Incidence of Dermatomyositis in a Nationwide Cohort Study of US Veterans

Bolender, Caitlin M.; Jimenez, Amber; Clarke, Jennie T.; Willson, Tina M.; Stevens, Vanessa; Rhoads, Jamie L W

doi:10.1001/jamadermatol.2022.3112

JAMA Dermatol

2022

DOI: 10.1001/jamadermatol.2022.3112

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Incidence of Dermatomyositis in a Nationwide Cohort Study of US Veterans

Caitlin M. Bolender

Amber Jimenez

Jennie T. Clarke

et al.

Abstract: This cohort study evaluates the incidence of dermatomyositis and its trend over time in the US Department of Veterans Affairs health care system.

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Cited by 2 publications

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Epidemiology of the idiopathic inflammatory myopathies

Khoo,

Lilleker,

Thong

et al. 2023

Nat Rev Rheumatol

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Epidemiology of the idiopathic inflammatory myopathies

Khoo,

Lilleker,

Thong

et al. 2023

Nat Rev Rheumatol

View full text Add to dashboard Cite

Deep learning-aided decision support for diagnosis of skin disease across skin tones

Groh,

Badri,

Daneshjou

et al. 2024

Nat Med

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Although advances in deep learning systems for image-based medical diagnosis demonstrate their potential to augment clinical decision-making, the effectiveness of physician–machine partnerships remains an open question, in part because physicians and algorithms are both susceptible to systematic errors, especially for diagnosis of underrepresented populations. Here we present results from a large-scale digital experiment involving board-certified dermatologists (n = 389) and primary-care physicians (n = 459) from 39 countries to evaluate the accuracy of diagnoses submitted by physicians in a store-and-forward teledermatology simulation. In this experiment, physicians were presented with 364 images spanning 46 skin diseases and asked to submit up to four differential diagnoses. Specialists and generalists achieved diagnostic accuracies of 38% and 19%, respectively, but both specialists and generalists were four percentage points less accurate for the diagnosis of images of dark skin as compared to light skin. Fair deep learning system decision support improved the diagnostic accuracy of both specialists and generalists by more than 33%, but exacerbated the gap in the diagnostic accuracy of generalists across skin tones. These results demonstrate that well-designed physician–machine partnerships can enhance the diagnostic accuracy of physicians, illustrating that success in improving overall diagnostic accuracy does not necessarily address bias.

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Incidence of Dermatomyositis in a Nationwide Cohort Study of US Veterans

Abstract: This cohort study evaluates the incidence of dermatomyositis and its trend over time in the US Department of Veterans Affairs health care system.

Cited by 2 publications

References 5 publications

Epidemiology of the idiopathic inflammatory myopathies

Epidemiology of the idiopathic inflammatory myopathies

Deep learning-aided decision support for diagnosis of skin disease across skin tones

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