Fast and reliable information is critical right now and the name of the game is collaboration. AI-powered literature review, Kaggle, is looking to leverage the expertise in three completely different industry sectors — basic science research, clinical medicine, and artificial intelligence — to provide continuously updated literature to help address the COVID-19 pandemic.
We had a chat with Dr. Tayab Waseem, the AI-literature reviews project lead, who has extensive experience bridging the worlds of AI and medicine together and he is excited for how this project will not only impact the pandemic, but also change the way research is done moving forward. Find out how Kaggle is using machine learning to help the research and medical communities, as well as the role that publishers play in this space moving forward.
The need to swiftly develop treatment protocols, vaccines, and stop the spread of the virus has led to an explosion in research within the scientific and medical community. With the rapid pace at which the literature is advancing and evolving the medical and scientific community is struggling to keep pace. To address this challenge, the White House Office of Science and Technology Policy issued a challenge to the machine learning community to develop an automated literature evaluation toolset for the medical research community. Leaders from the National Institutes of Health, Allen Institute for AI, Chan Zuckerberg Initiative, Microsoft, the National Library of Medicine, the Georgetown University’s Center for Security and Emerging Technology, and various other medical organizations were recruited to address this challenge. Over 128,000 manuscripts were converted into an AI readable format and tools were developed by the AI community on Kaggle to comb through and mine the literature to answer high-priority scientific questions in the form of an AI powered literature review.
TW: To create an AI powered literature review tool that aims to make the processes of writing literature reviews, rapid reviews, meta-analysis and editorials more efficient. The design of this toolset, which can be easily adapted for other purposes, will allot scientists and health care providers to find the most current information to quickly and efficiently identify current knowledge gaps and opportunities for further research.
TW: Ideally, these tables will help aggregate all relevant data needed to provide a meaningful overview of pertinent evidence. This will aid rapid production of systematic literature review, meta-analysis, etc.
Literature reviews need to be done for grants, papers, applications, etc. This will significantly decrease the amount of time it takes to go through and look for literature, decrease the amount of time it takes to extract relevant information, find papers that may be missed with a traditional search, and also identify gaps in knowledge.
TW: We use all papers from all journals including preprints (due to the rapid pace nature of the pandemic). Further, we are working with major medical journals in developing a tool that will be useful for the medical and scientific research community at large. We are also asking journals for specific questions that they want us to tackle using our tool.
We plan to publish the results of our studies in medical journals. The goal is not for these tables to live on Kaggle, but instead be put in front of the audiences that need/will use them (medical and institutional websites).
TW: All the AI code used to generate summary tables is released open source and under a Create Commons license. Without the articles being made open access and machine readable, this project wouldn't be possible. More generally, datasets like the Johns Hopkins University data on cases, recoveries and fatalities and Google and Apple's release of mobility data have been crucial to understanding the pandemic.
TW: Currently our team has over 150 medical and scientific volunteers across 30 institutes working on creating an AI-driven live literature review tool. We asked a couple of our team members across specialties, ranging from medical students, residents, and attending physicians to PhD students and post docs how this project will impact the future of their field. In their own words:
TW: Artificial intelligence has continued to evolve in its applications for healthcare. This will elucidate the first time AI has been implemented to glean insights from academic journals to combat the COVID-19 pandemic. It will be a cornerstone for others to build upon and further discoveries at a rate not previously possible
Many institutions have literature reviews on their websites, but keeping them up to date with the pace at which information is being published is unsustainable. We are happy to partner with these institutions and use our AI tool to alleviate part of this burden.
All our interviews reflect the views and opinions of the interviewees.