人工智能在生物医学中的发展:文献计量分析

JMIR AI Pub Date : 2023-12-19 DOI:10.2196/45770
Jiasheng Gu, Chongyang Gao, Lili Wang
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引用次数: 0

摘要

近几十年来,人工智能(AI)技术在生物医学领域的应用日益受到关注。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领域的,有助于预测当前(和未来)哪些人工智能技术有可能在未来几年被应用于医学领域,从而为未来的研究方向提供有益的参考。 本研究旨在根据相关技术和生物医学领域过去的发展趋势,预测人工智能技术在不同生物医学领域应用的未来趋势。 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相关的文章。起初,我们尝试仅对提取的关键词进行回归,但发现这种方法无法提供足够的信息。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。这种数据构建方法提高了所评估的六个回归模型的性能。我们的研究结果在循环预测和预报实验中得到了证实。 在使用背景信息进行预测的分析中,我们发现窗口大小为 3 的结果最好,优于仅使用关键词的结果。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(R2),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。根据预测,与蛋白质和肿瘤相关的研究将被挤出前 20 名,取而代之的是早期诊断、断层扫描和其他检测技术。这些领域非常适合采用人工智能技术。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流人工智能技术。生成对抗网络是一种新兴技术,具有强劲的增长趋势。 在本研究中,我们探讨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。
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The Evolution of Artificial Intelligence in Biomedicine: Bibliometric Analysis
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 (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. 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.
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