PDF(Pubmed)
Abstract：
BACKGROUND: 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.
OBJECTIVE: 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.
METHODS: 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.
RESULTS: 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 (R2), 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.
CONCLUSIONS: 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.
OBJECTIVE: 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.
METHODS: 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.
RESULTS: 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 (R2), 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.
CONCLUSIONS: 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.
摘要：
背景：近几十年来，人工智能（AI）技术在生物医学领域的利用引起了越来越多的关注。研究过去的人工智能技术是如何随着时间的推移进入医学的，可以帮助预测未来几年哪些当前（和未来）的人工智能技术有潜力用于医学。从而为今后的研究方向提供有益的参考。
目的：本研究的目的是根据相关技术和生物医学领域的过去趋势，预测AI技术在不同生物医学领域使用的未来趋势。
方法：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相关的文章。最初，我们试图单独对提取的关键字使用回归；然而，我们发现这种方法没有提供足够的信息。因此,我们提出了一种称为“背景增强预测”的方法，通过合并关键字及其周围上下文来扩展回归算法所利用的知识。这种数据构建方法提高了评估的六个回归模型的性能。我们的发现通过循环预测和预测实验得到了证实。
结果：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会产生最好的结果，优于单独使用关键字。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很高的决定系数(R2)，达到0.78，证明了我们的方法在预测长期趋势方面的有效性。根据预测，与蛋白质和肿瘤相关的研究将被推出前20名，并被早期诊断所取代，断层摄影术,和其他检测技术。这些是非常适合纳入AI技术的某些领域。深度学习,机器学习,神经网络仍然是生物医学应用中占主导地位的人工智能技术。生成对抗网络代表了一种具有强劲增长趋势的新兴技术。
结论：在这项研究中，我们探索了生物医学领域的人工智能趋势，并开发了预测模型来预测未来趋势。我们的发现通过对当前趋势的实验得到了证实。
目的：本研究的目的是根据相关技术和生物医学领域的过去趋势，预测AI技术在不同生物医学领域使用的未来趋势。
方法：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相关的文章。最初，我们试图单独对提取的关键字使用回归；然而，我们发现这种方法没有提供足够的信息。因此,我们提出了一种称为“背景增强预测”的方法，通过合并关键字及其周围上下文来扩展回归算法所利用的知识。这种数据构建方法提高了评估的六个回归模型的性能。我们的发现通过循环预测和预测实验得到了证实。
结果：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会产生最好的结果，优于单独使用关键字。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很高的决定系数(R2)，达到0.78，证明了我们的方法在预测长期趋势方面的有效性。根据预测，与蛋白质和肿瘤相关的研究将被推出前20名，并被早期诊断所取代，断层摄影术,和其他检测技术。这些是非常适合纳入AI技术的某些领域。深度学习,机器学习,神经网络仍然是生物医学应用中占主导地位的人工智能技术。生成对抗网络代表了一种具有强劲增长趋势的新兴技术。
结论：在这项研究中，我们探索了生物医学领域的人工智能趋势，并开发了预测模型来预测未来趋势。我们的发现通过对当前趋势的实验得到了证实。
