Underserved populations experience higher levels of pain. These disparities persist even after controlling for the objective severity of diseases like osteoarthritis, as graded by human physicians using medical images, raising the possibility that underserved patients’ pain stems from factors external to the knee, such as stress. Here we use a deep learning approach to measure the severity of osteoarthritis, by using knee X-rays to predict patients’ experienced pain. We show that this approach dramatically reduces unexplained racial disparities in pain. Relative to standard measures of severity graded by radiologists, which accounted for only 9% (95% confidence interval (CI), 3–16%) of racial disparities in pain, algorithmic predictions accounted for 43% of disparities, or 4.7× more (95% CI, 3.2–11.8×), with similar results for lower-income and less-educated patients. This suggests that much of underserved patients’ pain stems from factors within the knee not reflected in standard radiographic measures of severity. We show that the algorithm’s ability to reduce unexplained disparities is rooted in the racial and socioeconomic diversity of the training set. Because algorithmic severity measures better capture underserved patients’ pain, and severity measures influence treatment decisions, algorithmic predictions could potentially redress disparities in access to treatments like arthroplasty.

服务不足的人群经历的疼痛程度更高。即使在控制了骨关节炎等疾病的客观严重程度之后，这些差异仍然存在，人类医生使用医学图像对其进行分级，这增加了服务不足患者的疼痛源于膝关节外部因素（如压力）的可能性。在这里，我们使用深度学习方法来测量骨关节炎的严重程度，通过使用膝关节 X 射线来预测患者经历的疼痛。我们表明，这种方法极大地减少了无法解释的种族疼痛差异。相对于放射科医师分级的严重程度标准测量，仅占疼痛种族差异的 9%（95% 置信区间 (CI)，3-16%），算法预测占差异的 43%，或 4.7 倍以上（ 95% CI, 3.2–11.8×), 对于低收入和受教育程度较低的患者也有类似的结果。这表明，大部分未得到充分服务的患者的疼痛源于膝关节内的因素，这些因素并未反映在标准的严重程度放射照相测量中。我们表明，该算法减少无法解释的差异的能力植根于训练集的种族和社会经济多样性。由于算法严重程度测量更好地捕捉服务不足的患者的疼痛，并且严重程度测量影响治疗决策，算法预测可能会纠正获得关节成形术等治疗的差异。我们表明，该算法减少无法解释的差异的能力植根于训练集的种族和社会经济多样性。由于算法严重程度测量更好地捕捉服务不足的患者的疼痛，并且严重程度测量影响治疗决策，算法预测可能会纠正获得关节成形术等治疗的差异。我们表明，该算法减少无法解释的差异的能力植根于训练集的种族和社会经济多样性。由于算法严重程度测量更好地捕捉服务不足的患者的疼痛，并且严重程度测量影响治疗决策，算法预测可能会纠正获得关节成形术等治疗的差异。