Performance of Artificial Intelligence–Powered ECG Analysis in Suspected STEMI
Type:
Journal
Credits:
1
Description:
Cost: $10
NOTE: Course is free to HSHS colleagues, please contact [email protected] to get discount code if you do not have it already.
Hardware/Software Requirements:
High speed connection to the Internet. Browser: the most current version of Microsoft Internet Explorer, Chrome, Safari or Mozilla Firefox. The ability to access and view Adobe pdf documents, Adobe Reader and Adobe Flash Player are recommended. To download/update Adobe Reader and Flash Player, go to http://www.adobe.com/ and follow download the instructions. Printer access recommended to print completion certificate.
Format:
The teaching method for this program will consist of a Journal article with corresponding quiz. Participants will use what they have learned in the article, to complete a follow-up evaluation and quiz to assess new knowledge acquisition. Participants must answer 2 out of 3 questions correctly to pass this course. The program should take approximately 60 minutes to complete.
References:
Access to appropriate bibliographic sources are available at the end of the activity to allow for further study.
Confidentiality Statement:
We must protect the confidentiality of the information handled by Prairie/HSHS CME and Prairie Education and Research Cooperative. We will take precautions to avoid improper, inappropriate or inadvertent disclosures of sensitive, confidential or privileged information, records or documents.
Continuing Medical Education:
Prairie/HSHS is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians.
Prairie/HSHS designates this educational activity for a maximum of 1.0 AMA PRA Category I Credits ™. Physicians should only claim credit commensurate with the extent of their participation in the activity.
NOTE: Course is free to HSHS colleagues, please contact [email protected] to get discount code if you do not have it already.
Hardware/Software Requirements:
High speed connection to the Internet. Browser: the most current version of Microsoft Internet Explorer, Chrome, Safari or Mozilla Firefox. The ability to access and view Adobe pdf documents, Adobe Reader and Adobe Flash Player are recommended. To download/update Adobe Reader and Flash Player, go to http://www.adobe.com/ and follow download the instructions. Printer access recommended to print completion certificate.
Format:
The teaching method for this program will consist of a Journal article with corresponding quiz. Participants will use what they have learned in the article, to complete a follow-up evaluation and quiz to assess new knowledge acquisition. Participants must answer 2 out of 3 questions correctly to pass this course. The program should take approximately 60 minutes to complete.
References:
Access to appropriate bibliographic sources are available at the end of the activity to allow for further study.
Confidentiality Statement:
We must protect the confidentiality of the information handled by Prairie/HSHS CME and Prairie Education and Research Cooperative. We will take precautions to avoid improper, inappropriate or inadvertent disclosures of sensitive, confidential or privileged information, records or documents.
Continuing Medical Education:
Prairie/HSHS is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians.
Prairie/HSHS designates this educational activity for a maximum of 1.0 AMA PRA Category I Credits ™. Physicians should only claim credit commensurate with the extent of their participation in the activity.
Objectives:
Evaluate the limitations of conventional ECG criteria in diagnosing acute coronary occlusion in suspected STEMI patients.
Interpret the diagnostic performance of AI-powered ECG models, including sensitivity, specificity, and clinical applicability.
Integrate AI-assisted ECG analysis into clinical decision-making for suspected acute myocardial infarction. Differentiate clinical scenarios where AI-ECG may improve detection versus situations requiring additional clinical correlation.
Interpret the diagnostic performance of AI-powered ECG models, including sensitivity, specificity, and clinical applicability.
Integrate AI-assisted ECG analysis into clinical decision-making for suspected acute myocardial infarction. Differentiate clinical scenarios where AI-ECG may improve detection versus situations requiring additional clinical correlation.
