Gu Jiasheng, Gao Chongyang, Wang Lili
Department of Computer Science, University of Southern California, Los Angeles, CA, United States.
Department of Computer Science, Northwestern University, Evanston, IL, United States.
JMIR AI. 2023 Dec 19;2:e45770. doi: 10.2196/45770.
The utilization of artificial intelligence (AI) technologies in the biomedical field has attracted increasing attention in recent decades. Studying how past AI technologies have found their way into medicine over time can help to predict which current (and future) AI technologies have the potential to be utilized in medicine in the coming years, thereby providing a helpful reference for future research directions.
The aim of this study was to predict the future trend of AI technologies used in different biomedical domains based on past trends of related technologies and biomedical domains.
We collected a large corpus of articles from the PubMed database 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 propose 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 evaluated. Our findings were confirmed through experiments on recurrent prediction and forecasting.
In our analysis 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 prior to 2017, our regression projections for the period of 2017-2021 exhibited a high coefficient of determination (R), which reached up to 0.78, demonstrating the effectiveness of our method in predicting long-term trends. Based on the prediction, studies related to proteins and tumors will be pushed out of the top 20 and become replaced by early diagnostics, tomography, and other detection technologies. These are certain areas that are well-suited to incorporate AI technology. Deep learning, machine learning, and neural networks continue to be the dominant AI technologies in biomedical applications. Generative adversarial networks represent an emerging technology with a strong growth trend.
In this study, we explored AI trends in the biomedical field and developed a predictive model to forecast future trends. Our findings were confirmed through experiments on current trends.
近几十年来，人工智能（AI）技术在生物医学领域的应用受到了越来越多的关注。研究过去的人工智能技术是如何随着时间推移进入医学领域的，有助于预测哪些当前（以及未来）的人工智能技术在未来几年有潜力应用于医学，从而为未来的研究方向提供有益参考。
本研究的目的是基于相关技术和生物医学领域的过去趋势，预测不同生物医学领域中使用的人工智能技术的未来趋势。
我们从PubMed数据库中收集了大量与人工智能和生物医学交叉领域相关的文章语料库。最初，我们尝试仅对提取的关键词进行回归分析；然而，我们发现这种方法提供的信息不足。因此，我们提出了一种名为“背景增强预测”的方法，通过结合关键词及其周围上下文来扩展回归算法所利用的知识。这种数据构建方法在评估的六个回归模型中均提高了性能。我们的研究结果通过循环预测和预测实验得到了证实。
在我们使用背景信息进行预测的分析中，我们发现窗口大小为3时产生的结果最佳，优于仅使用关键词的情况。此外，仅利用2017年之前的数据，我们对2017 - 2021年期间的回归预测显示出较高的决定系数（R），高达0.78，证明了我们的方法在预测长期趋势方面的有效性。基于该预测，与蛋白质和肿瘤相关的研究将被挤出前20名，并被早期诊断、断层扫描和其他检测技术所取代。这些是非常适合纳入人工智能技术的特定领域。深度学习、机器学习和神经网络仍然是生物医学应用中的主导人工智能技术。生成对抗网络是一种具有强劲增长趋势的新兴技术。
在本研究中，我们探索了生物医学领域的人工智能趋势，并开发了一个预测模型来预测未来趋势。我们的研究结果通过对当前趋势的实验得到了证实。
