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INTRODUCTION

12/19/24 - Anesthesiology Grand Rounds-How to Consume and Critique AI literature

QUIZ

EVALUATION

CERTIFICATE

INTRODUCTION

Credit Hours: CME 1.00

Target Audience:

Anesthesiologists and anesthesiologists-in-training and other anesthesia professionals, nurse anesthetists and anesthesia assistants.

Educational Objectives:

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

  • Examine the landscape of AI-based technologies in healthcare today.
  • Identify popular AI and ML-based techniques.
  • Evaluate the importance of critiquing AI and ML literature published in healthcare.
  • Enhance confidence in reviewing AI and ML literature.

Joint Accreditation Statement - Note: This Accreditation Statement Supersedes All Other Statements:

Joint Accreditation Statement:

In support of improving patient care, the University of Pittsburgh 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.


The University of Pittsburgh School of Medicine is accredited by the ACCME to provide continuing medical education for physicians. The University of Pittsburgh School of Medicine designates this enduring material activity for a maximum of 1.0 AMA PRA Category 1 Credit™. Each physician should only claim credit commensurate with the extent of their participation in the activity. 


Other health care professionals will receive a certificate of attendance confirming the number of contact hours commensurate with the extent of participation in this activity.


Suggested Additional Reading:

  1. Artificial intelligence a modern approach: 4th Edition (Book) Stuart Russel and Peter Norvig
  2. Pattern recognition and Machine learning: Christopher Bishop  (Book)
  3. Mathis, M., Steffner, K. R., Subramanian, H., Gill, G. P., Girardi, N. I., Bansal, S., ... & Huang, J. (2024). Overview and Clinical Applications of Artificial Intelligence and Machine Learning in Cardiac Anesthesiology. Journal of Cardiothoracic and Vascular Anesthesia 

Authors:
Harikesh Subramanian, MBBS, MS, D.ABA, ABPM-CI — Assistant Professor, Department of Anesthesiology & Perioperative Medicine, University of Pittsburgh
No planners, members of the planning committee, speakers, presenters, authors, content reviewers and/or anyone else in a position to control the content of this education activity have relevant financial relationships to disclose.
No other members of the planning committee, speakers, presenters, authors, content reviewers and/or anyone else in a position to control the content of this education activity have relevant financial relationships with any companies whose primary business is producing, marketing, selling, re-selling, or distributing healthcare products used by or on patients.

This activity is approved for AMA PRA Category 1 Credit™

The University of Pittsburgh is an affirmative action, equal opportunity institution.