Clinical Decision Support and Natural Language Processing inMedicine:Systematic Literature Review

Background: 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. Objective: 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. Methods: A search was conducted in the PubMed, Embase, ScienceDirect, Scopus, and Web of Science online databases forarticles published between January 2000 and April 2023. The only inclusion criteria were (1) original research articles and studieson the application of AI-based medical clinical decision support using NLP techniques and (2) publications in English. A CriticalAppraisal Skills Programme tool was used to assess the quality of the studies. Results: 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 healthrecords as a data source, and a further 8 (31%) were based on clinical data. Only 5 (19%) of the articles showed the use ofcombined 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 informationobtained for immediate clinical use. Conclusions: The use of NLP engines can effectively improve clinical decision systems'accuracy, while biphasic tools combiningAI algorithms and human criteria may optimize clinical diagnosis and treatment flows. Trial Registration: PROSPERO CRD42022373386; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=373386