January 13, 2022 AI4C: An Alternative to the Black Box: Strategy Learning
In virtually any practical field or application, discovering and implementing near-optimal decision strategies is essential for achieving desired outcomes, and for avoiding costly mistakes. Finding these strategies, however, usually requires substantial time, risk, and expensive trial and error. Recently, machine learning has been used to attack this problem, but unfortunately, most proposed solutions are “black box” algorithms, with underlying logic unclear to humans, significantly limiting their practical use and scientific value. In this work, we propose an alternative approach: repurposing machine learning to discover optimal, comprehensible strategies which can be understood, transferred, and used by humans directly. Through three common decision-making problems found in scheduling, we demonstrate the implementation and feasibility of this approach, as well as its great potential to attain optimal results.
This lecture series in intended for Physicians of all specialties, nurses, Physician Assistants, Information Technology Specialists, and Researchers interested in Artificial Intelligence in healthcare. Faculty of all levels, students, residents, and fellows welcome. Personnel from the healthcare technology sector may also be interested in attending the series.
Upon completion of this activity, participants will be able to:
- Describe Artificial Intelligence (AI) and Machine Learning (ML) at a high-level.
- Identify current limitations of AI in healthcare.
- Cite examples of AI as applied to imaging and other healthcare data.
- Describe example AI tools that can impact workflow in the healthcare arena.
- Utilize clinical AI applications appropriately.
Massachusetts General Hospital and Brigham & Women's Hospital, Center for Clinical Data Science & Mass General Brigham
Katherine P. Andriole, PhD., FSIIM
Director of Research, Strategy and Operations
MGH & BWH Center for Clinical Data Science
Associate Professor of Radiology
Brigham and Women’s Hospital
Harvard Medical School
Oleg S. Pianykh, PhD
Director of Medical Analytics Group
Massachusetts General Hospital
Assistant Professor of Radiology
Harvard Medical School
In support of improving patient care, Mass General Brigham is jointly accredited by the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC), to provide continuing education for the healthcare team.
Mass General Brigham designates this live activity for a maximum of 1 AMA PRA Category 1 Credit™ per session. Physicians should claim only the credit commensurate with the extent of their participation in the activity.
- 1.00 AMA PRA Category 1 Credit™
- 1.00 Participation