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PLoS One. 2015 Aug 12;10(8):e0134269. doi: 10.1371/journal.pone.0134269. eCollection 2015.

Collective intelligence meets medical decision-making: the collective outperforms the best radiologist.

Author information

1
Leibniz Institute of Freshwater Ecology and Inland Fisheries, Müggelseedamm 310, 12587, Berlin, Germany.
2
Leibniz Institute of Freshwater Ecology and Inland Fisheries, Müggelseedamm 310, 12587, Berlin, Germany; Faculty of Life Sciences, Humboldt-University of Berlin, Berlin, Germany.
3
Departments of Family Medicine and Pubic Health & Preventive Medicine, Knight Cancer Institute, Oregon Health & Science University, 3181 S.W. Sam Jackson Park Road, Portland, Oregon, United States of America.
4
RAND Corporation, 1776 Main Street, Santa Monica, CA, 90407-2138, United States of America.

Abstract

While collective intelligence (CI) is a powerful approach to increase decision accuracy, few attempts have been made to unlock its potential in medical decision-making. Here we investigated the performance of three well-known collective intelligence rules ("majority", "quorum", and "weighted quorum") when applied to mammography screening. For any particular mammogram, these rules aggregate the independent assessments of multiple radiologists into a single decision (recall the patient for additional workup or not). We found that, compared to single radiologists, any of these CI-rules both increases true positives (i.e., recalls of patients with cancer) and decreases false positives (i.e., recalls of patients without cancer), thereby overcoming one of the fundamental limitations to decision accuracy that individual radiologists face. Importantly, we find that all CI-rules systematically outperform even the best-performing individual radiologist in the respective group. Our findings demonstrate that CI can be employed to improve mammography screening; similarly, CI may have the potential to improve medical decision-making in a much wider range of contexts, including many areas of diagnostic imaging and, more generally, diagnostic decisions that are based on the subjective interpretation of evidence.

PMID:
26267331
PMCID:
PMC4534443
DOI:
10.1371/journal.pone.0134269
[Indexed for MEDLINE]
Free PMC Article

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