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Current status of clinical research using artificial intelligence techniques: A registry-based audit

BACKGROUND AND OBJECTIVES: Artificial intelligence (AI) as a field has recently gained a lot of importance and is expected to revolutionize the health-care scenario in the near future. There have been no studies done worldwide to review the status of research with respect to the use of AI in health...

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Autores principales: Karekar, Sonali Rajiv, Vazifdar, Arzan Khurshed
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Wolters Kluwer - Medknow 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8011515/
https://www.ncbi.nlm.nih.gov/pubmed/33816209
http://dx.doi.org/10.4103/picr.PICR_25_20
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author Karekar, Sonali Rajiv
Vazifdar, Arzan Khurshed
author_facet Karekar, Sonali Rajiv
Vazifdar, Arzan Khurshed
author_sort Karekar, Sonali Rajiv
collection PubMed
description BACKGROUND AND OBJECTIVES: Artificial intelligence (AI) as a field has recently gained a lot of importance and is expected to revolutionize the health-care scenario in the near future. There have been no studies done worldwide to review the status of research with respect to the use of AI in health care. Hence, we conceptualized this study to get an overview of the clinical studies being conducted in the field of AI, by analyzing those registered on the Food and Drug Administration trial registry website. METHODOLOGY: All the clinical studies conducted in the field of AI registered on the ClinicalTrials.gov website up to September 2019 were reviewed and analyzed. The variables such as geographical distribution, study design, status of study whether ongoing or completed, therapy area, type of intervention tested, type of funding, and year of initiation of study were recorded. The data were analyzed using descriptive statistics using SPSS for Windows, Version 16.0 (SPSS Inc. Chicago, IL, USA). RESULTS: Out of all the studies registered, 156 were related to AI. Of these 156 studies, 84 were interventional and 72 were observational. The most common therapy area under study was oncology with 26.3% studies, followed by cardiology, ophthalmology, psychiatry, and neurology. Devices comprised the most common intervention being studied, accounting to 34% of studies, followed by diagnostics which included 28% of studies. In the first 8 months of 2019 itself, 65 studies had been registered. CONCLUSION: The study revealed an increasing trend in the studies being conducted using AI techniques, with majority being conducted in the area of oncology, with medical devices being the most common intervention being tested.
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spelling pubmed-80115152021-04-01 Current status of clinical research using artificial intelligence techniques: A registry-based audit Karekar, Sonali Rajiv Vazifdar, Arzan Khurshed Perspect Clin Res Original Article BACKGROUND AND OBJECTIVES: Artificial intelligence (AI) as a field has recently gained a lot of importance and is expected to revolutionize the health-care scenario in the near future. There have been no studies done worldwide to review the status of research with respect to the use of AI in health care. Hence, we conceptualized this study to get an overview of the clinical studies being conducted in the field of AI, by analyzing those registered on the Food and Drug Administration trial registry website. METHODOLOGY: All the clinical studies conducted in the field of AI registered on the ClinicalTrials.gov website up to September 2019 were reviewed and analyzed. The variables such as geographical distribution, study design, status of study whether ongoing or completed, therapy area, type of intervention tested, type of funding, and year of initiation of study were recorded. The data were analyzed using descriptive statistics using SPSS for Windows, Version 16.0 (SPSS Inc. Chicago, IL, USA). RESULTS: Out of all the studies registered, 156 were related to AI. Of these 156 studies, 84 were interventional and 72 were observational. The most common therapy area under study was oncology with 26.3% studies, followed by cardiology, ophthalmology, psychiatry, and neurology. Devices comprised the most common intervention being studied, accounting to 34% of studies, followed by diagnostics which included 28% of studies. In the first 8 months of 2019 itself, 65 studies had been registered. CONCLUSION: The study revealed an increasing trend in the studies being conducted using AI techniques, with majority being conducted in the area of oncology, with medical devices being the most common intervention being tested. Wolters Kluwer - Medknow 2021 2021-01-08 /pmc/articles/PMC8011515/ /pubmed/33816209 http://dx.doi.org/10.4103/picr.PICR_25_20 Text en Copyright: © 2021 Perspectives in Clinical Research http://creativecommons.org/licenses/by-nc-sa/4.0 This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms.
spellingShingle Original Article
Karekar, Sonali Rajiv
Vazifdar, Arzan Khurshed
Current status of clinical research using artificial intelligence techniques: A registry-based audit
title Current status of clinical research using artificial intelligence techniques: A registry-based audit
title_full Current status of clinical research using artificial intelligence techniques: A registry-based audit
title_fullStr Current status of clinical research using artificial intelligence techniques: A registry-based audit
title_full_unstemmed Current status of clinical research using artificial intelligence techniques: A registry-based audit
title_short Current status of clinical research using artificial intelligence techniques: A registry-based audit
title_sort current status of clinical research using artificial intelligence techniques: a registry-based audit
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8011515/
https://www.ncbi.nlm.nih.gov/pubmed/33816209
http://dx.doi.org/10.4103/picr.PICR_25_20
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