AI in the Clinic: What Could Go Wrong, and How We Can Catch It

Learning Objectives

Upon completion of this activity, participants should be able to:

• Discuss human–computer interaction (HCI) principles that support the safe and effective use of artificial intelligence (AI) in clinical workflows.

• Differentiate between algorithmic performance metrics and system-level safety processes necessary for clinical AI implementation.

• Recognize common sources of error in clinical AI use, including automation bias, alert fatigue, and interface-related trust failures.

• Evaluate strategies for pre-deployment testing, controlled piloting, and establishing human-in-the-loop oversight in AI-assisted decision-making.

In support of improving patient care, Mayo Clinic College of Medicine and Science 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.

ATTENDANCE / CREDIT
Text the session code (provided only at the session) to 507-200-3010 within 48 hours of the live presentation to record attendance. All learners are encouraged to text attendance regardless of credit needs. This number is only used for receiving text messages related to tracking attendance. Additional tasks to obtain credit may be required based on the specific activity requirements and will be announced accordingly. Swiping your badge will not provide credit; that process is only applicable to meet GME requirements for Residents & Fellows.

TRANSCRIPT
Any credit or attendance awarded from this session will appear on your Transcript.

For disclosure information regarding Mayo Clinic School of Continuous Professional Development accreditation review committee member(s) and staff, please go here to review disclosures.