URBANA, Ill., July 8, 2021 – The University of Illinois Urbana-Champaign has launched a new AI in Medicine Certificate program through an interdisciplinary partnership between the department of bioengineering at The Grainger College of Engineering, the Carle Illinois College of Medicine and the University of Illinois College of Veterinary Medicine. This self-paced online certificate program will equip healthcare professionals with a conceptual understanding of AI and its applications through real-world medical case studies using machine learning models. The cost for this program is $750 and Continuing Medical Education (CME) credits are available upon completion issued by the Carle health system and the University of Illinois College of Veterinary Medicine.
Artificial intelligence is poised to make a significant impact throughout healthcare systems. Current applications of AI in healthcare include disease diagnostics, health monitoring, enhancing electronic healthcare records and clinical decision support. However, existing AI training courses on the market are largely targeted for students with coding skills and who plan to develop AI tools. The purpose of this course is to familiarize clinicians with the key concepts and applications of AI in medicine. This course is intended for physicians, physician assistants, medical students, nurse practitioners, veterinarians and veterinary technicians. “We have created this program to meet an urgent need for AI training in the healthcare community,” said Mark A. Anastasio, Carle Illinois College of Medicine professor and Donald Biggar Willett Professor in Engineering and head of the department of bioengineering.
The AI in Medicine program is divided into six modules and delivered by Dr. Kevin R. Teal, a staff neurosurgeon at the Carle Foundation Hospital:
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– -Module 1 – Establish key concepts of artificial intelligence and machine learning and recognize their differences.Module 2 – Deep dive into subfields of machine learning and statistical learning grounded in clinical examples.
Module 3 – Understand how to train artificial neural networks to learn from data through deep learning.
Module 4 – Consider the value of medical data and the role of clinicians in the development and training of machine learning tools.
Module 5 – Examine ethical, regulatory and legal topics while using machine learning models in clinical settings.
Module 6 – Explore real-world applications of AI in medicine for diagnosing diseases, interpreting tests and processing electronic healthcare records.
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