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Unity Is Intelligence: A Collective Intelligence Experiment on ECG Reading to Improve Diagnostic Performance in Cardiology

1
Dipartimento di Informatica, Sistemistica e Comunicazione, University of Milano-Bicocca, Viale Sarca 336, 20126 Milan, Italy
2
IRCCS MultiMedica, Sesto San Giovanni, 20099 Milan, Italy
*
Author to whom correspondence should be addressed.
Received: 17 December 2020 / Revised: 21 February 2021 / Accepted: 9 March 2021 / Published: 1 April 2021
Medical errors have a huge impact on clinical practice in terms of economic and human costs. As a result, technology-based solutions, such as those grounded in artificial intelligence (AI) or collective intelligence (CI), have attracted increasing interest as a means of reducing error rates and their impacts. Previous studies have shown that a combination of individual opinions based on rules, weighting mechanisms, or other CI solutions could improve diagnostic accuracy with respect to individual doctors. We conducted a study to investigate the potential of this approach in cardiology and, more precisely, in electrocardiogram (ECG) reading. To achieve this aim, we designed and conducted an experiment involving medical students, recent graduates, and residents, who were asked to annotate a collection of 10 ECGs of various complexity and difficulty. For each ECG, we considered groups of increasing size (from three to 30 members) and applied three different CI protocols. In all cases, the results showed a statistically significant improvement (ranging from 9% to 88%) in terms of diagnostic accuracy when compared to the performance of individual readers; this difference held for not only large groups, but also smaller ones. In light of these results, we conclude that CI approaches can support the tasks mentioned above, and possibly other similar ones as well. We discuss the implications of applying CI solutions to clinical settings, such as cases of augmented ‘second opinions’ and decision-making. View Full-Text
Keywords: collective intelligence; ECG reading; medical decision support; diagnostic error collective intelligence; ECG reading; medical decision support; diagnostic error
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MDPI and ACS Style

Ronzio, L.; Campagner, A.; Cabitza, F.; Gensini, G.F. Unity Is Intelligence: A Collective Intelligence Experiment on ECG Reading to Improve Diagnostic Performance in Cardiology. J. Intell. 2021, 9, 17. https://0-doi-org.brum.beds.ac.uk/10.3390/jintelligence9020017

AMA Style

Ronzio L, Campagner A, Cabitza F, Gensini GF. Unity Is Intelligence: A Collective Intelligence Experiment on ECG Reading to Improve Diagnostic Performance in Cardiology. Journal of Intelligence. 2021; 9(2):17. https://0-doi-org.brum.beds.ac.uk/10.3390/jintelligence9020017

Chicago/Turabian Style

Ronzio, Luca, Andrea Campagner, Federico Cabitza, and Gian F. Gensini 2021. "Unity Is Intelligence: A Collective Intelligence Experiment on ECG Reading to Improve Diagnostic Performance in Cardiology" Journal of Intelligence 9, no. 2: 17. https://0-doi-org.brum.beds.ac.uk/10.3390/jintelligence9020017

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