The Evolution of Artificial Intelligence in Bio-Medicine
ABSTRACT
Background:
The usage of artificial intelligence (AI) technologies in the biomedical field has attracted increasing attention in recent decades. By studying how past AI technologies have found their way into medicine over time allows us to predict which current (and future) AI technologies have the potential to be utilized in medicine in the coming years which will provide a helpful reference for future research directions.
Objective:
To predict the future trend of AI technologies used in different biomedical domains based on past trends of related technologies and biomedical domains.
Methods:
We collected a large corpus of articles from PubMed pertaining to the intersection of AI and biomedicine. Initially, we attempted to use regression on the extracted keywords alone, however, we found that this approach did not provide sufficient information. Therefore, we proposed a method called “background-enhanced prediction” to expand the knowledge utilized by the regression algorithm by incorporating both the keywords and their surrounding context. This method of data construction resulted in improved performance across the six regression models we experimented with. Our findings were confirmed through experiments on recurrent prediction and forecasting.
Results:
In our analysis of using background information for prediction, we found that a window size of 3 yielded the best results, outperforming the use of keywords alone. Furthermore, utilizing data only from prior to 2017, our regression projections for the period of 2017-2021 exhibited a high coefficient of determination (R-squared), demonstrating the effectiveness of our method in predicting long-term trends.
Conclusions:
In this study, we explore AI trends in the biomedical field and develop a predictive model to forecast future trends. We collected a large corpus of articles from PubMed pertaining to the intersection of AI and biomedicine. We first trained a regression model on the extracted keywords alone, however, we found that this approach did not provide sufficient information. Next, we proposed a method called “background-enhanced prediction” to expand the knowledge utilized by the regression algorithm by incorporating both the keywords and their surrounding context. This method of data construction resulted in improved performance across the regression models we experimented with. Our findings were confirmed through experiments on current trends. Clinical Trial: N/A
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