As I checked in at a Manhattan radiology clinic for my annual mammogram in November, the front desk staffer reviewing my paperwork asked an unexpected question: Would I like to spend $40 for an artificial intelligence analysis of my mammogram? It\u2019s not covered by insurance, she added.<\/p>\n

I had no idea how to evaluate that offer. Feeling upsold, I said no. But it got me thinking: Is this something I should add to my regular screening routine? Is my regular mammogram not accurate enough? If this AI analysis is so great, why doesn\u2019t insurance cover it?<\/p>\n

I\u2019m not the only person posing such questions. The mother of a colleague had a similar experience when she went for a mammogram recently at a suburban Baltimore clinic. She was given a pink pamphlet that said: \u201cYou Deserve More. More Accuracy. More Confidence. More power with artificial intelligence behind your mammogram.\u201d The price tag was the same: $40. She also declined.<\/p>\n

In recent years, AI software that helps radiologists detect problems or diagnose cancer using mammography has been moving into clinical use. The software can store and evaluate large datasets of images and identify patterns and abnormalities that human radiologists might miss. It typically highlights potential problem areas in an image and assesses any likely malignancies. This extra review has enormous potential to improve the detection of suspicious breast masses and lead to earlier diagnoses of breast cancer.<\/p>\n

While studies showing better detection rates are extremely encouraging, some radiologists say, more research and evaluation are needed before drawing conclusions about the value of the routine use of these tools in regular clinical practice.<\/p>\n

\u201cI see the promise and I hope it will help us,\u201d said Etta Pisano, a radiologist who is chief research officer at the American College of Radiology<\/a>, a professional group for radiologists. However, \u201cit really is ambiguous at this point whether it will benefit an individual woman,\u201d she said. \u201cWe do need more information.\u201d<\/p>\n

The radiology clinics that my colleague\u2019s mother and I visited are both part of RadNet<\/a>, a company with a network of more than 350 imaging centers around the country. RadNet introduced its AI product for mammography in New York and New Jersey<\/a> last February and has since rolled it out in several other states, according to Gregory Sorensen, the company\u2019s chief science officer.<\/p>\n

Sorensen pointed to research the company conducted<\/a> with 18 radiologists, some of whom were specialists in breast mammography and some of whom were generalists who spent less than 75% of their time reading mammograms. The doctors were asked to find the cancers in 240 images, with and without AI. Every doctor\u2019s performance improved using AI, Sorensen said.<\/p>\n

Among all radiologists, \u201cnot every doctor is equally good,\u201d Sorensen said. With RadNet\u2019s AI tool, \u201cit\u2019s as if all patients get the benefit of our very top performer.\u201d<\/p>\n

But is the tech analysis worth the extra cost to patients? There\u2019s no easy answer.<\/p>\n

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\u201cSome people are always going to be more anxious about their mammograms, and using AI may give them more reassurance,\u201d said Laura Heacock<\/a>, a breast imaging specialist at NYU Langone Health\u2019s Perlmutter Cancer Center in New York. The health system has developed AI models and is testing the technology with mammograms but doesn\u2019t yet offer it to patients, she said.<\/p>\n
Still, Heacock said, women shouldn\u2019t worry that they need to get an additional AI analysis if it\u2019s offered.<\/p>\n
\u201cAt the end of the day, you still have an expert breast imager interpreting your mammogram, and that is the standard of care,\u201d she said.<\/p>\n
About 1 in 8 women<\/a> will be diagnosed with breast cancer during their lifetime, and regular screening mammograms are recommended to help identify cancerous tumors early. But mammograms are hardly foolproof: They miss about 20% of breast cancers, according to the National Cancer Institute.<\/p>\n
The FDA has authorized roughly two dozen AI products to help detect and diagnose cancer from mammograms. However, there are currently no billing codes radiologists can use to charge health plans for the use of AI to interpret mammograms. Typically, the federal Centers for Medicare & Medicaid Services would introduce new billing codes and private health plans would follow their lead for payment. But that hasn\u2019t happened in this field yet and it\u2019s unclear when or if it will.<\/p>\n
CMS didn\u2019t respond to requests for comment.<\/p>\n
Thirty-five percent of women who visit a RadNet facility for mammograms pay for the additional AI review, Sorensen said.<\/p>\n
Radiology practices don\u2019t handle payment for AI mammography all in the same way.<\/p>\n
The practices affiliated with Boston-based Massachusetts General Hospital don\u2019t charge patients for the AI analysis, said Constance Lehman, a professor<\/a> of radiology at Harvard Medical School who is co-director of the Breast Imaging Research Center at Mass General.<\/p>\n
Asking patients to pay \u201cisn\u2019t a model that will support equity,\u201d Lehman said, since only patients who can afford the extra charge will get the enhanced analysis. She said she believes many radiologists would never agree to post a sign listing a charge for AI analysis because it would be off-putting to low-income patients.<\/p>\n
Sorensen said RadNet\u2019s goal is to stop charging patients once health plans realize the value of the screening and start paying for it.<\/p>\n
Some large trials are underway in the United States, though much of the published research on AI and mammography to date has been done in Europe. There, the standard practice is for two radiologists to read a mammogram, whereas in the States only one radiologist typically evaluates a screening test.<\/p>\n
Interim results from the highly regarded MASAI randomized controlled trial<\/a> of 80,000 women in Sweden found that cancer detection rates were 20% higher in women whose mammograms were read by a radiologist using AI compared with women whose mammograms were read by two radiologists without any AI intervention, which is the standard of care there.<\/p>\n
\u201cThe MASAI trial was great, but will that generalize to the U.S.? We can\u2019t say,\u201d Lehman said.<\/p>\n
In addition, there is a need for \u201cmore diverse training and testing sets for AI algorithm development and refinement\u201d across different races and ethnicities, said Christoph Lee, director of the Northwest Screening and Cancer Outcomes Research Enterprise at the University of Washington School of Medicine. \u00a0<\/p>\n
The long shadow of an earlier and largely unsuccessful type of computer-assisted mammography hangs over the adoption of newer AI tools<\/a>. In the late 1980s and early 1990s, \u201ccomputer-assisted detection\u201d software promised to improve breast cancer detection. Then the studies started coming in, and the results were often far from encouraging. Using CAD at best provided no benefit, and at worst reduced the accuracy of radiologists\u2019 interpretations, resulting in higher rates of recalls and biopsies.<\/p>\n
\u201cCAD was not that sophisticated,\u201d said Robert Smith, senior vice president<\/a> of early cancer detection science at the American Cancer Society. Artificial intelligence tools today are a whole different ballgame, he said. \u201cYou can train the algorithm to pick up things, or it learns on its own.\u201d<\/p>\n
Smith said he found it \u201ctroubling\u201d that radiologists would charge for the AI analysis.<\/p>\n
\u201cThere are too many women who can\u2019t afford any out-of-pocket cost\u201d for a mammogram, Smith said. \u201cIf we\u2019re not going to increase the number of radiologists we use for mammograms, then these new AI tools are going to be very useful, and I don\u2019t think we can defend charging women extra for them.\u201d<\/p>\n
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