SEMERGEN New Technologies Working Group, Madrid, Spain.
Faculty of Health Sciences, Universitat Oberta de Catalunya (UOC), Barcelona, Spain.
J Med Internet Res. 2024 Sep 30;26:e55315. doi: 10.2196/55315.
Ensuring access to accurate and verified information is essential for effective patient treatment and diagnosis. Although health workers rely on the internet for clinical data, there is a need for a more streamlined approach.
This systematic review aims to assess the current state of artificial intelligence (AI) and natural language processing (NLP) techniques in health care to identify their potential use in electronic health records and automated information searches.
A search was conducted in the PubMed, Embase, ScienceDirect, Scopus, and Web of Science online databases for articles published between January 2000 and April 2023. The only inclusion criteria were (1) original research articles and studies on the application of AI-based medical clinical decision support using NLP techniques and (2) publications in English. A Critical Appraisal Skills Programme tool was used to assess the quality of the studies.
The search yielded 707 articles, from which 26 studies were included (24 original articles and 2 systematic reviews). Of the evaluated articles, 21 (81%) explained the use of NLP as a source of data collection, 18 (69%) used electronic health records as a data source, and a further 8 (31%) were based on clinical data. Only 5 (19%) of the articles showed the use of combined strategies for NLP to obtain clinical data. In total, 16 (62%) articles presented stand-alone data review algorithms. Other studies (n=9, 35%) showed that the clinical decision support system alternative was also a way of displaying the information obtained for immediate clinical use.
The use of NLP engines can effectively improve clinical decision systems' accuracy, while biphasic tools combining AI algorithms and human criteria may optimize clinical diagnosis and treatment flows.
PROSPERO CRD42022373386; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=373386.
确保获得准确和经过验证的信息对于有效的患者治疗和诊断至关重要。尽管卫生工作者依赖互联网获取临床数据，但需要更精简的方法。
本系统评价旨在评估人工智能（AI）和自然语言处理（NLP）技术在医疗保健中的现状，以确定它们在电子健康记录和自动化信息搜索中的潜在用途。
在 PubMed、Embase、ScienceDirect、Scopus 和 Web of Science 在线数据库中检索了 2000 年 1 月至 2023 年 4 月期间发表的文章。唯一的纳入标准是（1）使用 NLP 技术的基于 AI 的医学临床决策支持应用的原始研究文章和研究，以及（2）以英文发表的文章。使用批判性评估技能计划工具评估研究的质量。
搜索共产生了 707 篇文章，其中 26 项研究被纳入（24 篇原始文章和 2 篇系统评价）。在所评估的文章中，21 篇（81%）解释了 NLP 的使用作为数据收集的来源，18 篇（69%）使用电子健康记录作为数据源，另有 8 篇（31%）基于临床数据。只有 5 篇（19%）的文章显示使用了 NLP 的组合策略来获取临床数据。总共，16 篇（62%）文章提出了独立的数据审查算法。其他研究（n=9，35%）表明，临床决策支持系统替代方案也是一种显示即时临床使用的信息的方式。
NLP 引擎的使用可以有效提高临床决策系统的准确性，而结合 AI 算法和人工标准的双相工具可能会优化临床诊断和治疗流程。
PROSPERO CRD42022373386；https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=373386。
