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INTRODUCTION

5/30/2024 Anesthesiology Grand Rounds -Applications of Artificial Intelligence in Anesthesiology

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:

  • Describe different branches of artificial intelligence as it applies to perioperative medicine.
  • Describe research related to AI and opioid use outcomes.
  • Explain research related to multimodal AI models in predicting outcomes in the critically ill.
  • Recognize how AI may be used to optimize perioperative efficiency.

Suggested Additional Reading & 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

  • Gabriel RA, Mariano ER, McAuley J, Wu CL. How large language models can augment perioperative medicine: a daring discourse. Reg Anesth Pain Med 2023 Nov; 48 (11):575-577
  • Wornow M et al. The shaky foundations of large language models and foundation models for electronic health records. NPJ Digit Med. 2023 Jul 29;6(1):135
  • Suh HS, Tully JL, Meineke MN, Waterman RS, Gabriel RA. Identification of preanesthetic history elements by a natural language processing engine. Anesth Analg 2022 Dec 1;135(6):1162-1171

Authors:
Rodney A. Gabriel, MD, MAS — Associate Professor, Anesthesiology| Chief, Division of Regional Anesthesia, Anesthesiology| Clinical Director of Anesthesiology, Koman Outpatient Pavilion, Anesthesiology| Director, Division of Perioperative Informatics , Anesthesiology, University of California, San Diego.
Dr. Gabriel is a consultant for Avanos.
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.
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.