Development Pipeline and Geographic Representation of Trials for Artificial Intelligence/Machine Learning–Enabled Medical Devices (2010 to 2023) Article Swipe
Miquel Serra‐Burriel
,
Luca Locher
,
Kerstin Noëlle Vokinger
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1056/aipc2300038
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1056/aipc2300038
A high number of artificial intelligence/machine learning (AI/ML)-enabled medical devices are currently in development. To understand the development pipeline and worldwide geographic distribution of clinical trials for AI/ML-enabled medical devices that may enter the market in the upcoming years, we analyzed the trends in registration of clinical trials for AI/ML-enabled medical devices between 2010 and 2023 as well as their geographic distribution. We aggregated all registered trials initiated between January 1, 2010, and August 31, 2023, through the World Health Organization’s International Clinical Trials Registry Platform and included all clinical studies for AI/ML-enabled medical devices in our study cohort. Among the 710,800 registered clinical trials in this time period, 2669 clinical trials for AI/ML-enabled medical devices were identified and included in our study cohort. Of these, 2517 clinical trials provided information on the locations where the trial was conducted. Most of the trials were conducted for the medical specialties of radiology, general hospital, gastroenterology, and urology. Almost all were national trials; 1095 were conducted in China, followed by the United States (196), Japan (162), India (139), and Korea (118). The countries with the most enrolled patients in clinical trials per 100,000 inhabitants were mainly smaller countries in Asia and Europe. More international trials should be encouraged — including the involvement of low- and middle-income countries — to improve equality and ensure that the algorithms perform well across populations. (Funded by the Swiss National Science Foundation.)
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1056/aipc2300038
- https://ai.nejm.org/doi/pdf/10.1056/AIpc2300038
- OA Status
- bronze
- Cited By
- 13
- References
- 14
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388525160
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4388525160Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1056/aipc2300038Digital Object Identifier
- Title
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Development Pipeline and Geographic Representation of Trials for Artificial Intelligence/Machine Learning–Enabled Medical Devices (2010 to 2023)Work title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-11-09Full publication date if available
- Authors
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Miquel Serra‐Burriel, Luca Locher, Kerstin Noëlle VokingerList of authors in order
- Landing page
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https://doi.org/10.1056/aipc2300038Publisher landing page
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https://ai.nejm.org/doi/pdf/10.1056/AIpc2300038Direct link to full text PDF
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YesWhether a free full text is available
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bronzeOpen access status per OpenAlex
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https://ai.nejm.org/doi/pdf/10.1056/AIpc2300038Direct OA link when available
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Clinical trial, Medicine, Cohort, Artificial intelligence, Family medicine, Pathology, Computer scienceTop concepts (fields/topics) attached by OpenAlex
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13Total citation count in OpenAlex
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2025: 8, 2024: 5Per-year citation counts (last 5 years)
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14Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.Registry | 84 |
| abstract_inverted_index.analyzed | 40 |
| abstract_inverted_index.clinical | 24, 46, 89, 103, 110, 127, 187 |
| abstract_inverted_index.enrolled | 184 |
| abstract_inverted_index.equality | 218 |
| abstract_inverted_index.followed | 166 |
| abstract_inverted_index.included | 87, 119 |
| abstract_inverted_index.learning | 6 |
| abstract_inverted_index.national | 159 |
| abstract_inverted_index.patients | 185 |
| abstract_inverted_index.pipeline | 18 |
| abstract_inverted_index.provided | 129 |
| abstract_inverted_index.upcoming | 37 |
| abstract_inverted_index.urology. | 155 |
| abstract_inverted_index.conducted | 144, 163 |
| abstract_inverted_index.countries | 180, 195, 214 |
| abstract_inverted_index.currently | 11 |
| abstract_inverted_index.hospital, | 152 |
| abstract_inverted_index.including | 207 |
| abstract_inverted_index.initiated | 67 |
| abstract_inverted_index.locations | 133 |
| abstract_inverted_index.worldwide | 20 |
| abstract_inverted_index.aggregated | 63 |
| abstract_inverted_index.algorithms | 223 |
| abstract_inverted_index.artificial | 4 |
| abstract_inverted_index.conducted. | 138 |
| abstract_inverted_index.encouraged | 205 |
| abstract_inverted_index.geographic | 21, 60 |
| abstract_inverted_index.identified | 117 |
| abstract_inverted_index.radiology, | 150 |
| abstract_inverted_index.registered | 65, 102 |
| abstract_inverted_index.understand | 15 |
| abstract_inverted_index.development | 17 |
| abstract_inverted_index.information | 130 |
| abstract_inverted_index.inhabitants | 191 |
| abstract_inverted_index.involvement | 209 |
| abstract_inverted_index.specialties | 148 |
| abstract_inverted_index.Foundation.) | 234 |
| abstract_inverted_index.development. | 13 |
| abstract_inverted_index.distribution | 22 |
| abstract_inverted_index.populations. | 227 |
| abstract_inverted_index.registration | 44 |
| abstract_inverted_index.AI/ML-enabled | 27, 49, 92, 113 |
| abstract_inverted_index.International | 81 |
| abstract_inverted_index.distribution. | 61 |
| abstract_inverted_index.international | 201 |
| abstract_inverted_index.middle-income | 213 |
| abstract_inverted_index.(AI/ML)-enabled | 7 |
| abstract_inverted_index.Organization’s | 80 |
| abstract_inverted_index.gastroenterology, | 153 |
| abstract_inverted_index.intelligence/machine | 5 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 98 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.73374175 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |