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Cancer Screening, Prevention, Control

Artificial Intelligence in Breast Cancer ScreeningEvaluation of FDA Device Regulation and Future Recommendations

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1 Credit CME

JAMA Internal Medicine

Published Online: November 7, 2022

Special Communication

Article Information and Disclosures

Kunal C. Potnis, BS¹;

Joseph S. Ross, MD, MHS^2,3,4;

Sanjay Aneja, MD^3,5;

Cary P. Gross, MD^2,6,7;

Ilana B. Richman, MD, MHS^2,6

Author Affiliations

¹Yale School of Medicine, New Haven, Connecticut
²Section of General Medicine, Department of Medicine, Yale School of Medicine, New Haven, Connecticut
³Center for Outcomes Research and Evaluation, Yale School of Medicine, New Haven, Connecticut
⁴Department of Health Policy and Management, Yale School of Public Health, New Haven, Connecticut
⁵Department of Therapeutic Radiology, Yale School of Medicine, New Haven, Connecticut
⁶Cancer Outcomes, Public Policy, and Effectiveness Research Center, Yale School of Medicine, New Haven, Connecticut
⁷Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, Connecticut

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Abstract

Importance Contemporary approaches to artificial intelligence (AI) based on deep learning have generated interest in the application of AI to breast cancer screening (BCS). The US Food and Drug Administration (FDA) has approved several next-generation AI products indicated for BCS in recent years; however, questions regarding their accuracy, appropriate use, and clinical utility remain.

Objectives To describe the current FDA regulatory process for AI products, summarize the evidence used to support FDA clearance and approval of AI products indicated for BCS, consider the advantages and limitations of current regulatory approaches, and suggest ways to improve the current system.

Evidence Review Premarket notifications and other publicly available documents used for FDA clearance and approval of AI products indicated for BCS from January 1, 2017, to December 31, 2021.

Findings Nine AI products indicated for BCS for identification of suggestive lesions and mammogram triage were included. Most of the products had been cleared through the 510(k) pathway, and all clearances were based on previously collected retrospective data; 6 products used multicenter designs; 7 products used enriched data; and 4 lacked details on whether products were externally validated. Test performance measures, including sensitivity, specificity, and area under the curve, were the main outcomes reported. Most of the devices used tissue biopsy as the criterion standard for BCS accuracy evaluation. Other clinical outcome measures, including cancer stage at diagnosis and interval cancer detection, were not reported for any of the devices.

Conclusions and Relevance The findings of this review suggest important gaps in reporting of data sources, data set type, validation approach, and clinical utility assessment. As AI-assisted reading becomes more widespread in BCS and other radiologic examinations, strengthened FDA evidentiary regulatory standards, development of postmarketing surveillance, a focus on clinically meaningful outcomes, and stakeholder engagement will be critical for ensuring the safety and efficacy of these products.

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Article Information

Accepted for Publication: September 13, 2022.

Published Online: November 7, 2022. doi:10.1001/jamainternmed.2022.4969

Corresponding Author: Ilana B. Richman, MD, MHS, Yale School of Medicine, 367 Cedar St, Room 301a, New Haven, CT 06511 (ilana.richman@yale.edu).

Author Contributions: Mr Potnis and Dr Richman had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Potnis, Ross, Gross, Richman.

Acquisition, analysis, or interpretation of data: Potnis, Ross, Aneja, Richman.

Drafting of the manuscript: Potnis, Richman.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Richman.

Obtained funding: Richman.

Supervision: Gross, Richman.

Conflict of Interest Disclosures: Mr Potnis reported grants from the US National Institutes of Health (NIH) and grants from the American Society of Hematology outside the submitted work. Dr Ross reported grants from the US Food and Drug Administration, Johnson & Johnson, the Medical Devices Innovation Consortium, Agency for Healthcare Research and Quality (AHRQ), grants from the NIH and Arnold Ventures outside the submitted work; and serving as an expert witness at the request of Relator’s attorneys in a qui tam suit alleging violations of the False Claims Act and Anti-Kickback Statute against Biogen, all outside the submitted work. Dr Aneja reported grants from the US National Science Foundation, from the US National Cancer Institute (NCI), Patterson Family Trust, American Society of Clinical Oncology, the Radiation Society of North America, the American Cancer Society, and AHRQ, and nonfinancial support from Amazon, all outside the submitted work. Dr Gross reported grants from Johnson & Johnson, funding from AstraZeneca, and personal fees from Genentech outside the submitted work. Dr Richman reported funding from the NCI and salary support from the Centers for Medicare & Medicaid Services outside of the submitted work. No other disclosures were reported.

Funding/Support: Dr Richman received funding for this study from the US National Institutes of Health (No. K08 CA248725).

Role of the Funder/Sponsor: The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

AMA CME Accreditation Information

Credit Designation Statement: The American Medical Association designates this Journal-based CME activity activity for a maximum of 1.00  AMA PRA Category 1 Credit(s)™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.

Successful completion of this CME activity, which includes participation in the evaluation component, enables the participant to earn up to:

1.00 Medical Knowledge MOC points in the American Board of Internal Medicine's (ABIM) Maintenance of Certification (MOC) program;;
1.00 Self-Assessment points in the American Board of Otolaryngology – Head and Neck Surgery’s (ABOHNS) Continuing Certification program;
1.00 MOC points in the American Board of Pediatrics’ (ABP) Maintenance of Certification (MOC) program;
1.00 Lifelong Learning points in the American Board of Pathology’s (ABPath) Continuing Certification program; and
1.00 credit toward the CME [and Self-Assessment requirements] of the American Board of Surgery’s Continuous Certification program

It is the CME activity provider's responsibility to submit participant completion information to ACCME for the purpose of granting MOC credit.