Applied Artificial Intelligence in Healthcare: A Review of Computer Vision Technology Application in Hospital Settings

Author:

Lindroth Heidi123ORCID,Nalaie Keivan14ORCID,Raghu Roshini1ORCID,Ayala Ivan N.1ORCID,Busch Charles15,Bhattacharyya Anirban6,Moreno Franco Pablo7ORCID,Diedrich Daniel A.4,Pickering Brian W.4,Herasevich Vitaly4ORCID

Affiliation:

1. Division of Nursing Research, Department of Nursing, Mayo Clinic, Rochester, MN 55905, USA

2. Center for Aging Research, Regenstrief Institute, School of Medicine, Indiana University, Indianapolis, IN 46202, USA

3. Center for Health Innovation and Implementation Science, School of Medicine, Indiana University, Indianapolis, IN 46202, USA

4. Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN 55905, USA

5. College of Engineering, University of Wisconsin-Madison, Madison, WI 53705, USA

6. Department of Critical Care Medicine, Mayo Clinic, Jacksonville, FL 32224, USA

7. Department of Transplantation Medicine, Mayo Clinic, Jacksonville, FL 32224, USA

Abstract

Computer vision (CV), a type of artificial intelligence (AI) that uses digital videos or a sequence of images to recognize content, has been used extensively across industries in recent years. However, in the healthcare industry, its applications are limited by factors like privacy, safety, and ethical concerns. Despite this, CV has the potential to improve patient monitoring, and system efficiencies, while reducing workload. In contrast to previous reviews, we focus on the end-user applications of CV. First, we briefly review and categorize CV applications in other industries (job enhancement, surveillance and monitoring, automation, and augmented reality). We then review the developments of CV in the hospital setting, outpatient, and community settings. The recent advances in monitoring delirium, pain and sedation, patient deterioration, mechanical ventilation, mobility, patient safety, surgical applications, quantification of workload in the hospital, and monitoring for patient events outside the hospital are highlighted. To identify opportunities for future applications, we also completed journey mapping at different system levels. Lastly, we discuss the privacy, safety, and ethical considerations associated with CV and outline processes in algorithm development and testing that limit CV expansion in healthcare. This comprehensive review highlights CV applications and ideas for its expanded use in healthcare.

Funder

National Institute of Health, National Institute on Aging

Publisher

MDPI AG

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