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Web intelligence, which aims to understand the computational, cognitive, physical, and social foundations of the future Web, has attracted increasing attention to facilitate the study of brain informatics to promote human health. A large number of articles created in the recent few years are proof of the investment in Web intelligence-assisted human health. This study systematically reviews academic studies regarding article trends, top journals, subjects, countries\/regions, and institutions, study design, artificial intelligence technologies, clinical tasks, and performance evaluation. Results indicate that literature is especially welcomed in subjects such as <jats:italic>medical informatics<\/jats:italic> and <jats:italic>health care sciences and service<\/jats:italic>. There are several promising topics, for example, random forests, support vector machines, and conventional neural networks for disease detection and diagnosis, semantic Web, ontology mining, and topic modeling for clinical or biomedical text mining, artificial neural networks and logistic regression for prediction, and convolutional neural networks\u00a0and support vector machines for monitoring and classification. Additionally, future research should focus on algorithm innovations, additional information use, functionality improvement, model\u00a0and\u00a0system generalization, scalability, evaluation, and automation, data acquirement and quality improvement, and allowing interaction. The findings of this study help better understand what and how Web intelligence can be applied to promote healthcare procedures and clinical outcomes. This provides important insights into the effective use of Web intelligence to support informatics-enabled brain studies.<\/jats:p>","DOI":"10.1186\/s40708-022-00153-9","type":"journal-article","created":{"date-parts":[[2022,2,12]],"date-time":"2022-02-12T14:02:33Z","timestamp":1644674553000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Machine and cognitive intelligence for human health: systematic review"],"prefix":"10.1186","volume":"9","author":[{"given":"Xieling","family":"Chen","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5614-3348","authenticated-orcid":false,"given":"Gary","family":"Cheng","sequence":"additional","affiliation":[]},{"given":"Fu Lee","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Xiaohui","family":"Tao","sequence":"additional","affiliation":[]},{"given":"Haoran","family":"Xie","sequence":"additional","affiliation":[]},{"given":"Lingling","family":"Xu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,2,12]]},"reference":[{"key":"153_CR1","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1111\/j.0824-7935.2005.00270.x","volume":"21","author":"N Zhong","year":"2005","unstructured":"Zhong N, Hu J, Motomura S et al (2005) Building a data-mining grid for multiple human brain data analysis. 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