AI and Machine Learning in Clinical Genomics
Working Group Charter
Artificial Intelligence (AI) and Machine Learning (ML) in Clinical Genomics
Work Product Category
Manuscript or Publication
Specific Aims for the Work Product
Evaluate the current understanding, proficiency, and utilization of AI/ML concepts and tools in cancer genomics field, by developing and administering a comprehensive survey to assess knowledge, attitudes, and real-world integration of AI/ML technologies into clinical workflows.
Identify key focus areas, priorities, and perceived challenges for the implementation of AI/ML-driven tools in clinical genomics by survey findings, and to develop expert recommendations through a review of relevant literature.
Despite the significant potential of AI-driven tools and the growing enthusiasm among professionals, there is currently no baseline understanding of how AI is implemented within clinical laboratories. The limitations and potential risks associated with adopting AI are often neither well documented nor effectively communicated. To date, major professional societies have yet to issue formal guidelines or recommendations for the best use of AI in clinical genomics, and available educational resources are limited. The working group aims to conduct a survey to assess how AI/ML-based tools are currently integrated into clinical workflows, the specific types of tasks they support, and the level of understanding and proficiency with AI/ML concepts among professionals will be highly beneficial. This approach will help us establish a baseline understanding of AI/ML use in clinical genomics and identify key focus areas for developing guidelines and recommendations. Ultimately, this study will lay the groundwork for creating best practice strategies and highlight any gaps or barriers in the adoption of AI/ML within clinical laboratories.
Working Group Members
Greg Corboy
Pathology Queensland
Min Fang
Fred Hutchinson Cancer Center
QRashmi Kanagal-Shamanna
MD Anderson Cancer Center
Aiko Otsubo
University of Michigan
Thuy Phung
Univaersity of Texas Health San Antonio
Beth Pitel
Mayo Clinic
TBD