National Government Services (NGS), a Medicare Administrative Contractor (MAC), has proposed denying Medicare coverage for AI-powered brain MRI exams.
This would be done through a Local Coverage Determination (LCD) that would deny coverage for automated detection and quantification of brain MRI technologies, which include CPT codes 0865T and 0866T.
The American College of Radiology (ACR) said the draft policy would affect clinical practices in the following states: Illinois, Minnesota, Wisconsin, Connecticut, New York, Maine, Massachusetts, New Hampshire, Rhode Island, and Vermont.
The proposed LCD and accompanying billing article are now open for public comment through March 8. The ACR said it encourages members, particularly neuroradiologists who use these AI tools, to review the proposals and consider submitting comments.
NGS will host a public open meeting on February 26 from noon to 2 p.m. ET to discuss the draft LCD and hear presentations from clinicians, stakeholders, and the public.
More information can be found on the ACR's site.
![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
