Melissa P. Upton, MD
Department of Pathology, University of Washington, Seattle

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

Physician Competencies: Medical Knowledge, Patient Care & Procedural Skills, Practice-Based Learning & Improvement, Systems-Based Practice  

Eligibility for CME/CMLE credit: Max three attempts. You will have a maximum of three attempts to meet the following criteria:

• Module ≥80% = CME credit
• Module <80% = No CME credit, after a maximum of three attempts

Default Credit Type: None (You must meet the eligibility requirements in order to obtain CME credit.)

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.

Credit Designation Statement: The American Society for Clinical Pathology (ASCP) designates this journal-based CE 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.

Instructions

To claim CME/CMLE credit for the exercise, do the following:

1. Review the Disclosure Information.
2. Review the Technical Considerations.
3. Click Go to Course to view an overview of the modules in this course.
4. Click Access to begin the course.
5. Review the Educational Activity.
6. Complete and submit the Post Exam. You will have a maximum of three attempts to obtain the required score. You will be notified if you have met the credit requirements after each attempt.
7. Submit the course Evaluation.
8. Enter the maximum number of credits offered and click Claim CME/CMLE to register credit.

Disclosure Information​
The Journal CME editor has no relevant financial relationships with ineligible companies to disclose.

Technical Considerations

Release Date: 5/17/2024
Review Date:
Expiration Date: 5/17/2027

• List challenges facing pathology laboratories that process and interpret large numbers of prostatic samples.

• Describe applications of artificial intelligence (AI)-based algorithms that correlate with cancer diagnosis, grading, and prostatic predictive variables.

• Discuss limitations to current AI applications and potential technological improvements that may assist in their adoption.