Community-engaged artificial intelligence research: A scoping review Article Swipe
Tyler J. Loftus
,
Jeremy A. Balch
,
Kenneth L. Abbott
,
Die Hu
,
Matthew M. Ruppert
,
Benjamin Shickel
,
Tezcan Ozrazgat‐Baslanti
,
Philip A. Efron
,
Patrick Tighe
,
William R. Hogan
,
Parisa Rashidi
,
Michelle I. Cardel
,
Gilbert R. Upchurch
,
Azra Bihorac
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pdig.0000561
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pdig.0000561
The degree to which artificial intelligence healthcare research is informed by data and stakeholders from community settings has not been previously described. As communities are the principal location of healthcare delivery, engaging them could represent an important opportunity to improve scientific quality. This scoping review systematically maps what is known and unknown about community-engaged artificial intelligence research and identifies opportunities to optimize the generalizability of these applications through involvement of community stakeholders and data throughout model development, validation, and implementation. Embase, PubMed, and MEDLINE databases were searched for articles describing artificial intelligence or machine learning healthcare applications with community involvement in model development, validation, or implementation. Model architecture and performance, the nature of community engagement, and barriers or facilitators to community engagement were reported according to PRISMA extension for Scoping Reviews guidelines. Of approximately 10,880 articles describing artificial intelligence healthcare applications, 21 (0.2%) described community involvement. All articles derived data from community settings, most commonly by leveraging existing datasets and sources that included community subjects, and often bolstered by internet-based data acquisition and subject recruitment. Only one article described inclusion of community stakeholders in designing an application–a natural language processing model that detected cases of likely child abuse with 90% accuracy using harmonized electronic health record notes from both hospital and community practice settings. The primary barrier to including community-derived data was small sample sizes, which may have affected 11 of the 21 studies (53%), introducing substantial risk for overfitting that threatens generalizability. Community engagement in artificial intelligence healthcare application development, validation, or implementation is rare. As healthcare delivery occurs primarily in community settings, investigators should consider engaging community stakeholders in user-centered design, usability, and clinical implementation studies to optimize generalizability.
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Metadata
- Type
- review
- Language
- en
- Landing Page
- https://doi.org/10.1371/journal.pdig.0000561
- https://journals.plos.org/digitalhealth/article/file?id=10.1371/journal.pdig.0000561&type=printable
- OA Status
- gold
- Cited By
- 10
- References
- 57
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4401809162
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4401809162Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1371/journal.pdig.0000561Digital Object Identifier
- Title
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Community-engaged artificial intelligence research: A scoping reviewWork title
- Type
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reviewOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-08-23Full publication date if available
- Authors
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Tyler J. Loftus, Jeremy A. Balch, Kenneth L. Abbott, Die Hu, Matthew M. Ruppert, Benjamin Shickel, Tezcan Ozrazgat‐Baslanti, Philip A. Efron, Patrick Tighe, William R. Hogan, Parisa Rashidi, Michelle I. Cardel, Gilbert R. Upchurch, Azra BihoracList of authors in order
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https://doi.org/10.1371/journal.pdig.0000561Publisher landing page
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https://journals.plos.org/digitalhealth/article/file?id=10.1371/journal.pdig.0000561&type=printableDirect link to full text PDF
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://journals.plos.org/digitalhealth/article/file?id=10.1371/journal.pdig.0000561&type=printableDirect OA link when available
- Concepts
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Generalizability theory, Overfitting, Computer science, Inclusion (mineral), Community engagement, Health care, Artificial intelligence, Data science, Community health, Knowledge management, Psychology, Public relations, Artificial neural network, Political science, Developmental psychology, Law, Social psychologyTop concepts (fields/topics) attached by OpenAlex
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10Total citation count in OpenAlex
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2025: 9, 2024: 1Per-year citation counts (last 5 years)
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57Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| corresponding_author_ids | https://openalex.org/A5016909804, https://openalex.org/A5046193991, https://openalex.org/A5108089209, https://openalex.org/A5003769975, https://openalex.org/A5007040136, https://openalex.org/A5031214939, https://openalex.org/A5053410431, https://openalex.org/A5000012641, https://openalex.org/A5019504069, https://openalex.org/A5041720255, https://openalex.org/A5042513858, https://openalex.org/A5076610873, https://openalex.org/A5084386025, https://openalex.org/A5056833297 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 14 |
| corresponding_institution_ids | https://openalex.org/I106165777, https://openalex.org/I2800717037, https://openalex.org/I33213144 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/10 |
| sustainable_development_goals[0].score | 0.44999998807907104 |
| sustainable_development_goals[0].display_name | Reduced inequalities |
| citation_normalized_percentile.value | 0.83817616 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |