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Objective <p>Diagnostic and treatment errors are detrimental yet relatively common in primary healthcare settings. Crowdsourcing and collective intelligence (CI) in the form of peers’ opinion and group decision-making can lead to modifications in diagnosis and treatment planning by dentists. In this review, it was explored their use in oral healthcare delivery, investigating specific applications and evaluating their merits and drawbacks in oral healthcare.</p> Methods <p>A scoping review approach was chosen as the optimal study design, due to the complexity of the topic. A literature search from 1980 to December 2024 using selected search terms and a search strategy was implemented across six databases: Medline, Scopus, Web of Science, SciELO, LILACS, and CINAHL. Inclusion criteria specified studies published in English, Spanish, and Portuguese, focusing on CI applications in oral healthcare settings.</p> Results <p>Of the 265 identified studies, 63 abstracts and 20 full-text studies were screened, and 7 studies were included in the qualitative synthesis. The findings suggest that CI may significantly enhance patient management in primary healthcare settings by employing various group decision-making models such as multidisciplinary teams (MDTs) and the Delphi method. MDTs, comprising professionals from different disciplines, can enhance patient care through collaborative problem-solving and holistic approaches. The Delphi method allows for reliable, expert-driven decisions through anonymous feedback rounds. CI not only addresses diagnosis and treatment planning challenges but also improves resource allocation. While these approaches can reduce individual biases and systematic errors, challenges such as conformity bias and groupthink were also acknowledged.</p> Conclusion <p>This review highlights the significance of diverse expert opinions and structured consensus-building in advancing collective intelligence in oral healthcare. It emphasizes CI’s potential to improve patient management through collaborative decision-making models. However, further research is necessary to explore CI’s specific applications and effectiveness in oral healthcare.</p>

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A scoping review of crowdsourcing and collective intelligence in oral healthcare

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月14日

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  • Rodrigo J Mariño,
  • Max Ganhewa,
  • Alison Lau,
  • Randal von Marttens,
  • Nicola Cirillo

摘要

Objective

Diagnostic and treatment errors are detrimental yet relatively common in primary healthcare settings. Crowdsourcing and collective intelligence (CI) in the form of peers’ opinion and group decision-making can lead to modifications in diagnosis and treatment planning by dentists. In this review, it was explored their use in oral healthcare delivery, investigating specific applications and evaluating their merits and drawbacks in oral healthcare.

Methods

A scoping review approach was chosen as the optimal study design, due to the complexity of the topic. A literature search from 1980 to December 2024 using selected search terms and a search strategy was implemented across six databases: Medline, Scopus, Web of Science, SciELO, LILACS, and CINAHL. Inclusion criteria specified studies published in English, Spanish, and Portuguese, focusing on CI applications in oral healthcare settings.

Results

Of the 265 identified studies, 63 abstracts and 20 full-text studies were screened, and 7 studies were included in the qualitative synthesis. The findings suggest that CI may significantly enhance patient management in primary healthcare settings by employing various group decision-making models such as multidisciplinary teams (MDTs) and the Delphi method. MDTs, comprising professionals from different disciplines, can enhance patient care through collaborative problem-solving and holistic approaches. The Delphi method allows for reliable, expert-driven decisions through anonymous feedback rounds. CI not only addresses diagnosis and treatment planning challenges but also improves resource allocation. While these approaches can reduce individual biases and systematic errors, challenges such as conformity bias and groupthink were also acknowledged.

Conclusion

This review highlights the significance of diverse expert opinions and structured consensus-building in advancing collective intelligence in oral healthcare. It emphasizes CI’s potential to improve patient management through collaborative decision-making models. However, further research is necessary to explore CI’s specific applications and effectiveness in oral healthcare.