Significance of Artificial Intelligence in Health Research

Aims: The essence of this literature review is to investigate the benefits of using Artificial Intelligence, AI, in research, analysing its downfalls and how they may be curtailed.

Method: The electronic databases of PubMed and Google Scholar, and the engine, Google search were systematically searched for the relevant materials. This search strategy yielded 333 information sources, 155 of which were eligible for screening based on their publication in English language in a peer-reviewed journal. Of these, a total of 39 studies met the inclusion criteria and were included in the review.

Results: Artificial Intelligence benefits research by processing faster literature reviews, predictive analysis and data visualizations. AI also could recommend novel research topics and themes, improve academic writing and speed up data analysis. Popular examples of such tools that are developed purposely for undergoing research are Avid Note, Jenni, Paperpal, Research Rabit and Scispace.

Conclusion: As demonstrated by the Author’s experience of using AI during a PhD application process, and their research curiosity in Global health inequality themes to further support the result of this review, AI makes research less tedious, less menial and time effective. But a health researcher must put in the needed effort to manually synthesize evidence to undergo the needed personal development of brain processing. If this is ignored for the AI tool to do everything in the research, as with the use of ChatGPT, such work is liable to be subjected to components of falsehood, also known as “hallucinations”. Furthermore, avoiding this brain processing violates transparency, accountability and sincerity to commit the crime of publication fraud, which also means cognitive bypassing that makes the researcher less intelligent, causing more harm than good in health.