AI has been noted to form, copy, and amplify harmful stereotypes of already marginalized groups.
This phenomenon has far reaching effects as AI becomes a normal part of everyday life. Most worrying is that the prolific bias already circling healthcare settings may now be exacerbated by AI-driven decisions.
These can have a direct impact on patient care and outcomes, underscoring the need for careful evaluation of potential biases.
Therefore, a research group led by Dr. Shannon L. Walston at Osaka Metropolitan University's Graduate School of Medicine conducted a scoping review to identify studies validating commercially available radiology AI products and to note trends when reporting on sex, age, and ethnic demographic subgroups.
The team collected 545 studies on 252 products with reported demographic subgroup data.
Of the 545, only 77 studies validating 52 products were found to include demographic details and subgroup analysis results.
This finding exposes the need for effective, transparent reporting to confirm the safe and unbiased performance of these medical AI products across all patient subgroups.
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