Improving Primary Healthcare Workflow Using Extreme Summarization of
  Scientific Literature Based on Generative AI
Gregor Stiglic Leon Kopitar Lucija Gosak Primoz Kocbek Zhe He Prithwish Chakraborty Pablo Meyer Jiang Bian
Primary care professionals struggle to keep up to date with the latest
scientific literature critical in guiding evidence-based practice related to
their daily work. To help solve the above-mentioned problem, we employed
generative artificial intelligence techniques based on large-scale language
models to summarize abstracts of scientific papers. Our objective is to
investigate the potential of generative artificial intelligence in diminishing
the cognitive load experienced by practitioners, thus exploring its ability to
alleviate mental effort and burden. The study participants were provided with
two use cases related to preventive care and behavior change, simulating a
search for new scientific literature. The study included 113 university
students from Slovenia and the United States randomized into three distinct
study groups. The first group was assigned to the full abstracts. The second
group was assigned to the short abstracts generated by AI. The third group had
the option to select a full abstract in addition to the AI-generated short
summary. Each use case study included ten retrieved abstracts. Our research
demonstrates that the use of generative AI for literature review is efficient
and effective. The time needed to answer questions related to the content of
abstracts was significantly lower in groups two and three compared to the first
group using full abstracts. The results, however, also show significantly lower
accuracy in extracted knowledge in cases where full abstract was not available.
Such a disruptive technology could significantly reduce the time required for
healthcare professionals to keep up with the most recent scientific literature;
nevertheless, further developments are needed to help them comprehend the
knowledge accurately.