Artificial Intelligence and Genomic Diagnostics (WG-AIGD)

Membership

The IFCC Emerging Technology Division (ETD) is dedicated to providing current awareness for emerging technologies likely to have important clinical diagnostic applications in the near future. Artificial intelligence (AI) is an important emerging technology, and in the future, it is likely that this technology will become embedded in many aspects of medicine.  In particular, it is expected to play a key role in laboratory medicine.  Accordingly, a familiarity with this technology and its scope, applications, accessibility, and limitations will become important in the practice of laboratory medicine in the future.  The focus of this WG is the area defined by the intersection of artificial intelligence, genomics, and clinical diagnostics.

Terms of Reference

1. To evaluate and monitor emerging trends and directions of research and development in the field defined by the intersection of artificial intelligence, genomics, and clinical diagnostics.
2. To develop an in-depth assessment of the application of AI (deep learning, machine learning) in genomic (molecular) diagnosis.
3. To develop periodic updates of the applications of AI in clinical genomic testing.
4. To assess the accessibility and the barriers to routine implementation of AI in clinical genomic testing.
5. To develop a resource that will inform the IFCC community on developments and trends in the applications of artificial intelligence in clinical genomic

Current projects

1. Survey of the clinical diagnostic applications of AI in genomics, including recent literature, companies, clinical diagnostic products, and clinical trials.
2. Solicit industry and academia input into the AI in clinical genomics survey.
3. Assess the role of AI in genomic tests for detecting COVID-19.
4. Explore the utility of AI-based search engines in searching the AI and genomics literature for emerging diagnostic applications
5. Formulate consensus definitions of AI and other terms relevant to the application of AI in clinical laboratories (some examples of AI glossaries can be found at:
   • https://developers.google.com/machine
   • learning/glossary;https://www.stottlerhenke.com/artificial- intelligence/glossary/;
   • https://appen.com/ai-glossary/)
6. Develop recommendations/best practices for clinical laboratories validating and/or evaluating AI-based diagnostic or prognostic methods (e.g., minimum elements of a method that need to be shared to evaluate and/or validate a method).

Working Group Chair's contact

Prof Larry KRICKA
Department of Pathology and Laboratory Medicine
University of Pennsylvania Medical Center
3400 Spruce Street
Philadelphia, PA 19104 - USA
Email: kricka@pennmedicine.upenn.edu