health care and medicine are built upon a rich body of knowledge,
e.g. concerning the pathophysiology of diseases, molecular, genetic, cytological, and histological
characterization of stages of a disease, described by temporal and spatial disease patterns. Such
knowledge can also act as background knowledge to guide machine learning.
This workshop aimed at elucidating the relationship between what can be expected from
AI methods when applied to health care problems and the role knowledge of health care and
clinical medicine can play in developing AI solutions to health care and clinical problems.
Aknowledgements
We wish to express our gratitude to the two invited speakers of the workshop, Agnieszka Onisko
(Bialystok University of Technology, Poland) and Anthony Hunter (UCL, UK) for their very
interesting talks, and the reviewers (Allan Tucker, Paola Cavalcante, Arjen Hommersom, Silvan
Quaglini, Luis Enrique Sucar, Szymon Wilk, Marek Druzdzel, Carlo Combi, Riccardo Bellazzi,
Elif Ozkirimli, Alessandro Bregoli, Federico Cabitza, Stephen Swift, Francesco Bellocchio, Luigi
Portinale, Gregor Štiglic, Alice Bernasconi, Pedro Pereira Rodrigues, Marco Scutari, Federico
Chesani) for providing valuable feedback to the authors of the papers included in the present
volume.