Machine Learning Models and Technologies for Evidence-Based Telehealth and Smart Care: A Review

Abstract

Background: Over the past few years, clinical studies have utilized machine learning in telehealth and smart care for disease management, self-management, and managing health issues like pulmonary diseases, heart failure, diabetes screening, and intraoperative risks. However, a systematic review of machine learning’s use in evidence-based telehealth and smart care is lacking, as evidence-based practice aims to eliminate biases and subjective opinions. Methods: The author conducted a mixed methods review to explore machine learning applications in evidence-based telehealth and smart care. A systematic search of the literature was performed during 16 June 2023–27 June 2023 in Google Scholar, PubMed, and the clinical registry platform ClinicalTrials.gov. The author included articles in the review if they were implemented by evidence-based health informatics and concerned with telehealth and smart care technologies. Results: The author identifies 18 key studies (17 clinical trials) from 175 citations found in internet databases and categorizes them using problem-specific groupings, medical/health domains, machine learning models, algorithms, and techniques. Conclusions: Machine learning combined with the application of evidence-based practices in healthcare can enhance telehealth and smart care strategies by improving quality of personalized care, early detection of health-related problems, patient quality of life, patient-physician communication, resource efficiency and cost-effectiveness. However, this requires interdisciplinary expertise and collaboration among stakeholders, including clinicians, informaticians, and policymakers. Therefore, further research using clinicall studies, systematic reviews, analyses, and meta-analyses is required to fully exploit the potential of machine learning in this area.

TeleStroke: real-time stroke detection with federated learning and YOLOv8 on edge devices

“…By incorporating deep learning into telemedicine services, healthcare professionals are enabled to deliver care that is both more efficient and economical, effectively overcoming the challenges posed by geographic limitations. This approach ensures that patients receive high-quality care tailored to their specific needs, regardless of where they are located, making healthcare more accessible and personalized [19][20][21]. Real-time deep learning has emerged as a powerful tool for enhancing diagnostic accuracy and efficiency.…”