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Applying advanced technologies to improve clinical trials: a systematic mapping study.

Esther Nanzayi Ngayua¹, Jianjia He¹, Kwabena Agyei-Boahene²

¹School of Business, University of Shanghai for Science and Technology, Shanghai, 200093 China.

|November 30, 2020

Summary

Advanced technologies like artificial intelligence (AI) and machine learning (ML) are increasingly used in clinical trials. Research shows growing interest, focusing on recruitment and eligibility, but gaps exist in architecture and metrics.

Keywords:

Artificial intelligence Clinical trials Deep learning Internet of things Machine learning

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Area of Science:

Clinical Trials
Health Informatics
Biotechnology

Background:

Clinical trials are crucial for evaluating new therapies, but face challenges with rising costs, extended timelines, and low productivity due to increasing data complexity.
Advanced technologies, including artificial intelligence (AI), machine learning (ML), deep learning (DL), and the internet of things (IoT), present opportunities to enhance clinical trial efficiency and productivity.

Purpose of the Study:

To systematically map and analyze existing research on the application of advanced technologies in clinical trials.
To identify trends, research focus areas, and gaps in the literature concerning AI, ML, DL, and IoT in clinical trial research.

Main Methods:

A systematic mapping study was conducted.
Literature search was restricted to articles published between 2010 and 2020.
A total of 443 relevant articles were identified and analyzed.

Main Results:

Research interest in advanced technologies for clinical trials has significantly increased between 2010 and 2020.
The primary focus of studies was on recruitment and eligibility aspects of clinical trials.
Most research involved validation and evaluation studies, with a notable gap in contributions related to architecture, process, and metrics.

Conclusions:

There is a clear upward trend in research applying AI, ML, DL, and IoT to clinical trials.
While current research primarily focuses on methods and theories, future work is expected to involve more empirical studies and address identified gaps in architectural and metric contributions.