Table 2.

Comparison of machine learning tools based on NLP and a conventional search engine

	AI-powered search tool based on NLP (e.g., WellAI)	A publication search engine (e.g., PubMed)
General objective	Neural networks summarize, generalize and predict relationships	Searches for key words and phrases in an article. Cannot make conclusions about relationships.
Synonyms (correlated concepts)	Understands synonyms and correlated concepts. For example, understands that “hypertension” is a synonym for “high blood pressure” and “elevated blood pressure”. This knowledge helps build more accurate relationships between concepts.	The results produced match the search words or phrases, without knowledge of synonyms and related concepts.
Result aggregated and summarized?	Yes. Every single concept suggestion is based on a large number of articles.	No. The result is a list of articles that contain the key words or phrases.
Output & next step	A structured list of concepts with ranked probabilities. This narrows the scope of work and results in greater efficiency. Focus on concepts of interest and exploration of relationships - not only between concepts (e.g., COVID-19 and Diagnostics Radiology), but between clusters of concepts (e.g., COVID-19 + Diagnosis, Clinical + Diagnostic Tests and Diagnostics Radiology)	A list of every single occurrence (i.e., every article) of a word or a phrase. Read the articles (time consuming), summarize, and make generalizations.
Example	Starting with “COVID-19” as the preselected concept, selecting “READ ARTICLES” corresponding to “Diagnosis, Clinical” produces a list of articles in which the machine learning models have determined there is a relationship between COVID-19 and clinical diagnosis, and not just the whole list of articles that mentions both COVID-19 and clinical diagnosis. In addition, the models know there is a difference between clinical diagnosis and diagnosis.	The result for search terms “COVID-19” and “clinical diagnosis”, is a list of all articles that mention “COVID-19” and “clinical diagnosis” irrespective of whether there is a relationship between the two phrases mentioned in the article. For example, hypothetically speaking, the article may not be about clinical diagnosis at all, the phrase “Clinical diagnosis” may be just mentioned in the References section.