The UF College of Medicine, the Herbert Wertheim College of Engineering and the Intelligent Critical Care Center (IC3), in collaboration with the Bridge2AI CHoRUS network, are proud to present the AI for Clinical Care Workshop. The workshop will provide skill and professional development in AI for a diverse group of trainees, physicians, and early stage investigators in medicine. Artificial Intelligence (AI) tools are increasingly used for diagnostic, monitoring, and prognostic applications in clinical medicine, particularly in acute and critical care settings. In the long term, the success of development and implementation of AI in Clinical Care requires close collaboration between scholars, healthcare practitioners, private partners, and community stakeholders. This workshop aims to enhance AI in Clinical Care skills in the clinical research workforce. It will be based on a highly interdisciplinary and integrated skill and workforce development approach. Our fundamental goal is to support research skill development and training in medical AI for the healthcare workforce, for practitioners at all levels from pre-doctoral to faculty. The workshop will consist of two tracks: beginner and advanced. We will employ a competitive selection process to select workshop participants. Ten applicants will receive travel grants to facilitate attending the meeting (details below).
Venue and Dates
Please note that the Workshop will be at a different venue than the UF AI4Health CME Conference.
The AI for Clinical Care Workshop will be held April 19, 2023 at the UF Research and Academic Center at Lake Nona Campus (6550 Sanger Road, Orlando, FL 32827).
This workshop will precede the UF AI4Health CME Conference, which will take place April 20-22 at the Disney World Yacht Club Resort (approximately 30 minutes from the Lake Nona campus).
For the beginner track, micro-learning tutorials built on the UF AI Boot Camp curricula will be modified and taught by CHoRUS faculty and UF College of Medicine AI faculty. Participants will be guided through several hands-on example applications of AI in Clinical Care via interactive Python programming notebooks that showcase AI/ML methods applied to real-world clinical datasets. Instructional material will combine didactic with experiential learning. The beginner track assumes no prior programming experience and is aimed primarily at AI/ML novices.
Workshop material will consist of brief case studies that articulate and evaluate AI/ML practices and contribute to the development of AI in Clinical Care domain knowledge. For this track, we will accept 40 total participants. Thirty slots are open to practicing clinicians or students who are interested in obtaining a basic certification in AI in healthcare.
Before the workshop (self-paced prerequisite instruction available upon registration):
- Introduction to Python (syntax, variables, libraries, loops, conditionals, functions)
- Introduction to Version Control: Reproducibility with git and Github
- FAIR Data for AI (Findable, Accessible, Interoperable, Reusable)
For the advanced track, we will leverage our existing partnership with NVIDIA to train instructors. Participants will receive training on deep learning with emphasis on medical AI and use cases based on CHORUS data. The participants will also receive training on classroom delivery skills as well as on effective use of NVIDIA tools. We will especially encourage applicants from partnering Tribal and HBCU institutions. This session will be open to up to 40 participants.
This training will focus on MONAI (Medical Open Network for Artificial Intelligence). Project MONAI was developed by NVIDIA and King’s College London to establish an inclusive community of AI researchers for the development and exchange of best practices for AI in healthcare imaging across academia and enterprise researchers.
In order to participate in the advanced track, it is recommended that learners have some experience with:
- Python tools such as NumPy and pandas
- Pytorch (MONAI is built with Pytorch framework)
- Some idea of how to use a cluster is needed (can take any online course on Slurm for Beginners)
- Understanding medical image data types and workflows for AI applications preferred, but not required
- Knowledge of labelers is not required (will be reviewed in the workshop)