Cargando…
Characteristics of Artificial Intelligence Clinical Trials in the Field of Healthcare: A Cross-Sectional Study on ClinicalTrials.gov
Artificial intelligence (AI) has driven innovative transformation in healthcare service patterns, despite a lack of understanding of its performance in clinical practice. We conducted a cross-sectional analysis of AI-related trials in healthcare based on ClinicalTrials.gov, intending to investigate...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9602501/ https://www.ncbi.nlm.nih.gov/pubmed/36294269 http://dx.doi.org/10.3390/ijerph192013691 |
_version_ | 1784817333705900032 |
---|---|
author | Wang, Anran Xiu, Xiaolei Liu, Shengyu Qian, Qing Wu, Sizhu |
author_facet | Wang, Anran Xiu, Xiaolei Liu, Shengyu Qian, Qing Wu, Sizhu |
author_sort | Wang, Anran |
collection | PubMed |
description | Artificial intelligence (AI) has driven innovative transformation in healthcare service patterns, despite a lack of understanding of its performance in clinical practice. We conducted a cross-sectional analysis of AI-related trials in healthcare based on ClinicalTrials.gov, intending to investigate the trial characteristics and AI’s development status. Additionally, the Neo4j graph database and visualization technology were employed to construct an AI technology application graph, achieving a visual representation and analysis of research hotspots in healthcare AI. A total of 1725 eligible trials that were registered in ClinicalTrials.gov up to 31 March 2022 were included in this study. The number of trial registrations has dramatically grown each year since 2016. However, the AI-related trials had some design drawbacks and problems with poor-quality result reporting. The proportion of trials with prospective and randomized designs was insufficient, and most studies did not report results upon completion. Currently, most healthcare AI application studies are based on data-driven learning algorithms, covering various disease areas and healthcare scenarios. As few studies have publicly reported results on ClinicalTrials.gov, there is not enough evidence to support an assessment of AI’s actual performance. The widespread implementation of AI technology in healthcare still faces many challenges and requires more high-quality prospective clinical validation. |
format | Online Article Text |
id | pubmed-9602501 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96025012022-10-27 Characteristics of Artificial Intelligence Clinical Trials in the Field of Healthcare: A Cross-Sectional Study on ClinicalTrials.gov Wang, Anran Xiu, Xiaolei Liu, Shengyu Qian, Qing Wu, Sizhu Int J Environ Res Public Health Article Artificial intelligence (AI) has driven innovative transformation in healthcare service patterns, despite a lack of understanding of its performance in clinical practice. We conducted a cross-sectional analysis of AI-related trials in healthcare based on ClinicalTrials.gov, intending to investigate the trial characteristics and AI’s development status. Additionally, the Neo4j graph database and visualization technology were employed to construct an AI technology application graph, achieving a visual representation and analysis of research hotspots in healthcare AI. A total of 1725 eligible trials that were registered in ClinicalTrials.gov up to 31 March 2022 were included in this study. The number of trial registrations has dramatically grown each year since 2016. However, the AI-related trials had some design drawbacks and problems with poor-quality result reporting. The proportion of trials with prospective and randomized designs was insufficient, and most studies did not report results upon completion. Currently, most healthcare AI application studies are based on data-driven learning algorithms, covering various disease areas and healthcare scenarios. As few studies have publicly reported results on ClinicalTrials.gov, there is not enough evidence to support an assessment of AI’s actual performance. The widespread implementation of AI technology in healthcare still faces many challenges and requires more high-quality prospective clinical validation. MDPI 2022-10-21 /pmc/articles/PMC9602501/ /pubmed/36294269 http://dx.doi.org/10.3390/ijerph192013691 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Wang, Anran Xiu, Xiaolei Liu, Shengyu Qian, Qing Wu, Sizhu Characteristics of Artificial Intelligence Clinical Trials in the Field of Healthcare: A Cross-Sectional Study on ClinicalTrials.gov |
title | Characteristics of Artificial Intelligence Clinical Trials in the Field of Healthcare: A Cross-Sectional Study on ClinicalTrials.gov |
title_full | Characteristics of Artificial Intelligence Clinical Trials in the Field of Healthcare: A Cross-Sectional Study on ClinicalTrials.gov |
title_fullStr | Characteristics of Artificial Intelligence Clinical Trials in the Field of Healthcare: A Cross-Sectional Study on ClinicalTrials.gov |
title_full_unstemmed | Characteristics of Artificial Intelligence Clinical Trials in the Field of Healthcare: A Cross-Sectional Study on ClinicalTrials.gov |
title_short | Characteristics of Artificial Intelligence Clinical Trials in the Field of Healthcare: A Cross-Sectional Study on ClinicalTrials.gov |
title_sort | characteristics of artificial intelligence clinical trials in the field of healthcare: a cross-sectional study on clinicaltrials.gov |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9602501/ https://www.ncbi.nlm.nih.gov/pubmed/36294269 http://dx.doi.org/10.3390/ijerph192013691 |
work_keys_str_mv | AT wanganran characteristicsofartificialintelligenceclinicaltrialsinthefieldofhealthcareacrosssectionalstudyonclinicaltrialsgov AT xiuxiaolei characteristicsofartificialintelligenceclinicaltrialsinthefieldofhealthcareacrosssectionalstudyonclinicaltrialsgov AT liushengyu characteristicsofartificialintelligenceclinicaltrialsinthefieldofhealthcareacrosssectionalstudyonclinicaltrialsgov AT qianqing characteristicsofartificialintelligenceclinicaltrialsinthefieldofhealthcareacrosssectionalstudyonclinicaltrialsgov AT wusizhu characteristicsofartificialintelligenceclinicaltrialsinthefieldofhealthcareacrosssectionalstudyonclinicaltrialsgov |