Artificial Intelligence in Dermatology—Where We Are and the Way to the Future: A Review

Hogarty, Daniel T; Su, John; Phan, Kevin; Attia, Mohamed; Hossny, Mohammed; Nahavandi, Saeid; Lenane, Patsy; Moloney, Fergal J.; Yazdabadi, Anousha

doi:10.1007/s40257-019-00462-6

Cited by 113 publications

(83 citation statements)

References 36 publications

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“…Advances in computing power in the last two decades because of improved hardware and software technologies, made us recognize the potential for AI to improve current medical practices, and AI research is already being conducted in numerous medical fields, including dermatology. [ 8 ]…”

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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“…High profile examples include the classification of chest radiographs based on abnormality for triage [1] or pathological processes [2,3]. Multiple studies have demonstrated the potential utility of such AI algorithms in other medical specialties including ophthalmology [4], dermatology [5], and pathology [6]. Although there is currently a paucity of evidence to support routine use of AI algorithms in realworld clinical practice, it is expected that with increasing academic and industry interest, validated use cases for AI tools in radiology will likely emerge rapidly [7].…”

Section: Introductionmentioning

confidence: 99%

Attitudes and perceptions of UK medical students towards artificial intelligence and radiology: a multicentre survey

et al. 2020

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Objectives: To explore the attitudes of United Kingdom (UK) medical students regarding artificial intelligence (AI), their understanding, and career intention towards radiology. We also examine the state of education relating to AI amongst this cohort. Methods: UK medical students were invited to complete an anonymous electronic survey consisting of Likert and dichotomous questions. Results: Four hundred eighty-four responses were received from 19 UK medical schools. Eighty-eight percent of students believed that AI will play an important role in healthcare, and 49% reported they were less likely to consider a career in radiology due to AI. Eighty-nine percent of students believed that teaching in AI would be beneficial for their careers, and 78% agreed that students should receive training in AI as part of their medical degree. Only 45 students received any teaching on AI; none of the students received such teaching as part of their compulsory curriculum. Statistically, students that did receive teaching in AI were more likely to consider radiology (p = 0.01) and rated more positively to the questions relating to the perceived competence in the post-graduation use of AI (p = 0.01-0.04); despite this, a large proportion of students in the taught group reported a lack of confidence and understanding required for the critical use of healthcare AI tools. Conclusions: UK medical students understand the importance of AI and are keen to engage. Medical school training on AI should be expanded and improved. Realistic use cases and limitations of AI must be presented to students so they will not feel discouraged from pursuing radiology.

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“…In the field of dermatology, Artificial intelligence-based tools are being developed to evaluate the severity of psoriasis ( 6 ) or to distinguish between onychomycosis and healthy nails ( 7 , 8 ). In experimental settings, the sensitivity and specificity of AI-based algorithms in discriminating melanomas from nevi were similar to or better than those of dermatologists ( 9 – 11 ).…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence in Skin Cancer Diagnostics: The Patients' Perspective

et al. 2020

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Background: Artificial intelligence (AI) has shown promise in numerous experimental studies, particularly in skin cancer diagnostics. Translation of these findings into the clinic is the logical next step. This translation can only be successful if patients' concerns and questions are addressed suitably. We therefore conducted a survey to evaluate the patients' view of artificial intelligence in melanoma diagnostics in Germany, with a particular focus on patients with a history of melanoma. Participants and Methods: A web-based questionnaire was designed using LimeSurvey, sent by e-mail to university hospitals and melanoma support groups and advertised on social media. The anonymous questionnaire evaluated patients' expectations and concerns toward artificial intelligence in general as well as their attitudes toward different application scenarios. Descriptive analysis was performed with expression of categorical variables as percentages and 95% confidence intervals. Statistical tests were performed to investigate associations between sociodemographic data and selected items of the questionnaire. Results: 298 individuals (154 with a melanoma diagnosis, 143 without) responded to the questionnaire. About 94% [95% CI = 0.91-0.97] of respondents supported the use of artificial intelligence in medical approaches. 88% [95% CI = 0.85-0.92] would even make their own health data anonymously available for the further development of AI-based applications in medicine. Only 41% [95% CI = 0.35-0.46] of respondents were amenable to the use of artificial intelligence as stand-alone system, 94% [95% CI = 0.92-0.97] to its use as assistance system for physicians. In subgroup analyses, only minor differences were detectable. Respondents with a previous history of melanoma were more amenable to the use of AI applications for early detection even at home. They would prefer an application scenario where physician and AI classify the lesions independently. With respect to AI-based applications in medicine, patients were concerned about insufficient Jutzi et al. Patients' View on AI-Based Diagnosis data protection, impersonality and susceptibility to errors, but expected faster, more precise and unbiased diagnostics, less diagnostic errors and support for physicians. Conclusions: The vast majority of participants exhibited a positive attitude toward the use of artificial intelligence in melanoma diagnostics, especially as an assistance system.

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Artificial Intelligence in Dermatology—Where We Are and the Way to the Future: A Review

Cited by 113 publications

References 36 publications

Use of artificial intelligence in dermatology

Use of artificial intelligence in dermatology

Attitudes and perceptions of UK medical students towards artificial intelligence and radiology: a multicentre survey

Artificial Intelligence in Skin Cancer Diagnostics: The Patients' Perspective

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