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Dr. Nico Bruining
Dr. Paul Cummins
Dr. Joost Daemen
As discussed in the introduction editorial of the first issue of the Journal of ESC Digital Health (JEDH), artificial intelligence (AI) has been chosen as the theme for this issue as our Editorial Board considers this topic to be a prime characterisation of the digital revolution in medicine.
The first curated paper, published in the European Heart Journal, is ‘deep learning for cardiovascular medicine: a practical primer’ by the group Krittanawong et al (3). This article reviews the current state of deep learning (DL) for the cardiovascular clinical and scientific communities by addressing the technical conditions to grasp and understand both the affirmative potentials and the difficult challenges whilst considering the myriad opportunities in this new, appealing field. Figure 1 illustrates nicely the association of deep learning with clinical and translational medicine.
The second paper, by Rajkomar et al., discusses the convenience of deploying AI for general medicine by delineating how valuable AI could be in clinical decision-making to support capitalising on all available patient data (4). A paper by Johnson et al., recently published in the Journal of the American College of Cardiology, links three distinct areas in cardiovascular medicine: namely research and development, clinical practice, and population health (5).
In concurrence with the aforementioned papers, AI relies on statistics in order to execute predictive analytics. The paper by Goldstein et al. (6) – moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges – delves deeper into the statistical modelling approach necessary to move forward in this AI era. Figure 2 provides a good illustration of this. Eric Boersma provides an interesting perspective.
Imaging modalities have become decisive diagnostic workhorses within cardiovascular treatment strategies. Our colleagues within radiology had the agile foresight to recognise the capabilities of AI within their specialty. Their experiences and learnings have traversed into cardiovascular imaging as deliberated in review by Al’Aref et al (7). This analysis is accompanied by an opinion piece from Filippo Cademartiri.
Whilst acknowledging that the adult heart is a complex structure, congenital pathologies are inherently more complex. With this in mind, and taking a modest step outside the AI-themed issue, the board could not resist the temptation to select the following paper-based on holography recently published by Brun et al. (8) (figure 3). Although holograms are ‘trending’, are they an effective tool in clinical practice? Ad Bogers seeks to address this contemporary question. A nice link with congenital diseases, big data, and machine learning is the paper by Diller et al.. (9) which illuminates the benefits of these new technologies.
The previously highlighted papers focus on the interactions on a patient level. Parallel to these advances in patient care is the cautious concern whether automation technologies will result in physician job losses. This has led to an ongoing yet lively debate within the radiology arena. Shameer et al. positions these discussions in a broader perspective with the paper titled, Machine Learning in Cardiovascular Medicine: Are We There Yet? (10) Quer et al. complements this debate with the paper: Augmenting Diagnostic Vision with AI (11).
Somewhat contrasting but interesting, nonetheless, is the paper: Pharma Blockchains AI for Drug Development. This report by the group Burki et al. examines the evolution in performing pharmaceutical trials for new drug developments from the classic formats using animals and humans to in-silico configurations (in-silico defined as solely computer-based) (12). Last but not least are the ethical concerns when employing big data, machine- and deep-learning algorithms, a topic addressed by Prabhu S.P. (13).
While working on the portal and this editorial, we stumbled by chance on a thesis written in 1980 with the title: Computer Analysis of Cardiac Catheterization Data by G.T. Meester, Rotterdam, The Netherlands. He described such automated systems as useful, improving the quality of cardiac catheterisation and providing the clinician with relevant data that would not otherwise be available. He concluded his thesis with an agreeable quote from John Benjamin Murphy (1857–1916), a quote which today is especially pertinent for digital health technologies: ‘The patient is the center of our medical universe, around which all our work revolves, and towards which all our efforts tend.’
Editorial on Krittanawong C, Johnson KW, Rosenson RS, Wang Z, Aydar M, Baber U, Min JK, Tang WHW, Halperin JL, Narayan SM. Deep learning for cardiovascular medicine: a practical primer. Eur Heart J 2019;40(25):2058-2073.
Declaration of Interests: The author(s) have declared no conflicts of interest.
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