Emerging AI Software Gets FDA Clearance for Diagnosing Breast and Thyroid Cancer

Article

Will this artificial intelligence technology improve diagnostic accuracy and reduce false positive biopsy orders?

Koios DS, an artificial intelligence (AI)-based software platform that reportedly improves the accuracy and efficiency of diagnosing breast and thyroid cancer, recently garnered clearance from the Food and Drug Administration (FDA), according to the software manufacturer Koios Medical.

A recent multicenter retrospective review involving the assessment of 900 breast lesions showed that the addition of Koios DS Breast to ultrasound imaging improved correct BI-RADS classification of sonographic breast lesions by 14 of the 15 physicians participating in the study. 

For thyroid cancer, Koios Medical reported a 14 percent increase in detection rates with its Thyroid DS platform and more than a 35 percent reduction in false positive biopsy orders. The company also noted a greater than 50 percent reduction in interpretation variability.

Built with ultrasound data from a worldwide, 48-site network, the AI-generated findings with Koios DS are directly aligned with the BI-RADS and TI-RADS rating systems from the American College of Radiology, according to Koios Medical. The company added that the software is also aligned with the American Thyroid Association’s system for tissue classification and scoring. 

“ … This novel software demonstrates that (by) using AI for decision support, physicians can make clinically meaningful shifts in performance, improving interpretation efficacy and diagnostic performance, improving sensitivity and reducing false positives,” noted Lev Barinov, VP of clinical excellence and product management at Koios Medical. 

On the reimbursement front, Koios Medical pointed to new CPT Category 3 codes from the American Medical Association that clinicians may employ to bill for use of the Koios DS software in interpreting, classifying and reporting findings from traditional ultrasound examinations.

Recent Videos
New Mammography Studies Assess Image-Based AI Risk Models and Breast Arterial Calcification Detection
Can Deep Learning Provide a CT-Less Alternative for Attenuation Compensation with SPECT MPI?
Employing AI in Detecting Subdural Hematomas on Head CTs: An Interview with Jeremy Heit, MD, PhD
Pertinent Insights into the Imaging of Patients with Marfan Syndrome
How Will the New FDA Guidance Affect AI Software in Radiology?: An Interview with Nina Kottler, MD, Part 2
How Will the New FDA Guidance Affect AI Software in Radiology?: An Interview with Nina Kottler, MD, Part 1
Teleradiology and Breast Imaging: Keys to Facilitating Personalized Service, Efficiency and Equity
Radiology Study Finds Increasing Rates of Non-Physician Practitioner Image Interpretation in Office Settings
Addressing the Early Impact of National Breast Density Notification for Mammography Reports
Where the USPSTF Breast Cancer Screening Recommendations Fall Short: An Interview with Stacy Smith-Foley, MD
Related Content
© 2025 MJH Life Sciences

All rights reserved.