JMIR Publications

ABSTRACT

Background:

Artificial intelligence (AI) was coined in the 1950s. Since then, AI has made its way into many industries in several waves and has become more mainstream in parallel with the increase in hardware’s computing power. However, AI in medicine lags behind other industries. In recent years AI in medicine has gained massive attention among researchers and practitioners, and thus, the studies regarding medical AI have shown expositional growth.

Objective:

Given that there was no current framework as a blueprint guideline, this research aimed to map the current research status and future trends in medical AI by screening all the AI-related studies within PubMed in the past two decades. This article also provided possible approaches to acquiring data and analyzing it via Python in similar studies in the future.

Methods:

Our approach was twofold: (1) we obtained the metadata of the publications on AI from PubMed between 2000 and 2021 via Python, including titles, abstracts, author names, journal names, and publishing year, and then conducted the keyword occurrences counting; and (2) we classified the relevant topics by unsupervised machine learning approach – latent Dirichlet allocation (LDA) and identified the research scope on AI in medicine. As there is no universal medical AI taxonomy, we generated an AI dictionary based on the European Commission Joint Research Centre (JRC) AI Watch report.

Results:

A total of 249,243 eligible publications were included in this research. The result showed exponential growth of medical AI research on PubMed. Articles in the (machine) learning domain constantly account for half of all AI research. The number of research in communication, integration and interaction, and service has also increased over the past years, but the overall shares remain similar during the past decades. In the reasoning domain, research articles increased every year. However, the share dropped significantly. AI ethics appeared to be the new research area based on increased published articles.

Conclusions:

Along with the increasing number of studies on AI each year, the research topics have shown different trends. Machine learning was the focus of medical AI research and would still be the center of future research. Deep learning will continue to play a crucial role in AI in medicine. With the help of predictive algorithms, pattern recognition power, and imaging analysis capabilities, research regarding a medical diagnosis, robotic intervention, and disease management would likely be the future focuses.