Alex Zhavoronkov explains why AI can help in peer reviewing drug development research

14-Sep-2020
KEY POINTS FROM ARTICLE:

An average of 367 COVID-19 papers are being published every week.
Unprecedented peer review turnaround times (6 days) risk the release of flawed publications.
Likewise by the recent establishment of non-peer-reviewed preprint servers.
Regulatory authorities are rushing through emergency approvals of the drugs without data on effective dosage or safety protocols.
Need to understand the ever-growing stream of knowledge being published in order to process it in real time.
Dictionary-based text mining coupled with AI/ML statistical pattern recognition can help.
Systems designed to predict the outcomes of clinical trials on the basis of multiomics data may be used to caution regulators.
Unbiased clinical data, compiled in real time, ought to be made accessible without restrictions.
A synchronized workflow could assist in assembling disparate evidence & hypotheses into actionable healthcare solutions.
ML algorithms can accelerate the design of clinical trials by automatically identifying suitable subjects.

An automated sense-check of publications sounds like a good idea

Mentioned in this article:

Academic Alex Zhavoronkov - CEO of InSilico Medicine & Deep Longevity. CSO of Biogerontology Research Foundation.

Journal Nature Biotechnology - Journal providing information from all areas of biotechnology.

Read full article on Nature Biotechnology website
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