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JULY 8 - 9, 2024 | HILTON | ROCHESTER, MINNESOTA
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We are pleased to invite you to participate in the 2024 Mayo Clinic AI Summit conference titled “AI Summit: Generative & Multimodal AI – Potentials and Challenges" to be held in Rochester, Minnesota on July 8-9, 2024. The conference is organized by The Mayo Clinic Department of Artificial Intelligence and Informatics in partnership with the Mayo Clinic AI community.
Hilton Rochester Mayo Clinic Area
10 E Center Street
Rochester, MN 55904
Digital transformation has brought a paradigm shift to healthcare, powered by increasing availability of healthcare data and rapid progression of analytics techniques including machine learning and AI. Across various healthcare settings, AI plays a significant role in achieving the mission of preventing, diagnosing, and curing diseases, connecting people everywhere, and transforming healthcare. The AI Summit aims to bring AI experts and the healthcare community together to review advances and discuss the potentials and challenges of AI and large multimodal models in healthcare.
You are invited to submit proposals to be considered for presentation through the conference registration and submission website.
Registration (in person and virtual):
$350
Students:
$250
* Please note that participants’ accommodation, travel, and other conference-related expenses will
be borne by participants.
Abstract Submission:
We invite the submission of abstracts to be considered for the
following presentation types and topic areas:
* Abstract submissions must utilize the template
provided on the website and will only be accepted if submitted as a part of the registration
process.
Abstract submission has closed.
Presentation Types:
Lightning Talk (10 minutes)
Poster Presentation
Workshop/Tutorial (up to 3 hour blocks available)
Topic Areas:
The Summit aims to cover various topic areas along the thrusts of the potentials and challenges
associated with generative multimodal AI in healthcare, including but not limited to the following:
Potentials: Generative AI in healthcare, unimodal and multimodal foundation models, virtual reality and augmentation, large language models in healthcare, self-supervision and continuous learning, AI-empowered discovery science, AI-powered diagnostics and treatment, AI for human well-being
Challenges: AI literacy and hype, AI bias, trust and hallucinations, disparities and the digital divide, translational gaps, ethical considerations, data availability and quality, cloud computation, data privacy and security, regulatory compliance and monitoring, workflow integration and training, utility and user experience