[Translated article] Artificial intelligence in dermatology: A threat or an opportunity?

Martorell, A.; Martín-Gorgojo, Alejandro; Ríos‐Viñuela, E.; Rueda-Carnero, J.M.; Alfageme, Fernando; Taberner, R.

doi:10.1016/j.ad.2021.07.014

Cited by 9 publications

(8 citation statements)

References 80 publications

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“…AI is a broad scientific discipline dedicated to developing programs that display properties of human intellect and encompasses ML and deep learning [ 3 , 6 , 17 , 18 ]. ML includes various approaches that enable algorithms to learn from data without explicit programming [ 1 , 10 , 17 ].…”

Section: Discussionmentioning

confidence: 99%

“…Artificial intelligence (AI) is "the scientific understanding of the mechanisms underlying thought and intelligent behaviour, and their exemplification in machines" [1]. It aims to reproduce properties similar to human cognition [2][3][4]. A subset of AI where computer programs learn from experience without any definitive coding instructions is known as machine learning (ML) [1].…”

Section: Introductionmentioning

confidence: 99%

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Analyzing the Predictability of an Artificial Intelligence App (Tibot) in the Diagnosis of Dermatological Conditions: A Cross-sectional Study

Marri¹,

Inamadar²,

Janagond³

et al. 2023

JMIR Dermatol

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Background Artificial intelligence (AI) aims to create programs that reproduce human cognition and processes involved in interpreting complex data. Dermatology relies on morphological features and is ideal for applying AI image recognition for assisted diagnosis. Tibot is an AI app that analyzes skin conditions and works on the principle of a convolutional neural network. Appropriate research analyzing the accuracy of such apps is necessary. Objective This study aims to analyze the predictability of the Tibot AI app in the identification of dermatological diseases as compared to a dermatologist. Methods This is a cross-sectional study. After taking informed consent, photographs of lesions of patients with different skin conditions were uploaded to the app. In every condition, the AI predicted three diagnoses based on probability, and these were compared with that by a dermatologist. The ability of the AI app to predict the actual diagnosis in the top one and top three anticipated diagnoses (prediction accuracy) was used to evaluate the app’s effectiveness. Sensitivity, specificity, and positive predictive value were also used to assess the app’s performance. Chi-square test was used to contrast categorical variables. P<.05 was considered statistically significant. Results A total of 600 patients were included. Clinical conditions included alopecia, acne, eczema, immunological disorders, pigmentary disorders, psoriasis, infestation, tumors, and infections. In the anticipated top three diagnoses, the app’s mean prediction accuracy was 96.1% (95% CI 94.3%-97.5%), while for the exact diagnosis, it was 80.6% (95% CI 77.2%-83.7%). The prediction accuracy (top one) for alopecia, acne, pigmentary disorders, and fungal infections was 97.7%, 91.7%, 88.5%, and 82.9%, respectively. Prediction accuracy (top three) for alopecia, eczema, and tumors was 100%. The sensitivity and specificity of the app were 97% (95% CI 95%-98%) and 98% (95% CI 98%-99%), respectively. There is a statistically significant association between clinical and AI-predicted diagnoses in all conditions (P<.001). Conclusions The AI app has shown promising results in diagnosing various dermatological conditions, and there is great potential for practical applicability.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analyzing the Predictability of an Artificial Intelligence App (Tibot) in the Diagnosis of Dermatological Conditions: A Cross-sectional Study

Marri¹,

Inamadar²,

Janagond³

et al. 2023

JMIR Dermatol

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“…Para [31] las redes neuronales artificiales "están formadas por capas de variables interconectadas entre sí con unos pesos que el algoritmo va calculando y ajustando mediante iteraciones (repeticiones) de un proceso que se conoce como «descenso del gradiente»". Para [32] atribuye el éxito de las redes neuronales artificiales en usar varios algoritmos de optimización y varias estructuras de la red, como la normalización por lotes.…”

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Deep Learning to support the recognition of pests and diseases in Yungay potato crops in the province of Cutervo, Angurra hamlet, Perú

Huarote Zegarra,

Cabrera Herrera,

Llanos Chacaltana

2023

Proceedings of the 21th LACCEI International Multi-Conference for Engineering, Education and Technology (LACCEI 2023): “Leaders

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The present investigation efficiently covers the need to classify according to the diseases or pests of the Yungay potato leaf in the province of Cutervo, Angurra village (Peru),specifically classify them as fly plague (PM=1), weevil plague (PG=2), streak disease (ER=3) or as no plague (SP=0). To achieve this, the digital images have necessarily been prepared to decrease in size and at the same time obtain their representative, this in order to enter the SOM neural network (self-organizing map). The functions based on artificial vision for the preparation of the image (in jpg format of varied dimensions), to the shots of the potato leaves, are 1200 images for training and another 1200 for tests, only the image being extracted from each scene. sheet, scaled to a dimension of 256x256 pixels to homogenize, extract its characteristics from each disease and passed to a tone of gray to be learned in the neural network, managing to verify with this sequence an accuracy of 99.42%, a

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Section: Introductionmentioning

confidence: 99%

“…Artificial intelligence (AI) has emerged as a promising technology in the field of medicine, including dermatology 1,2 . AI is designed to replicate human cognitive functions and, despite being in its nascent stage, is expected to revolutionize the practice of dermatology 1 . Dermatology, with its large patient base, is well‐suited for big data analysis by AI, which can aid in diagnosis, treatment decision‐making and management.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence in psoriasis: Where we are and where we are going

Liu,

Wang,

et al. 2023

Experimental Dermatology

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Artificial intelligence (AI) is a field of computer science that involves the development of programs designed to replicate human cognitive processes and the analysis of complex data. In dermatology, which is predominantly a visual‐based diagnostic field, AI has become increasingly important in improving professional processes, particularly in the diagnosis of psoriasis. In this review, we summarized current AI applications in psoriasis: (i) diagnosis, including identification, classification, lesion segmentation, lesion severity and area scoring; (ii) treatment, including prediction treatment efficiency and prediction candidate drugs; (iii) management, including e‐health and preventive medicine. Key challenges and future aspects of AI in psoriasis were also discussed, in hope of providing potential directions for future studies.

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[Translated article] Artificial intelligence in dermatology: A threat or an opportunity?

Cited by 9 publications

References 80 publications

Analyzing the Predictability of an Artificial Intelligence App (Tibot) in the Diagnosis of Dermatological Conditions: A Cross-sectional Study

Analyzing the Predictability of an Artificial Intelligence App (Tibot) in the Diagnosis of Dermatological Conditions: A Cross-sectional Study

Deep Learning to support the recognition of pests and diseases in Yungay potato crops in the province of Cutervo, Angurra hamlet, Perú

Artificial intelligence in psoriasis: Where we are and where we are going

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