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. 2022 Oct 21;19(20):13691.
doi: 10.3390/ijerph192013691.

Characteristics of Artificial Intelligence Clinical Trials in the Field of Healthcare: A Cross-Sectional Study on ClinicalTrials.gov

Anran Wang  1 , Xiaolei Xiu  1 , Shengyu Liu  1 , Qing Qian  1 , Sizhu Wu  1
Affiliations

Characteristics of Artificial Intelligence Clinical Trials in the Field of Healthcare: A Cross-Sectional Study on ClinicalTrials.gov

Anran Wang et al. Int J Environ Res Public Health. .

Abstract

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.

Keywords: ClinicalTrials.gov; artificial intelligence; clinical trials; healthcare; registry analysis.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Flowchart of trial selection.
Figure 2
Figure 2
Distribution of AI-related trials according to the posted year on ClinicalTrials.gov (n = 1725).
Figure 3
Figure 3
Part of the AI technology application graph. (a) The left figure shows the application of DL technology in different healthcare scenarios. (b) The right figure shows the situation of DL technology application in the diagnosis and screening field of various condition areas.
See this image and copyright information in PMC

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References

    1. Miller D.D., Brown E.W. Artificial Intelligence in Medical Practice: The Question to the Answer? Am. J. Med. 2018;131:129–133. doi: 10.1016/j.amjmed.2017.10.035. - DOI - PubMed
    1. Ahmed Z., Mohamed K., Zeeshan S., Dong X. Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. Database. 2020;2020:baaa010. doi: 10.1093/database/baaa010. - DOI - PMC - PubMed
    1. Coudray N., Ocampo P.S., Sakellaropoulos T., Narula N., Snuderl M., Fenyö D., Moreira A.L., Razavian N., Tsirigos A. Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning. Nat. Med. 2018;24:1559–1567. doi: 10.1038/s41591-018-0177-5. - DOI - PMC - PubMed
    1. Hesamian M.H., Jia W., He X., Kennedy P. Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges. J. Digit. Imaging. 2019;32:582–596. doi: 10.1007/s10278-019-00227-x. - DOI - PMC - PubMed
    1. Rodellar J., Alférez S., Acevedo A., Molina A., Merino A. Image processing and machine learning in the morphological analysis of blood cells. Int. J. Lab. Hematol. 2018;40:46–53. doi: 10.1111/ijlh.12818. - DOI - PubMed

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