Artificial Intelligence in Dermatology: Challenges and Perspectives

Abstract

Artificial intelligence (AI) based on machine learning and convolutional neuron networks (CNN) is rapidly becoming a realistic prospect in dermatology. Non-melanoma skin cancer is the most common cancer worldwide and melanoma is one of the deadliest forms of cancer. Dermoscopy has improved physicians’ diagnostic accuracy for skin cancer recognition but unfortunately it remains comparatively low. AI could provide invaluable aid in the early evaluation and diagnosis of skin cancer. In the last decade, there has been a breakthrough in new research and publications in the field of AI. Studies have shown that CNN algorithms can classify skin lesions from dermoscopic images with superior or at least equivalent performance compared to clinicians. Even though AI algorithms have shown very promising results for the diagnosis of skin cancer in reader studies, their generalizability and applicability in everyday clinical practice remain elusive. Herein we attempted to summarize the potential pitfalls and challenges of AI that were underlined in reader studies and pinpoint strategies to overcome limitations in future studies. Finally, we tried to analyze the advantages and opportunities that lay ahead for a better future for dermatology and patients, with the potential use of AI in our practices.

Artificial Intelligence in microbiomes analysis: A review of applications in dermatology

“…Dermatologists can also apply this method for disease diagnosis. For example, an image is annotated according to the corresponding medical records and pathological results and, after generating standardized data, these data can then be analyzed using CNN to distinguish and thus diagnose skin lesion images from that of normal skin images ( Haenssle et al, 2018 ; Liopyris et al, 2022 ). This method is often used in the diagnosis of fungal infections, such as onychomycosis, as this condition represents the most common nail disease infected with fungi.…”

Phototherapy in the artificial intelligence era

“…Dear Editor, Artificial intelligence (AI) based on machine learning and convolutional neuron networks is rapidly becoming a realistic prospect in Dermatology. 1,2 Due to the unique nature of Dermatology, AI-aided dermatological diagnosis based on image recognition has become a current focus and future trend. 3 In recent studies, AI algorithms have shown promising results for diagnosing non-melanoma skin cancer and melanoma, with a diagnostic accuracy comparable with that of skin experts.…”

“…3 In recent studies, AI algorithms have shown promising results for diagnosing non-melanoma skin cancer and melanoma, with a diagnostic accuracy comparable with that of skin experts. [1][2][3] The use of 3D imaging systems allows clinicians to screen and label skin-pigmented lesions and distributed disorders, which can provide an objective assessment and image documentation of lesion sites. 3 Dermatoscopes combined with intelligent software help the dermatologist to easily correlate each close-up image with the corresponding marked lesions in the 3D body map.…”

“…However, applying AI algorithms in clinical practice needs to be revised. 1,2,4,5 Limitations concerning the lack of generalizability and standardization must continue to be addressed. 1,2,5 Finally, the clinician's role remains vital, as no machine can replace a human-to-human relationship.…”

“…1,2,4,5 Limitations concerning the lack of generalizability and standardization must continue to be addressed. 1,2,5 Finally, the clinician's role remains vital, as no machine can replace a human-to-human relationship. 1 Still, we have a tool that can enhance medical care if we use it rationally under the correct guidance and supervision.…”