JMIR Publications

ABSTRACT

Background:

Artificially-intelligent self-diagnosing digital platforms are becoming widely available and used by the general population. Little is known about the body of knowledge surrounding this technology.

Objective:

The objectives of this scoping review are: 1) to systematically map the extent and nature of the literature and topic areas pertaining to digital platforms that use computerized algorithms to provide a list of potential diagnoses and 2) to identify key knowledge gaps.

Methods:

The following databases were searched: ACM, IEEE, Google Scholar, Open Grey, ProQuest Dissertations and Theses. The search strategy was developed and refined with the assistance of a librarian and consisted of three main concepts: 1) self-diagnosis; 2) digital platforms; 3) public or patients. The search generated 2,536 articles from which 217 were duplicates. Following the Tricco et al. 2018 checklist, two researchers screened the titles and abstracts (n=2,316) and full-texts (n=104), independently. A total of 19 articles were included for review and data were retrieved following a data-charting form that was pre-tested by the research team.

Results:

Included articles were mainly conducted in the US (n=10) or the UK (n=4). Among the articles, topic areas included: accuracy or correspondence with a doctor’s diagnosis (n=6), commentaries (n=2), regulation (n=3), sociological (n=2), user experience (n=2), theoretical (n=1), privacy and security (n=1), ethical (n=1), design (n=1). Individuals who do not have access to health care and perceive to have a stigmatizing condition are more likely to use this technology. The accuracy of this technology to provide a correct first diagnosis varied substantially based on the disease examined and platform used. Factors influencing accuracy include the design of the online platform and sociodemographic profile of the user. Regulation of this technology is lacking in most parts of the world; however, they are currently under development.

Conclusions:

There are prominent research gaps in the literature surrounding the use of self-diagnosing AI digital platforms. Given the variety of digital platforms and the types of diseases they cover, measuring accuracy is cumbersome. More research is needed to inform regulations and to consider user experience.