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

Autor/inn/en	Ronzio, Luca; Campagner, Andrea; Cabitza, Federico; Gensini, Gian Franco
Titel	Unity Is Intelligence: A Collective Intelligence Experiment on ECG Reading to Improve Diagnostic Performance in Cardiology
Quelle	In: Journal of Intelligence, 9 (2021), Artikel 17 (18 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	ORCID (Ronzio, Luca) ORCID (Cabitza, Federico)
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2079-3200
Schlagwörter	Medicine; Equipment; Clinical Diagnosis; Medical Services; Error Patterns; Intelligence; Artificial Intelligence; Accuracy; Decision Making + Suchen Sie Ihr Suchwort? Medizin; Arzt; Fehlertyp; Intelligenz; Klugheit; Künstliche Intelligenz; Decision-making; Entscheidungsfindung
Abstract	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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01