Use of Artificial Intelligence for Medical Literature Search: Randomized Controlled Trial Using the Hackathon Format Article Swipe
Dominik S. Schoeb
,
Rodrigo Suarez-Ibarrola
,
Simon Hein
,
Franz F. Dressler
,
Fabian Adams
,
Daniel Schlager
,
Arkadiusz Miernik
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.2196/16606
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.2196/16606
Background Mapping out the research landscape around a project is often time consuming and difficult. Objective This study evaluates a commercial artificial intelligence (AI) search engine (IRIS.AI) for its applicability in an automated literature search on a specific medical topic. Methods To evaluate the AI search engine in a standardized manner, the concept of a science hackathon was applied. Three groups of researchers were tasked with performing a literature search on a clearly defined scientific project. All participants had a high level of expertise for this specific field of research. Two groups were given access to the AI search engine IRIS.AI. All groups were given the same amount of time for their search and were instructed to document their results. Search results were summarized and ranked according to a predetermined scoring system. Results The final scoring awarded 49 and 39 points out of 60 to AI groups 1 and 2, respectively, and the control group received 46 points. A total of 20 scientific studies with high relevance were identified, and 5 highly relevant studies (“spot on”) were reported by each group. Conclusions AI technology is a promising approach to facilitate literature searches and the management of medical libraries. In this study, however, the application of AI technology lead to a more focused literature search without a significant improvement in the number of results.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.2196/16606
- https://jmir.org/api/download?alt_name=ijmr_v9i1e16606_app1.pdf&filename=cd17a738ef79f95261f47b53b5b9cad9.pdf
- OA Status
- gold
- Cited By
- 36
- References
- 15
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2994977308
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2994977308Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.2196/16606Digital Object Identifier
- Title
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Use of Artificial Intelligence for Medical Literature Search: Randomized Controlled Trial Using the Hackathon FormatWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2019Year of publication
- Publication date
-
2019-12-15Full publication date if available
- Authors
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Dominik S. Schoeb, Rodrigo Suarez-Ibarrola, Simon Hein, Franz F. Dressler, Fabian Adams, Daniel Schlager, Arkadiusz MiernikList of authors in order
- Landing page
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https://doi.org/10.2196/16606Publisher landing page
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https://jmir.org/api/download?alt_name=ijmr_v9i1e16606_app1.pdf&filename=cd17a738ef79f95261f47b53b5b9cad9.pdfDirect link to full text PDF
- Open access
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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://jmir.org/api/download?alt_name=ijmr_v9i1e16606_app1.pdf&filename=cd17a738ef79f95261f47b53b5b9cad9.pdfDirect OA link when available
- Concepts
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Computer science, Search engine, Information retrieval, Relevance (law), Field (mathematics), Scientific literature, Artificial intelligence, Data science, Mathematics, Paleontology, Pure mathematics, Biology, Political science, LawTop concepts (fields/topics) attached by OpenAlex
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36Total citation count in OpenAlex
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2025: 8, 2024: 9, 2023: 8, 2022: 5, 2021: 5Per-year citation counts (last 5 years)
- References (count)
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15Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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