A group of 15 patient advocacy organizations have urged lawmakers to work with the U.S. Centers for Medicare and Medicaid Services (CMS) to establish a formalized payment pathway for AI healthcare services.
In a May 13 letter to representatives Kay Granger (R-TX) and Rosa DeLauro (D-CT) and senators Patty Murray (D-WA) and Susan Collins (R-ME), the groups noted that in radiology, AI is being used to help read and interpret images and help make more informed diagnoses.
“From helping a physician detect polyps in a colonoscopy to prioritizing a scan showing a doctor that a patient needs an emergency intervention, AI in medical imaging benefits patients,” the groups wrote.
Currently, CMS has only assigned payment for less than 10 AI applications, compared with the 600 plus AI-enabled medical devices that the U.S. Food and Drug Administration has approved or cleared, the groups noted. Ultimately, the groups said that it is essential for these technologies to reach all patients, regardless of their location or socioeconomic status.
“We cannot afford to exacerbate existing healthcare disparities by restricting access to cutting-edge AI-enabled medical devices only to those who can pay out-of-pocket,” the groups wrote.
In related news, on May 9, American College of Radiology CEO William Thorwarth Jr., MD, issued a nine-page letter to Congress recommending how to solve the reimbursement problem for AI in healthcare.
![Representative example of a 16-year-old male patient with underlying X-linked adrenoleukodystrophy. (A, B) Paired anteroposterior (AP) chest radiograph and dual-energy x-ray absorptiometry (DXA) report shows lumbar spine (L1 through L4) areal bone mineral density (BMD). The DXA report was reformatted for anonymization and improved readability. The patient had low BMD (Z score ≤ −2.0). (C) Model (chest radiography [CXR]–BMD) output shows the predicted raw BMD and Z score in comparison with the DXA reference standard, together with interpretability analyses using Shapley additive explanations (SHAP) and gradient-weighted class activation maps. The patient was classified as having low BMD, consistent with the reference standard. AM = age-matched, DEXA = dual-energy x-ray absorptiometry, RM2 = room 2, SNUH = Seoul National University Hospital, YA = young adult.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/04/ai-children-bone-density.0snnf2EJjr.jpg?auto=format%2Ccompress&fit=crop&h=100&q=70&w=100)
![Representative example of a 16-year-old male patient with underlying X-linked adrenoleukodystrophy. (A, B) Paired anteroposterior (AP) chest radiograph and dual-energy x-ray absorptiometry (DXA) report shows lumbar spine (L1 through L4) areal bone mineral density (BMD). The DXA report was reformatted for anonymization and improved readability. The patient had low BMD (Z score ≤ −2.0). (C) Model (chest radiography [CXR]–BMD) output shows the predicted raw BMD and Z score in comparison with the DXA reference standard, together with interpretability analyses using Shapley additive explanations (SHAP) and gradient-weighted class activation maps. The patient was classified as having low BMD, consistent with the reference standard. AM = age-matched, DEXA = dual-energy x-ray absorptiometry, RM2 = room 2, SNUH = Seoul National University Hospital, YA = young adult.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/04/ai-children-bone-density.0snnf2EJjr.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
