In southern California, where rates of homelessness are among the highest in the nation, a private company, Akido Labs, is running clinics for unhoused patients and others with low incomes. The caveat? The patients are seen by medical assistants who use artificial intelligence (AI) to listen to the conversations, then spit out potential diagnoses and treatment plans, which are then reviewed by a doctor. The company’s goal, its chief technology officer told the MIT Technology Review, is to “pull the doctor out of the visit”.
This is dangerous. Yet it’s part of a larger trend where generative AI is being pushed into healthcare for medical professionals. In 2025, a survey by the American Medical Association reported that two out of three physicians used AI to assist with their daily work, including diagnosing patients. One AI startup raised $200m to provide medical professionals with an app dubbed “ChatGPT for doctors”. US lawmakers are considering a bill that would recognize AI as able to prescribe medication. While this trend of AI in healthcare affects almost all patients, it has a deeper impact on people with low incomes who already face substantial barriers to care and higher rates of mistreatment in healthcare settings. People who are unhoused and have low incomes should not be testing grounds for AI in healthcare. Instead, their voices and priorities should drive if, how, and when AI is implemented in their care.
The rise of AI in healthcare didn’t happen in a vacuum. Overcrowded hospitals, overworked clinicians and relentless pressure for medical offices to run seamlessly, shuttling patients in and out of a large for-profit healthcare system, set the conditions. The demands on healthcare workers are often compounded in economically disadvantaged communities where healthcare settings are often under-resourced and patients are uninsured, with a greater burden of chronic health conditions due to racism and poverty.
Here is where someone might ask, “Isn’t something better than nothing?” Well, actually, no. Studies show that AI-enabled tools generate inaccurate diagnoses. A 2021 study in Nature Medicine examined AI algorithms trained on large, chest X-ray datasets for medical imaging research and found that these algorithms systematically under-diagnosed Black and Latinx patients, patients recorded as female and patients with Medicaid insurance. This systematic bias risks deepening health inequities for patients already facing barriers to care. Another study, published in 2024, found that AI misdiagnosed breast cancer screenings among Black patients – the odds of false positives for Black patients screened for breast cancer was greater than for their white counterparts. Due to algorithmic bias, some clinical AI tools have notoriously performed worse on Black patients and other people of color. That’s because AI isn’t independently “thinking”; it relies on probabilities and pattern recognition, which can reinforce…
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