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

Abstract

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.

Keywords

• data mining
• deep learning
• machine learning
• trial registry