Ngayua, Esther Nanzayi 
ORCID: 0000-0001-5161-158X, He, Jianjia ORCID: 0000-0001-5987-3605 and Agyei-Boahene, Kwabena ORCID: 0000-0003-1799-071X
(2021)
Applying advanced technologies to improve clinical trials: a systematic mapping study.
Scientometrics, 126 (2).
pp. 1217-1238.
Abstract
The increasing demand for new therapies and other clinical interventions has made researchers conduct many clinical trials. The high level of evidence generated by clinical trials makes them the main approach to evaluating new clinical interventions. The increasing amounts of data to be considered in the planning and conducting of clinical trials has led to higher costs and increased timelines of clinical trials, with low productivity. Advanced technologies including artificial intelligence, machine learning, deep learning, and the internet of things offer an opportunity to improve the efficiency and productivity of clinical trials at various stages. Although researchers have done some tangible work regarding the application of advanced technologies in clinical trials, the studies are yet to be mapped to give a general picture of the current state of research. This systematic mapping study was conducted to identify and analyze studies published on the role of advanced technologies in clinical trials. A search restricted to the period between 2010 and 2020 yielded a total of 443 articles. The analysis revealed a trend of increasing research interests in the area over the years. Recruitment and eligibility aspects were the main focus of the studies. The main research types were validation and evaluation studies. Most studies contributed methods and theories, hence there exists a gap for architecture, process, and metric contributions. In the future, more empirical studies are expected given the increasing interest to implement the AI, ML, DL, and IoT in clinical trials.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Artificial intelligence, Clinical trials, Deep learning, Internet of things, Machine learning |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 09 Dec 2020 10:29 |
| Last Modified: | 18 Mar 2024 01:06 |
| DOI: | 10.1007/s11192-020-03774-1 |
| Open Access URL: | http://10.0.3.239/s11192-020-03774-1 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3109736 |
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