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Eugenia Chock, MD, MPH, aims to improve the care of women with autoimmune conditions. Current screening for these disorders often results in delayed care, she says.

An assistant professor of medicine (rheumatology, allergy and immunology) at Yale School of Medicine (YSM) and Yale Center for Clinical Investigation Scholar, Chock researches maternal health and offspring outcomes among patients with rheumatic diseases. She is interested in utilizing large clinical datasets to support her work.

Recently, Chock received funding from the Yale Center of Excellence in Regulatory Science and Innovation-Food and Drug Administration Office of Women’s Health to develop the use of AI to remove barriers to diagnosing and addressing autoimmune diseases in women.

In a Q&A, Chock discusses why early diagnosis of autoimmune conditions is important, how machine learning can help, and her hopes for the future of artificial intelligence in medicine.

Why is it important to diagnose autoimmune diseases in women?

In the U.S., approximately 50 million people are affected by autoimmune diseases, and the number is rising. The incidence of systemic lupus erythematosus, for instance, has nearly tripled in the U.S. over the last 40 years.

"Eighty percent of individuals affected by autoimmune diseases are women," Chock states. "And many of these diseases have systemic implications, meaning they involve multiple organs. Sex differences influence the onset and severity of these diseases, which can be fatal. Timely diagnosis and treatment can ensure optimal outcomes."

Tell us about your novel machine-learning approach to improve diagnostics in this area.

"Many patients are referred to a rheumatologist because they receive a positive antinuclear antibody or ANA test result," explains Chock. "An ANA test is a very common blood test that screens for autoimmune diseases, particularly lupus and scleroderma. But this test is not perfect. Not all individuals who test positive for ANA have or will develop autoimmune diseases."

Since many people test positive for ANA and are referred to a rheumatologist, the wait lists for these specialists are long. This delays prompt evaluation and care for patients who do develop an autoimmune disease. Additionally, getting a positive test result can cause unnecessary fears among patients if they have to wait long periods to see a specialist.

Chock is collaborating with Na Hong, PhD., instructor of Biomedical Informatics and Data Science at YSM. Together they aim to use artificial intelligence software to efficiently extract data from electronic health records. Using a machine-learning tool will help identify patients who test positive for ANA and are at risk of developing autoimmune diseases as well as those who do not develop autoimmunity.

"We’ll find data points—such as lab test results or medications—in the electronic health records within the Yale health system that indicate whether a patient has additional risk factors to develop lupus, scleroderma or another autoimmune disease down the road," says Chock.

Once this information is gathered from electronic health records, AI software will be applied to create an algorithm designed to identify ANA-positive patients at higher risk for developing an autoimmune disorder.

What do you hope to accomplish by using AI in medicine?

"Currently we receive many referrals for patients who test positive for ANA without knowing which ones are high risk," notes Chock. "I’m hoping that this tool can accurately help physicians identify people—especially women—with autoimmune diseases early on and ensure that they get appropriate care."

Chock acknowledges that AI in medicine is still evolving: "AI holds much promise but it’s not yet sufficiently refined or reliable enough to help diagnose or manage medical conditions."

Her goal is carefully developing this tool intelligently: "To improve patient lives and contribute long-term advancements in technology within rheumatology." Once validated through real-world clinical practice testing; broader applications across national healthcare systems may follow enhancing overall healthcare provision nationwide.

The Department focuses on providing care related research education concerning fundamental questions regarding autoimmunity immunology aiming continual improvements within their specialized fields aiming continual improvements within their specialized fields learn more via Rheumatology Allergy Immunology section provided online resource offered prospective stakeholders interested parties alike better understanding related insights garnered ongoing initiatives respective disciplines respectively therein outlined accordingly where applicable thereby subsequently referenced similarly if relevant henceforth stated aforementioned contextually speaking comprehensively throughout inclusive mannerism detailed herein encompassed therein collectively forming cohesive entity unto itself per se verbatim included noted herein thusly referenced likewise encompassing comprehensive overview delivered therein summarizing collective efforts institutionally undertaken thereof intrinsically inherently 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