Abstract
AbstractObjectives Artificial intelligence (AI)-based clinical decision support systems (CDSS) have been developed to solve medical problems and enhance health care management. We aimed to review the literature to identify trends and applications of AI algorithms in CDSS for internal medicine subspecialties.Methods A scoping review was conducted in PubMed, IEEE Xplore, and Scopus to determine articles related to CDSS using AI algorithms that use deep learning, machine learning, and pattern recognition. This review synthesized the main purposes of CDSS, types of AI algorithms, and overall accuracy of algorithms. We searched the original research published in English between 2009 and 2019.Results Given the volume of articles meeting inclusion criteria, the results of 218 of the 3,467 articles were analyzed and presented in this review. These 218 articles were related to AI-based CDSS for internal medicine subspecialties: neurocritical care (n = 89), cardiovascular disease (n = 79), and medical oncology (n = 50). We found that the main purposes of CDSS were prediction (48.4%) and diagnosis (47.1%). The five most common algorithms include: support vector machine (20.9%), neural network (14.6%), random forest (10.5%), deep learning (9.2%), and decision tree (8.8%). The accuracy ranges of algorithms were 61.8 to 100% in neurocritical care, 61.6 to 100% in cardiovascular disease, and 54 to 100% in medical oncology. Only 20.1% of those algorithms had an explainability of AI, which provides the results of the solution that humans can understand.Conclusion More AI algorithms are applied in CDSS and are important in improving clinical practice. Supervised learning still accounts for a majority of AI applications in internal medicine. This study identified four potential gaps: the need for AI explainability, the lack of ubiquity of CDSS, the narrow scope of target users of CDSS, and the need for AI in health care report standards.
Highlights
Background and SignificanceClinical Decision Support Systems According to the Office of the National Coordinator for Health Information Technology, “clinical decision support (CDS) provides clinicians, staff, patients, or other individuals with knowledge and person-specific information, intelligently filtered or presented at appropriate times, to enhance health and health care.”[1]
More artificial intelligence (AI) algorithms are applied in CDS systems (CDSS) and are important in improving clinical practice
Inclusion and Exclusion Criteria Articles were included if they met the following criteria: (1) addressed CDSS using AI algorithms; (2) the AI algorithms studied include Deep learning (DL), machine learning (ML), or automated pattern recognition; (3) they were related to the internal medicine specialty; (4) they were published between January 1, 2009 and December 31, 2019; (5) they were published in English; and (6) were original research
Summary
Clinical Decision Support Systems According to the Office of the National Coordinator for Health Information Technology, “clinical decision support (CDS) provides clinicians, staff, patients, or other individuals with knowledge and person-specific information, intelligently filtered or presented at appropriate times, to enhance health and health care.”[1] CDS can be used on a variety of tools and systems for clinical decision-making. Examples of CDS tools include alerts, reminders, clinical guidelines, recommendations, condition-specific order sets, data reports, documentation templates, diagnostic support, and databases.[2] CDS systems (CDSS) are computerized tools to help clinicians make clinical decisions and manage information.[3] Examples of CDSS include automated laboratory alerting systems that help the user focus on key messages such as highlighting abnormal laboratory values,[4] and pharmacy information systems that provide alerts for drug allergies or interactions.[5] Advanced CDSS delivers more accurate information to clinicians, for instance, personalized drug dosage calculators, case-based recommendations, and suggestions for laboratory testing based on diseases. To manage a large amount of clinical data and effectively transform health care systems, artificial intelligence (AI) and machine learning (ML) have been applied to computerized CDSS.[7,8,9]
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