An evaluation of two commercial deep learning-based information retrieval systems for COVID-19 literature.

Journal of the American Medical Informatics Association : JAMIA
Sarvesh Soni, Kirk Roberts

Abstract

The COVID-19 pandemic has resulted in a tremendous need for access to the latest scientific information, leading to both corpora for COVID-19 literature and search engines to query such data. While most search engine research is performed in academia with rigorous evaluation, major commercial companies dominate the web search market. Thus, it is expected that commercial pandemic-specific search engines will gain much higher traction than academic alternatives, leading to questions about the empirical performance of these tools. This paper seeks to empirically evaluate two commercial search engines for COVID-19 (Google and Amazon) in comparison with academic prototypes evaluated in the TREC-COVID task. We performed several steps to reduce bias in the manual judgments to ensure a fair comparison of all systems. We find the commercial search engines sizably underperformed those evaluated under TREC-COVID. This has implications for trust in popular health search engines and developing biomedical search engines for future health crises.

References

Dec 4, 2019·Journal of the American Medical Informatics Association : JAMIA·Stephen WuHua Xu
May 5, 2020·Journal of the American Medical Informatics Association : JAMIA·Kirk RobertsWilliam R Hersh

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Citations

Oct 13, 2020·Chaos, Solitons, and Fractals·Mohammad-H Tayarani N
Jun 29, 2021·Arabian Journal for Science and Engineering·Jafar A AlzubiMeenu Gupta
Aug 11, 2021·Journal of Medical Internet Research·Douglas TeodoroPoorya Amini
Aug 18, 2021·Expert Systems with Applications·Muzammil KhanHaider Abbass

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Software Mentioned

- 19 Search
CORD
COVID - 19 Research Explorer
SMART
GPT
Amazon Comprehend Medical ( ACM )
Clinical BERT
Amazon Kendra
Sabir
BioBERT

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