AI in Healthcare Equity

Maria schedules her annual mammogram at the same clinic she's visited for eight years. When she arrives for her examination, the technician mentions that they now use an AI system to help radiologists detect abnormalities earlier. "It's really advanced," he explains. "Catches things we might miss."

Three days later, Maria receives a callback requesting additional imaging. The AI flagged something suspicious. During her follow-up appointment, the radiologist shows her the scan and explains that the AI identified an area of concern that led to this precautionary step.

What Maria doesn't know is that the computer learned to spot problems by looking at thousands of mammograms from other women. But most of those women had lighter skin and different breast tissue patterns. For women like Maria, whose characteristics differ from the majority of data used to train the algorithm, the computer gets confused more often and sees problems that aren't really there. This means more scary phone calls and extra appointments that she doesn't actually need.

Meanwhile, across town, Robert sits in his doctor’s office as she looks at computer-generated suggestions for his diabetes treatment. The program recommends specific medications based on Robert's test results and medical history, and the doctor has learned to trust these suggestions because they seem thorough and scientific.

However, the algorithm is based on patients who took their medications regularly, never missed appointments, and had stable living situations. Robert sometimes skips his medicine when money is tight, and has missed appointments because he can't take time off work. Because the doctor relies too heavily on an algorithm that doesn’t account for these factors, she is unaware that the suggestions may not be suitable for Robert’s situation. 

In both cases, computer systems designed to help people get better healthcare accidentally make things worse. Maria gets scared and has to come back for tests she doesn't need. Robert gets treatment advice that isn’t tailored to his real-life situation.

These stories illustrate how medical programs designed to improve patient care can accidentally make healthcare unequal. The technology itself isn't good or bad. What matters is the information it learned from, where it is applied, and how doctors and patients respond to it—all of which determine whether it benefits everyone equally or makes existing problems worse.

People’s trust in medical technology varies, often because of past experiences with healthcare.1 Some communities have good reason to be cautious, while others welcome new tools. Doctors also react differently to computer suggestions, and those reactions can shape whether the technology helps or harms. The challenge is bigger than fixing computer code. To make patient care more equitable, we need to look closely at each potential impact of AI.