Keeping current with professional literature can be a daunting task. Even if you have personal subscriptions to major journals, it is difficult to keep up to date with the many recent publications in your areas of interest. Many journals provide options to receive tables of contents as new journal issues are published which can be requested from a library. Free online specialized search engines are available to search for recent and past journal articles. If you are fortunate to have university library privileges, this may mean having full text online access to journal articles. Some articles are published as open access and may be available online for free or from the author (s). Below is a list of several major professional literature search engines that may be useful in searching for professional articles.
Pub Med https://www.ncbi.nlm.nih.gov/pubmed
“PubMed comprises more than 28 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.” This search engine is available as a mobile app at:
The Apple Store https://itunes.apple.com/us/app/pubmed-on-tap/id301316540
Google Play Store https://play.google.com/store/apps/details?id=gov.nih.nlm.lhc.pubmed4hh
Google Scholar https://scholar.google.com/
“Google Scholar describes its scope and content generally, and, unlike the major science and technology bibliographic databases such as PubMed, ISI Web of Science, or Cambridge Scientific Abstracts, the search engine does not provide any source lists of publications searched or authority files for author names, journal titles, or controlled vocabulary for subjects. These considerations greatly limit Google Scholar’s use as an exclusive research tool for STM research. The search engine, however, returns a large number of search results very quickly.”
Semantic Scholar https://www.semanticscholar.org/
Semantic Scholar “… is designed to be a “smart” search service for journal articles. The project uses a combination of machine learning, natural language processing, machine vision to add a layer of semantic analysis to the traditional methods of citation analysis. In comparison to Google Scholar and PubMed, it is designed to quickly highlight the most important papers and identify the connections between them.”