Artificial Intelligence in Dermatology Image Analysis: Current Developments and Future Trends

Abstract

Background: Thanks to the rapid development of computer-based systems and deep-learning-based algorithms, artificial intelligence (AI) has long been integrated into the healthcare field. AI is also particularly helpful in image recognition, surgical assistance and basic research. Due to the unique nature of dermatology, AI-aided dermatological diagnosis based on image recognition has become a modern focus and future trend. Key scientific concepts of review: 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. Dermatoscopes combined with intelligent software help the dermatologist to easily correlate each close-up image with the corresponding marked lesion in the 3D body map. In addition, AI in the field of prosthetics can assist in the rehabilitation of patients and help to restore limb function after amputation in patients with skin tumors. The aim of the study: For the benefit of patients, dermatologists have an obligation to explore the opportunities, risks and limitations of AI applications. This study focuses on the application of emerging AI in dermatology to aid clinical diagnosis and treatment, analyzes the current state of the field and summarizes its future trends and prospects so as to help dermatologists realize the impact of new technological innovations on traditional practices so that they can embrace and use AI-based medical approaches more quickly.

The value of artificial intelligence in the diagnosis of lung cancer: A systematic review and meta-analysis

“…Since applying AI-assisted diagnostic systems to clinical work, a new page has been turned in the study of a lung cancer diagnosis. Some studies have shown [ 41 – 43 ] that AI technologies are being used more and more extensively in the practice of clinical diagnosis and treatment. These technologies are mainly applied to diagnose and analyze various medical images such as skin lesions, pathological microscopic images, and radiological data, and AI technologies are remarkable in their ability to improve diagnostic accuracy, stability, and work efficiency.…”

Phototherapy in the artificial intelligence era

“…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. [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 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.…”

“…[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. 3 AI's emerging applications in Dermatology include diagnosing, managing, and monitoring other skin diseases, including inflammatory and autoimmune diseases.…”

Precision surgery

“…Precision surgery is also the ability to detect occult tumor deposits by means of near-infrared spectroscopy based on indocyanine green fluorescence [12] or radiomics [13], and to increase the ability to selectively identify nerves in procedures aiming at nerve sparing [14]. As shown in dermatology [15] and in endoscopy [16], image recognition by artificial intelligence can enhance the ability to detect and classify small lesions that otherwise could be missed. Interestingly enough, modifications in microbiota composition can influence the occurrence of "surgical complications", such as anastomotic leakage, and can affect cancer recurrence.…”