ArticlePloS one2015
Collective intelligence meets medical decision-making: the collective outperforms the best radiologist.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
56 citing papers in PubMed.
- A scoping review of crowdsourcing and collective intelligence in oral healthcare.BMC oral health · 2026Article
- Development and Evaluation of a Rural Longitudinal Neonatal Resuscitation Program Telesimulation Program (MOOSE: Maine Ongoing Outreach Simulation Education).American journal of perinatology · 2025Article
- Incorporating label uncertainty during the training of convolutional neural networks improves performance for the discrimination between certain and inconclusive cases in dopamine transporter SPECT.European journal of nuclear medicine and molecular imaging · 2025Article
- Panoramic radiographic features for machine learning based detection of mandibular third molar root and inferior alveolar canal contact.Scientific reports · 2025Article
- Explainable AI in medicine: challenges of integrating XAI into the future clinical routine.Frontiers in radiology · 2025Review
- Empowering minorities and everyone in participatory budgeting: an agent-based modelling perspective.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2024Article
- AI-enhanced collective intelligence.Patterns (New York, N.Y.) · 2024Review
- Humans flexibly integrate social information despite interindividual differences in reward.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Gamified Crowdsourcing as a Novel Approach to Lung Ultrasound Data Set Labeling: Prospective Analysis.Journal of medical Internet research · 2024Article
- Boosting wisdom of the crowd for medical image annotation using training performance and task features.Cognitive research: principles and implications · 2024Article
- Collective Intelligence Increases Diagnostic Accuracy in a General Practice Setting.Medical decision making : an international journal of the Society for Medical Decision Making · 2024Article
- Terminology of e-Oral Health: Consensus Report of the IADR's e-Oral Health Network Terminology Task Force.BMC oral health · 2024Article
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- Remote Surgical Discussion of Multivessel Coronary Artery Disease Patients without Surgery on Site-Retrospective Insights.Journal of clinical medicine · 2023Article
- Human-machine cooperation meta-model for clinical diagnosis by adaptation to human expert's diagnostic characteristics.Scientific reports · 2023Article
- Automating hybrid collective intelligence in open-ended medical diagnostics.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Experimental evidence for structured information-sharing networks reducing medical errors.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Harnessing the power of collective intelligence in dentistry: a pilot study in Victoria, Australia.BMC oral health · 2023Article
- Building public trust and confidence in secondary use of health data for healthcare improvement and research: a qualitative study pre-protocol.HRB open research · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.