The role of artificial intelligence in healthcare: a structured literature review

• Published in: BMC medical informatics and decision making Vol. 21; no. 1; pp. 125 - 23
• Main Authors: Secinaro, Silvana, Calandra, Davide, Secinaro, Aurelio, Muthurangu, Vivek, Biancone, Paolo
• Format: Journal Article
• Language: English
• Published: London BioMed Central 10.04.2021; BioMed Central Ltd; BMC
• Subjects: Artificial Intelligence; Bibliometrics; Big Data; China; Clinical Decision-Making; Data mining; Decision making; Delivery of Health Care; Health care; Health Informatics; Health services; Healthcare; Humans; Informatics; Information Systems and Communication Service; Keywords; Literature reviews; Management; Management of Computing and Information Systems; Medical informatics; Medical personnel; Medical records; Medical research; Medical treatment; Medicine; Medicine & Public Health; Patient data; Patients; Physicians; Public health; Qualitative analysis; Research Article; Secondary data analysis; Systematic review; Technology application; Trends; United Kingdom; United Kingdom; China; Healthcare; Patient data; Management; Artificial intelligence; Clinical decision-making
• ISSN: 1472-6947; 1472-6947
• DOI: 10.1186/s12911-021-01488-9

Background/Introduction Artificial intelligence (AI) in the healthcare sector is receiving attention from researchers and health professionals. Few previous studies have investigated this topic from a multi-disciplinary perspective, including accounting, business and management, decision sciences and health professions. Methods The structured literature review with its reliable and replicable research protocol allowed the researchers to extract 288 peer-reviewed papers from Scopus. The authors used qualitative and quantitative variables to analyse authors, journals, keywords, and collaboration networks among researchers. Additionally, the paper benefited from the Bibliometrix R software package. Results The investigation showed that the literature in this field is emerging. It focuses on health services management, predictive medicine, patient data and diagnostics, and clinical decision-making. The United States, China, and the United Kingdom contributed the highest number of studies. Keyword analysis revealed that AI can support physicians in making a diagnosis, predicting the spread of diseases and customising treatment paths. Conclusions The literature reveals several AI applications for health services and a stream of research that has not fully been covered. For instance, AI projects require skills and data quality awareness for data-intensive analysis and knowledge-based management. Insights can help researchers and health professionals understand and address future research on AI in the healthcare field.