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.
MATERIALS AND METHODS: A questionnaire was designed and distributed to MIPs in Jordan. The survey comprised four sections: demographic data, MIP knowledge on dose/protocol parameters, image quality, and DRLs. Statistical analyses were performed utilizing Pearson's correlation, t-tests, ANOVA, and linear regression, with a significance level of 95 % and a p-value threshold of <0.05.
RESULTS: The study involved 147 participants. Most respondents were male (76.2 %), and most were aged 26-35 years (44.2 %). Approximately 51 % held a bachelor's degree, and the most common range of experience was 3-5 years (28.6 %). Participants showed a moderate level of knowledge regarding dose and protocol parameters, with a mean score of 61.8 %. The mean scores for knowledge of image quality and DRLs were 45.2 % and 44.8 %, respectively. The age group of the MIPs and the total experience were found to have a significant impact on the knowledge of the dose and protocol parameters, as well as the DRLs. Additionally, experience was found to have a significant influence on knowledge of the dose and protocol parameters. The study revealed a positive and significant effect of MIPs' knowledge of dose/protocol parameters and image quality on their knowledge of DRLs.
CONCLUSIONS: This study indicates that professionals across five specialties who are engaged in PET/CT and CT imaging possess a moderate understanding of dosage and protocol parameters. However, there is a notable gap in knowledge regarding DRLs and image quality. To address this issue, it is recommended that MIPs actively engage in educational programs emphasizing exposure parameters and their impact on image quality. Additionally, access to comprehensive education and training programs will enable MIPs to grasp the complexities of DRLs and their implications, facilitating their implementation in clinical practice.