Završnik Jernej, Kokol Peter, Žlahtič Bojan, Blažun Vošner Helena
Community Healthcare Center Dr. Adolf Drolc Maribor, 2000 Maribor, Slovenia.
Alma Mater Europaea, 2000 Maribor, Slovenia.
Healthcare (Basel). 2025 Jul 7;13(13):1629. doi: 10.3390/healthcare13131629.
Artificial intelligence and machine learning are playing crucial roles in digital transformation, aiming to improve the efficiency, effectiveness, equity, and responsiveness of primary health systems and their services. Using synthetic knowledge synthesis and bibliometric and thematic analysis triangulation, we identified the most productive and prolific countries, institutions, funding sponsors, source titles, publications productivity trends, and principal research categories and themes. The United States and the United Kingdom were the most productive countries; and were the most prolific journals; and the National Institutes of Health, USA, and the National Natural Science Foundation of China were the most productive funding sponsors. The publication productivity trend is positive and exponential. The main themes are related to natural language processing in clinical decision-making, primary health care optimization focusing on early diagnosis and screening, improving health-based social determinants, and using chatbots to optimize communications with patients and between health professionals. The use of machine learning in primary health care aims to address the significant global burden of so-called "missed diagnostic opportunities" while minimizing possible adverse effects on patients.
人工智能和机器学习在数字转型中发挥着关键作用，旨在提高初级卫生系统及其服务的效率、效果、公平性和响应能力。通过综合知识合成以及文献计量与主题分析三角测量法，我们确定了最具生产力和最多产的国家、机构、资助赞助商、来源期刊、出版物生产力趋势以及主要研究类别和主题。美国和英国是最具生产力的国家；[此处原文缺失最具生产力的期刊相关内容]是最多产的期刊；美国国立卫生研究院和中国国家自然科学基金是最具生产力的资助赞助商。出版物生产力趋势呈积极的指数增长。主要主题涉及临床决策中的自然语言处理、以早期诊断和筛查为重点的初级卫生保健优化、改善基于健康的社会决定因素，以及使用聊天机器人优化与患者及卫生专业人员之间的沟通。在初级卫生保健中使用机器学习旨在应对所谓“漏诊机会”带来的重大全球负担，同时将对患者可能产生的不利影响降至最低。
