Evaluating online health information quality using machine learning and deep learning: A systematic literature review

Abstract

Background Due to the large volume of online health information, while quality remains dubious, understanding the usage of artificial intelligence to evaluate health information and surpass human-level performance is crucial. However, the existing studies still need a comprehensive review highlighting the vital machine, and Deep learning techniques for the automatic health information evaluation process. Objective Therefore, this study outlines the most recent developments and the current state of the art regarding evaluating the quality of online health information on web pages and specifies the direction of future research. Methods In this article, a systematic literature is conducted according to the PRISMA statement in eight online databases PubMed, Science Direct, Scopus, ACM, Springer Link, Wiley Online Library, Emerald Insight, and Web of Science to identify all empirical studies that use machine and deep learning models for evaluating the online health information quality. Furthermore, the selected techniques are compared based on their characteristics, such as health quality criteria, quality measurement tools, algorithm type, and achieved performance. Results The included papers evaluate health information on web pages using over 100 quality criteria. The results show no universal quality dimensions used by health professionals and machine or deep learning practitioners while evaluating health information quality. In addition, the metrics used to assess the model performance are not the same as those used to evaluate human performance. Conclusions This systemic review offers a novel perspective in approaching the health information quality in web pages that can be used by machine and deep learning practitioners to tackle the problem more effectively.

Critical Review on the Contribution of Machine Learning to Health Science

“…The most frequent areas of ML application in healthcare are oncology and neurology, likely reflecting the prevalence of these diseases 9 . ML has demonstrated high predictive ability in many disease areas, but the reporting quality of these studies is often low, with many lacking data on accuracy, sensitivity, specificity, and validation 9,10 . The most used data source for ML in healthcare is radiological imaging, which has been used to develop ML solutions for clinical decision support 9 .…”