Artificial intelligence and dermatology: opportunities, challenges, and future directions

Schlessinger, Daniel I.; Chhor, Guillaume; Gevaert, Olivier; Swetter, Susan M.; Ko, Justin; Novoa, Roberto A.

doi:10.12788/j.sder.2019.007

Cited by 34 publications

(26 citation statements)

References 63 publications

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“…AI was first coined at a famous Dartmouth College conference in 1956. [ 6 ] In the early 1970s, medical researchers discovered the applicability of AI in life sciences. [ 7 ] However, the limitation in technology also restricted the application of AI.…”

Section: History Of Artificial Intelligencementioning

confidence: 99%

Use of artificial intelligence in dermatology

Sarda²,

Gupta

et al. 2020

Indian J Dermatol

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Artificial intelligence (AI) has emerged as a major frontier in computer science research. Although AI has been available for some time and found its application in many fields of medicine, its use in dermatology is comparatively new and limited. A sound understanding of the concepts of AI is essential for dermatologists as skin conditions with their abundant clinical and dermatoscopic data and images can potentially be the next big thing in the application of AI in medicine. There are already a number of artificial intelligence studies focusing on skin disorders, such as skin cancer, psoriasis, atopic dermatitis and onychomycosis. This article presents an overview of AI and new developments relevant to dermatology, examining both its current applications and future potential.

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Section: History Of Artificial Intelligencementioning

confidence: 99%

Use of artificial intelligence in dermatology

Sarda²,

Gupta

et al. 2020

Indian J Dermatol

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“…Recently, artificial intelligence has made dramatic progress in its applications for image analysis in dermatology. [38][39][40][41][42] The improving performance of artificial intelligence models, therefore, can potentially be applied to improve TD processes. Though existing TD platforms can connect patients and referring physicians to dermatologists across geographic distances, these processes are still relatively time-and labor-intensive for dermatologists.…”

Section: Future Directionsmentioning

confidence: 99%

The policy dimensions, regulatory landscape, and market characteristics of teledermatology in the United States

et al. 2020

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The COVID-19 pandemic has spurred healthcare systems across the world to rapidly redesign their models of care delivery. As such, this pandemic has accelerated the adoption of teledermatology in the United States. However, it remains unknown whether this momentum will be maintained after the pandemic. The future of teledermatology in the United States will be significantly influenced by a complex set of policy, legal, and regulatory frameworks. An understanding of these frameworks will help dermatologists more effectively adopt and implement teledermatology platforms. In this article, we review the current state of teledermatology in the United States, including policy dimensions, the regulatory landscape, market characteristics, and future directions.

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“…In addition, the training dataset should be balanced and minorities with regard to race, ethnicity, or socioeconomic status should be well represented. An example of racial bias is evident in a recent study by Esteva et al [30], which claimed that a particular AI algorithm was as accurate as a dermatologist in diagnosing skin malignancy; however, the dataset the researchers used consisted mostly of Caucasian skin samples, and as a result, the algorithm was inaccurate when used in non-Caucasian populations [31]. If the training data does not comprise a balanced population that includes patients from particular backgrounds, the prediction model will not be reliable for that group and can lead to misdiagnosis.…”

Section: Understanding the Inherent Biases Of Ai And Guarding Againstmentioning

confidence: 99%

The Role of Artificial Intelligence in Understanding and Addressing Disparities in Breast Cancer Outcomes

Mema

McGinty

2020

Curr Breast Cancer Rep

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Purpose of Review The goal of our paper is to explore the role of AI in understanding health disparities in cancer care and its potential role in resolving them. Recent Findings Multiple studies have shown that with the recent advances in AI, its integration in cancer care has the potential to impact earlier diagnosis and improve clinical decision making. While AI risks to further widen health disparities, some studies suggest that it represents an excellent opportunity for resolving them. Summary With active engagement, incorporating AI in breast cancer care represents an excellent opportunity for elucidating and resolving health disparities; however, without deliberate effort, it risks to further widen them.

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Artificial intelligence and dermatology: opportunities, challenges, and future directions

Cited by 34 publications

References 63 publications

Use of artificial intelligence in dermatology

Use of artificial intelligence in dermatology

The policy dimensions, regulatory landscape, and market characteristics of teledermatology in the United States

The Role of Artificial Intelligence in Understanding and Addressing Disparities in Breast Cancer Outcomes

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