AI detects cancer but it’s also reading who you are

AI tools designed to diagnose cancer from tissue samples are quietly learning more than just disease patterns. New research shows these systems can infer patient demographics from pathology slides, leading to biased results for certain groups. The bias stems from how the models are trained and the data they see, not just from missing samples. Researchers also demonstrated a way to significantly reduce these disparities.

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For decades, pathology has been essential to how doctors diagnose and treat cancer. A pathologist studies an extremely thin slice of human tissue under a microscope, searching for visual signs that reveal whether cancer is present and, if so, what type and stage it has reached.

To a trained specialist, examining a pink, swirling tissue sample dotted with purple cells is like grading a test without a name on it -- the slide contains vital information about the disease, but it offers no clues about who the patient is.

That assumption does not fully apply to artificial intelligence systems now entering pathology labs. A new study led by researchers at Harvard Medical School shows that pathology AI models can infer demographic details directly from tissue slides. This unexpected ability can introduce bias into cancer diagnosis across different patient groups.

After evaluating several widely used AI models designed to identify cancer, the researchers found that these systems did not perform equally for all patients. Diagnostic accuracy varied based on patients' self-reported race, gender, and age. The team also uncovered several reasons why these disparities occur.

To address the issue, the researchers developed a framework called FAIR-Path, which significantly reduced bias in the tested models.