LAUSR

In 2003, the Institute of Medicine reported that Black US individuals receive fewer procedures and poorerquality care than White individuals, independent of socioeconomic determinants of health. Seventeen years later, access to care and perioperative level of care assignments potentiate disparities in surgical care, particularly affecting Black patients.1 These disparities are partially attributable to implicit bias that is entrenched deeply in US culture and media and are exacerbated when patient and physician demographics are mismatched. It is difficult to identify modifiable mechanisms of implicit bias because its latent mental constructs cannot be directly observed, but the weight of evidence suggests that many well-intentioned clinicians have 2 conflicting cognitive processes: one that is governed by a conscious, explicit system of beliefs and values, and one subconscious, implicit process that adapts to repeated stimuli. The former process is typically fair and equitable; the latter may drive implicit bias. Efforts to overcome implicit bias and health care disparities by building awareness and enacting structural changes to credentialing agencies and training curricula have yielded modest progress; additional strategies are needed. This Viewpoint endeavors to impart understanding of mechanisms by which artificial intelligence can either propagate or counteract disparities and suggests methods to tilt the balance toward fairness and equity in surgical care.