Artificial intelligence is everywhere and is becoming increasingly embedded not only in research but in clinical practice. After defining and exploring what is meant by "artificial intelligence" and potential applications in pathology, we will show how to understand the basic principles and components that go into developing AI models in a simple and understandable manner without the need for complex mathematical formulas. From this mechanistic understanding, we will show how and why AI models are biased and the effect such bias has and may have. We will explore ways to recognize this bias and mechanisms to mitigate bias in their development. Throughout the discussion, we will highlight areas of ethical challenges and dilemmas. 

Faculty/Authors

Emma E. Furth, MD, FASCP

Physician Competencies: Patient Care, Medical Knowledge, Professionalism, Interpersonal Skills Communication

CME/CMLE Credit: 1.0
Estimated Completion Time: 1 hour
Format: Online Educational Activity

Accreditation Statement: The American Society for Clinical Pathology (ASCP) is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education (CME) for physicians. This activity has been planned and implemented in accordance with the Essential Areas and Policies of the Accreditation Council for Continuing Medical Education (ACCME).

Credit Designation Statement: The American Society for Clinical Pathology (ASCP) designates this journal-based CME activity for a maximum of 1 AMA PRA Category 1 Credit(s)™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.