Format

Send to

Choose Destination
Proc Natl Acad Sci U S A. 2016 Aug 2;113(31):8777-82. doi: 10.1073/pnas.1601827113. Epub 2016 Jul 18.

Boosting medical diagnostics by pooling independent judgments.

Author information

1
Center for Adaptive Rationality, Max Planck Institute for Human Development, 14195 Berlin, Germany; Department of Biology and Ecology of Fishes, Leibniz Institute of Freshwater Ecology and Inland Fisheries, 12587 Berlin, Germany; kurvers@mpib-berlin.mpg.de.
2
Center for Adaptive Rationality, Max Planck Institute for Human Development, 14195 Berlin, Germany;
3
Department of Biology and Ecology of Fishes, Leibniz Institute of Freshwater Ecology and Inland Fisheries, 12587 Berlin, Germany;
4
Department of Family Medicine, Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239; Department of Public Health & Preventive Medicine, Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239;
5
Group Health Research Institute, Seattle, WA 98101;
6
Department of Dermatology, Second University of Naples, 80131 Naples, Italy;
7
Department of Dermatology and Venerology, Medical University of Graz, 8036 Graz, Austria.

Abstract

Collective intelligence refers to the ability of groups to outperform individual decision makers when solving complex cognitive problems. Despite its potential to revolutionize decision making in a wide range of domains, including medical, economic, and political decision making, at present, little is known about the conditions underlying collective intelligence in real-world contexts. We here focus on two key areas of medical diagnostics, breast and skin cancer detection. Using a simulation study that draws on large real-world datasets, involving more than 140 doctors making more than 20,000 diagnoses, we investigate when combining the independent judgments of multiple doctors outperforms the best doctor in a group. We find that similarity in diagnostic accuracy is a key condition for collective intelligence: Aggregating the independent judgments of doctors outperforms the best doctor in a group whenever the diagnostic accuracy of doctors is relatively similar, but not when doctors' diagnostic accuracy differs too much. This intriguingly simple result is highly robust and holds across different group sizes, performance levels of the best doctor, and collective intelligence rules. The enabling role of similarity, in turn, is explained by its systematic effects on the number of correct and incorrect decisions of the best doctor that are overruled by the collective. By identifying a key factor underlying collective intelligence in two important real-world contexts, our findings pave the way for innovative and more effective approaches to complex real-world decision making, and to the scientific analyses of those approaches.

KEYWORDS:

collective intelligence; dermatology; groups; mammography; medical diagnostics

PMID:
27432950
PMCID:
PMC4978286
DOI:
10.1073/pnas.1601827113
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for HighWire Icon for PubMed Central
Loading ...
Support Center