Get ready for re:MARS: The Healthcare and Life Sciences guide to re:MARS 2022
re:MARS is back! This year, re:MARS is happening in person from Las Vegas, NV from June 21-24, delivering cutting edge talks, demos, workshops, and networking opportunities, as well as a rich on-demand option for virtual attendees. The event is offering 100+ sessions spanning Machine Learning, Automation, Robotics, and Space tracks, with six of them focused primarily on healthcare and life sciences use cases.
Learn how AWS experts and customers in biotechnology, academia, and nonprofit are leveraging the cloud to advance their science, business, and mission. If you work in the healthcare or life sciences space and are planning on participating in re:MARS 2022, make sure you’ve done the following:
- Visit the re:MARS 2022 website for all things re:MARS
- Register for re:MARS to attend in person or virtually
- Let your account team know that you will be at re:MARS and if there are any particular questions that you have. We’re happy to help connect you to an AWS healthcare and life sciences specialist who will be in attendance.
For quick reference, here are the titles and abstracts for healthcare and life sciences relevant sessions.
- MLR201-L: Fairness in AI: Discussion with NSF researchers and Amazon: In 2019, Amazon and the National Science Foundation (NSF) announced a $20 million collaboration to fund academic research on fairness in AI over a three-year period. In this session, hear from a panel of select grant recipients of the NSF Fairness in Artificial Intelligence (FAI) program and Amazon researchers on their perspective on FATE topics applied to machine learning, automation, robotics, and space (MARS) themes.
- MLR208: ML methods to accelerate the drug discovery cycle: Behind every drug in the market, there are thousands of compounds that get discarded along the drug development pipeline. Knowing what is going to work before it gets produced is the ultimate goal of drug development. In this session, hear about some of the latest work the Amazon Machine Learning Solutions Lab, in collaboration with customers, has done to get closer to this important goal. Walk through key use cases, solutions, and state-of-the-art approaches that are emerging in this area.
- MLR209: AI for making decisions in clinical medicine: To make decisions about a single patient, today’s clinicians must reconcile growing amounts of diverse health data from multiple sources. Pranav Rajpurkar’s research cuts across computer vision, natural language processing, structured health data, and medical imaging to accelerate and improve the clinical decision-making process. In one study, this research helped reduce the time required to interpret chest X-rays from 240 to 1.5 minutes. By better predicting the healthcare needs of patients in vulnerable populations, the work also resulted in savings of $200 a year per patient. In this session, learn how artificial intelligence can help empower the next generation of clinicians.
- MLR312: Managed federated learning on AWS: A case study for healthcare: A key challenge working with real-world healthcare and life sciences (HCLS) patient data is the siloing of data across multiple hospital systems and research facilities. Regulations prohibit open data sharing, and the complexity and cost of centralized data repositories deter their use. HCLS partners and customers seek privacy-preserving mechanisms for managing and analyzing distributed and sensitive data. In this session, explore a proposed federated learning framework built on AWS, which facilitates training a global machine learning model on distributed healthcare records. Learn about the effectiveness of the framework on eICU, a critical-care database with over 200 contributing hospitals.
- AUT308: Automated drug cartridge counting: The central innovation team at Novo Nordisk has developed an industry-first automated quality assurance system using AWS services to count drug cartridges in real time on the manufacturing line. In this session, learn how they developed a demonstration in which a robotic arm places a box full of drug cartridges on a platform, a camera rig takes images of the box, ML inference is performed at the edge (using an edge device), and the final results are displayed on an Amazon QuickSight dashboard.
- MLR204: Accelerating protein research with AlphaFold: Proteins are the main engines that drive the biological ecosystem. Protein structure sets the foundation for its function, and accurately predicting protein structures can improve and open up many downstream applications, such as drug development. In this chalk talk, learn what AlphaFold, the state-of-the-art protein structure prediction model, can do and possibilities beyond AlphaFold.
- MLR214: Eliminating bias in AI/ML: This session discusses inherit biases in AI/ML. Learn how diversity, equity, and inclusion (DEI) intersect with AI/ML to help build the future of technology.
We look forward to seeing you in Las Vegas!
To learn more about how AWS is helping customers innovate across healthcare and life sciences visit, https://aws.amazon.com/health/