From Hesitation to Integration: Evidence-Based Adoption of Breast Imaging AI for Productivity & Earlier Detection

Dr. David Gruen is a member of Advanced Radiology Consultants in
Connecticut, and the former IBM Watson Health Chief Medical Officer, specializing in women’s imaging and body imaging. He lectures frequently on imaging and healthcare technology, including Artificial Intelligence in healthcare.

After receiving his medical degree from the Weill-Cornell University
Medical College, he completed a radiology residency at the New York
Hospital- Weill Cornell Medical Center, followed by a post-doctoral
fellowship at Memorial Sloan Kettering- Cancer Center. He received his MBA from the Isenberg School of Management at the University of
Massachusetts.

Dr. Gruen is a Fellow of the American College of Radiology and serves on several national committees of the American College of Radiology and Society of Breast Imaging, including patient centered care and breast imaging economics. Dr. Gruen co-authored ACR guidelines on the detection of breast carcinoma, and is a national ACR accreditation
reviewer for mammography and breast biopsy programs. He has lectured and published extensively on breast care and AI, and was the first radiologist in the country to become a program inspector for the National Accreditation Program for Breast Centers (NAPBC).

Educational Objectives

Upon completion of this activity, the participant will be able to:

• Evaluate recent peer-reviewed studies of breast AI (2D/ DBT mammography) and understand their impact on key screening metrics (cancer detection rate, recall, PPV) for use in departmental decision-making.
• Examine clinical cases—including dense-breast and subtle lesion presentations—to identify when and how AI changes management or prompts additional workups.
• Understand the quantifiable productivity effects by comparing AI-assisted workflows (triage, second-reader, concurrent) against baseline read times and volumes in DBT environments.
• Understand the method to estimate ROI using a transparent calculator (inputs: exam volume, reading-time change, recall/biopsy mix, reimbursement/costs) to justify adoption with cost-effectiveness literature relevant to breast screening.
• Evaluate the steps of an implementaton checklist (governance, QA, bias/safety monitoring, staff training, IT integration) to safely introduce AI while maintaining quality standards and addressing common adoption hesitations documented in needs assessments

ACCREDITATION

World Class CME is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians.

World Class CME designates this activity for a maximum of 1.0 AMA PRA Category 1 Credit™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.

The American Society of Radiologic Technologists has approved this activity as follows:

This activity may be available in multiple formats or from different sponsors.  A self-learning activity can be completed only once per biennium.

Physician Assistants (PA)
AAPA accepts AMA PRA Category 1 Credit™ for organizations accredited by the ACCME.

Nurse Practitioners and RNs
The American Nurses Credentialing Center and the American Academy of Nurse Practitioners Certification Program accept continuing education credits sponsored by organizations, agencies, or educational institutions accredited or approved by the ACCME.

DISCLOSURE

World Class CME relies on its program directors and faculty to provide continuing medical education content that is relevant, balanced and evidence-based. Therefore, in accordance with ACCME standards, all program directors, faculty, and reviewer relevant financial relationships with ineligible companies will be disclosed and mitigated. Speakers are required to openly disclose any limitations of data and/or any off-label, experimental or investigational uses of drugs or devices in their presentations.

None of the program directors/planners for this educational activity have relevant financial relationship(s) to disclose with ineligible companies whose primary business is producing, marketing, selling, re-selling, or distributing healthcare products used by or on patients.

This activity is supported by an unrestricted educational grant from Lunit.