The Power of AI to Address AMR and Sepsis
Recorded On: 04/26/2023
Description:
This session was part of the Sepsis Alliance AMR Conference 2023.
Artificial intelligence (AI) has been increasingly applied in medicine and, in particular, in the development of new drugs to combat antimicrobial resistance (AMR). AI can serve as a new tool to shorten drug development time and costs in the development of new therapeutics and antibiotics. During this session, the presenter will discuss the use of AI to combat AMR and other infections that can lead to sepsis and the importance of the accelerated development of new drugs.
Learning Objectives:
At the end of the session, the learner should be able to:
- Summarize the basics of AI and other forms of computing;
- List the ways in which AI can be leveraged to combat AMR;
- Identify how AI can be used to drastically reduce the time and cost to discover new drugs.
Target Audience:
Nurses, advanced practice providers, physicians, emergency responders, pharmacists, medical technologists, respiratory therapists, physical/occupational therapists, infection prevention specialists, data/quality specialists, and more.
Supratik Mukhopadhyay, PhD
Professor, Department of Environmental Sciences
Louisiana State University
Supratik Mukhopadhyay, PhD, is a full Professor at the Center for Computation & Technology (CCT) at Louisiana State University (LSU). Professor Mukhopadhyay led the DeepDrug team (DeepDrug (lsu.edu)) for automated drug discovery using Artificial Intelligence to the semifinals of AI XPRIZE (among 147 teams worldwide), the world’s top competition for using AI for solving moonshot challenges (DeepDrug | AI XPRIZE). Combination therapy discovered by the DeepDrug Artificial Intelligence Platform for COVID-19 has undergone human studies at the Riverside University Health System, California (Riverside University Health System Medical Center, Skymount Medical Begin U.S. Clinical Trial for COVID-19 Oral Therapeutics Discovered Using Artificial Intelligence | County of Riverside (rivco.org)) and in Ukraine. The platform took 13 months from inception to human studies, which is one of the fastest in the pharmaceutical world for combination therapy. DeepDrug has also been approved for human trials by MHRA in the United Kingdom (Skymount Medical Receives UK MHRA Approval to Test New (globenewswire.com)). DeepDrug is the world’s only AI platform to discover a nutraceutical and bring it to market (Home - Inhibinol).
Apart from Drug Discovery, Professor Mukhopadhyay has worked on AI for environmental challenges, education, port and supply chain security, satellite image understanding, video and image analytics, design of intelligent buildings and transportation systems, wildfire prediction and detection, conservation of endangered species and intelligent cyber-physical-human systems, to name a few. His DeepSat framework for satellite imagery understanding forms the basis of NASA Earth Exchange (NEX) (see DeepSAT: A Deep Learning Approach to Tree-Cover Delineation in 1-m NAIP Imagery for the Continental United States - NASA Technical Reports Server (NTRS)).
In the last 13 years, Professor Mukhopadhyay has received more than $9 million in research grants. His research has been funded by the NSF, DARPA, ARO, ONR, NGA, NASA, DOE, USDOT, USDA, NRL, state agencies, and private industry. Professor Mukhopadhyay has published around 125 refereed publications in reputed journals and conferences. He has been awarded 3 US Patents and has 8 US patents pending. Additionally, he has received numerous awards for his research and cofounded a startup, Ailectric, for commercializing his research on sound, video, and image analytics (Ailectric | Home | Artificial Intelligence | Deep Learning). He serves as an associate editor for IEEE Transactions on Artificial Intelligence and Remote Sensing letters and has served in the program committees of AAAI.
Provider approved by the California Board of Registered Nursing, Provider Number CEP17068 for 0.8 contact hours.
Other healthcare professionals will receive a certificate of attendance for 0.5 contact hours.
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