writes Michelle R. Kaufman<\/u><\/a>, PhD, MA, assisted by Ai Yajuan. \u201cFor everyone to benefit from the predicted future breakthroughs, AI developers should work to rectify a long-standing inequity in medical research: the systemic bias against women.\u201d<\/p>\nPart of this relates to the practice of radiology, through which so many types of serious conditions are diagnosed. AI has really had a big impact in radiology, in helping clinicians to read scans. What does that mean for women\u2019s health?<\/p>\n
A Woman\u2019s Perspective<\/h2>\n
At our Imagination in Action event April 9 and 10, Boston Globe reporter Aaron Pressman interviewed Connie Lehman of Clarity, Inc. about her journey to figure out the impact of AI on women\u2019s health.<\/p>\n
\u201cI was so interested in women’s health and global health and access around the world to better health care,\u201d Lehman said, setting the stage for a discussion about advances in this area. \u201cMy friends were surprised that I got really excited about radiology. They just didn’t think it was a fit for me. But the power of the image, I thought it was so incredible and so untapped in healthcare, just the power of imaging the body, and the technology by which we can take images outside and inside the body. So I started in that domain.\u201d<\/p>\n
She talked about the limitations of the traditional mammogram.<\/p>\n
\u201cThe mammogram isn’t enough,\u201d Lehman said. \u201cSome women really need contrast enhanced imaging where you can see blood flow and vascular flow inside the body, such as in an MRI or contrast enhanced mammogram.\u201d<\/p>\n
Approaching analysis on a case by case basis, she said, is important.<\/p>\n
\u201cEvery hammer isn’t the right hammer for every single problem, and we were using mammography as a very blunt tool, and we didn’t have ways to identify the right test in the right patient at the right time, so we’re really stuck in a very crude age, based on a one size fits all screening paradigm,\u201d Lehman noted.<\/p>\n
Timelines of Care<\/h2>\n
Describing early research on this type of technology in the 1970s, Lehman explained that women\u2019s healthcare also got a shot in the arm when an MIT professor named Regina Barzilay found herself diagnosed, and when broadcast professional Katie Couric also battled breast cancer.<\/p>\n
\u201cShe was shocked by how little science was being used to determine exactly what she should do next,\u201d Lehman said, of Barzilay\u2019s story. \u201cAnd so she felt that this was a domain where her lab could really help change the field.\u201d<\/p>\n
Couric\u2019s case, she said, exemplifies the sadly common scenario where someone gets breast cancer despite an absence of family medical history.<\/p>\n
She also pointed to challenges with the data, and the process.<\/p>\n
\u201cI think we are always battling, maybe in humanity, but definitely in healthcare,\u201d she said. \u201cWe are uncomfortable with change, so we have to look at our best tools for change management, and how to have buy-in, and how to have interest, and how to have science lead our decision making.\u201d<\/p>\n
Data Stores<\/h2>\n
There\u2019s really no lack of data here, Lehman conceded.<\/p>\n
\u201cWe have a huge mass of data around the globe, and within the U.S., very, very large numbers of women being screened with mammography every year,\u201d she said. \u201cSo much of what we do in breast cancer and breast imaging is about these very large databases.\u201d<\/p>\n
Lehman pointed to the use of something called a \u201cclarity score\u201d as a more targeted rating of numerous mammography scans. This type of profile, she suggested, can boost the power of diagnosis in clinical environments.<\/p>\n
\u201cThe input to the model is the four basic views of a woman’s breast tissue that’s obtained with every screening mammogram,\u201d she explained. \u201cSo two views of the right breast, two views of the left breast, those four images go into the model. The model extracts the predictive data and provides a percent score of the likelihood of that woman to develop breast cancer in the next five years. So it’s a five year AI-mammogram-based risk score.\u201d<\/p>\n
Diversity in Patient Audiences<\/h2>\n
\u201cJust because you’re at increased risk doesn’t mean you’ll stay there,\u201d she said, noting that with the new tools, doctors can figure out a woman\u2019s changing risk rating, provide the right information to support ongoing risk management, and make better care decisions.<\/p>\n
She also talked about diversity as a major goal for healthcare, and a global consortium hard at work on these issues, while acknowledging that, in this sense, the medical world still has a long way to go.<\/p>\n
\u201cThroughout the history of medicine, we have really fallen down in the area of ensuring that our studies include the full diversity of patients at risk of that disease. And we’re not there yet. We think we are. Still, at NIH, we have studies in mice that don’t include female mice because their cycles really mess up the data. Think about it, all the doses that we’re getting in our medications as women are based on men, and it is a real challenge.\u201d<\/p>\n
There\u2019s a lot more in the interview, about patient equity, the FDA process, and other happenings around this part of a vastly important sector. It illustrates how using AI can actually even out patient care in key areas, and how to enhance diagnosis when it counts. Stay tuned for more from our spring conference.<\/p>\n<\/div>\n
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