Artificial intelligence in dermatology: a comparative analysis of computer vision programs based on machine learning models

The use of artificial intelligence (AI) in dermatology is skyrocketing, but a comprehensive overview integrating regulatory, ethical, validation, and clinical issues is lacking. This work aims to review current research, map applicable legal regulations, identify ethical challenges and methods of verifying AI models in dermatology, assess publication trends, compare the most popular neural network architectures and datasets, and identify good practices in creating AI-based applications for dermatological use. A systematic literature review is conducted in accordance with the PRISMA guidelines, utilising Google Scholar, PubMed, Scopus, and Web of Science and employing bibliometric analysis. Since 2016, there has been exponential growth in deep learning research in dermatology, revealing gaps in EU and US regulations and significant differences in model performance across different datasets. The decision-making process in clinical dermatology is analysed, focusing on how AI is augmenting skin imaging techniques such as dermatoscopy and histology. Further demonstration is provided regarding how AI is a valuable tool that supports dermatologists by automatically analysing skin images, enabling faster diagnosis and the more accurate identification of skin lesions. These advances enhance the precision and efficiency of dermatological care, showcasing the potential of AI to revolutionise the speed of diagnosis in modern dermatology, sparking excitement and curiosity. Then, we discuss the regulatory framework for AI in medicine, as well as the ethical issues that may arise. Additionally, this article addresses the critical challenge of ensuring the safety and trustworthiness of AI in dermatology, presenting classic examples of safety issues that can arise during its implementation. The review provides recommendations for regulatory harmonisation, the standardisation of validation metrics, and further research on data explainability and representativeness, which can accelerate the safe implementation of AI in dermatological practice.

Ethics of artificial intelligence in dermatology

As the use of artificial intelligence (AI) as a diagnostic tool increases in the field of dermatology, there has been a growing need to diversify datasets to improve its diagnostic capability in darker skin tones. Currently, AI is not as effective as a diagnostic tool in darker skin tones (Fitzpatrick IV-VI) as it has been in lighter skin-toned (Fitzpatrick I-III) populations. This narrative review will provide a summary of the recent data and advancements made within the area. Medline and PubMed databases were searched with the following search terms: dermato* AND (skin tone or race or skin colour or ethnicity or race or Fitzpatrick) AND (ai or artificial intelligence). Texts were filtered for full text and English language from 2020 to 2025. Results including patients under 18 years of age were excluded, which resulted in 52 papers. After scanning through titles and abstracts, a total of eight papers remained that were relevant to the review. AI models have demonstrated lower accuracy in recognising cutaneous pathology in darker skin tones in the majority of studies. When looking at the results after training the models with diverse datasets, there was an overall improvement in the accuracy of AI to recognise pathology in Fitzpatrick skin tone IV-VI. Several studies also showed that there is some benefit to training AI with artificially pigmented images to improve its accuracy. AI has significant potential to enhance dermatology by improving diagnostic accuracy, reducing variability, and improving efficiency. Expanding datasets further appears to be of benefit in improving accuracy in darker skin tones. Further studies with larger sample sizes are needed to analyse other reasons the algorithms have lower accuracy in darker skin tones and how this could be mitigated.

Applications of Artificial Intelligence in Dermatology: Ethical Considerations.

Artificial intelligence in dermatology: The shifting paradigms.

Medicine is entering a transformative era with disruptive technologies such as virtual reality, genomic prediction, data analytics, personalized medicine, stem cell therapy, 3-D printing, and nanorobotics. Dermatology is significantly impacted by these advancements, particularly through artificial intelligence (AI). AI, defined as devices performing functions typically requiring human intelligence, plays an increasingly prominent role in healthcare. John McCarthy coined the term AI in 1956. In dermatology, AI aids in diagnosis, treatment planning, and understanding diseases across communities. Machine learning and deep learning, subsets of AI, require extensive datasets and robust analysis to improve accuracy and performance. AI's integration into dermatology is revolutionizing the field by enabling precision, reducing errors, and minimizing staffing needs. AI tools support dermatologists in diagnosing and treating various conditions, from psoriasis and acne to dermatitis and ulcers. Convolutional neural networks (CNNs) enhance the classification of skin lesions, while predictive models optimize treatment strategies based on patient data. AI's role extends to oncology, where it improves skin cancer detection through image analysis and histopathological assessment. Despite its potential, AI in dermatology faces challenges such as data quality, representativeness, algorithm transparency, and ethical considerations. Addressing biases, standardizing imaging protocols, and enhancing human-machine collaboration are crucial for maximizing AI's benefits.AI holds immense promise in dermatology, offering innovative solutions to enhance patient care and diagnostic accuracy. The future of AI in dermatology includes advancements in vision-language models, federated learning, and precision medicine approaches. Overcoming challenges related to data privacy, regulatory standards, and model evaluation is essential for successful integration into clinical practice. Collaborative efforts among stakeholders are vital to drive progress and realize the full potential of AI, ultimately improving patient outcomes globally.

The potential areas of application of artificial intelligence in dermatology are ever-increasing. With the wide availability of smartphones equipped with high-resolution cameras and impressive processing powers, harnessing these capabilities using machine learning (ML) could open new prospects in the management of dermatological disorders. Du-Harpur et al. have done a commendable job reviewing the utility of artificial intelligence in dermatology in an easily understandable manner by most dermatologists1 .

The potential areas of application of artificial intelligence in dermatology are ever-increasing. With the wide availability of smartphones equipped with high-resolution cameras and impressive processing powers, harnessing these capabilities using machine learning (ML) could open new prospects in the management of dermatological disorders. Du-Harpur et al. have done a commendable job reviewing the utility of artificial intelligence in dermatology in an easily understandable manner by most dermatologists1 .

Artificial intelligence (AI) promises to change health care, with some studies showing proof of concept of a provider-level performance in various medical specialties. However, there are many barriers to implementing AI, including patient acceptance and understanding of AI. Patients' attitudes toward AI are not well understood. We systematically reviewed the literature on patient and general public attitudes toward clinical AI (either hypothetical or realised), including quantitative, qualitative, and mixed methods original research articles. We searched biomedical and computational databases from Jan 1, 2000, to Sept 28, 2020, and screened 2590 articles, 23 of which met our inclusion criteria. Studies were heterogeneous regarding the study population, study design, and the field and type of AI under study. Six (26%) studies assessed currently available or soon-to-be available AI tools, whereas 17 (74%) assessed hypothetical or broadly defined AI. The quality of the methods of these studies was mixed, with a frequent issue of selection bias. Overall, patients and the general public conveyed positive attitudes toward AI but had many reservations and preferred human supervision. We summarise our findings in six themes: AI concept, AI acceptability, AI relationship with humans, AI development and implementation, AI strengths and benefits, and AI weaknesses and risks. We suggest guidance for future studies, with the goal of supporting the safe, equitable, and patient-centred implementation of clinical AI.

Although artificial intelligence has been available for some time, it has garnered significant interest recently and has been popularized by major companies with its applications in image identification, speech recognition and problem solving. Artificial intelligence is now being increasingly studied for its potential uses in medicine. A sound understanding of the concepts of this emerging field is essential for the dermatologist as dermatology has abundant medical data and images that can be used to train artificial intelligence for patient care. There are already a number of artificial intelligence studies focusing on skin disorders such as skin cancer, psoriasis, atopic dermatitis and onychomycosis. This article aims to present a basic introduction to the concepts of artificial intelligence as well aspresent an overview of the current research into artificial intelligence in dermatology, examining both its current applications and its future potential.

Background: Artificial intelligence (AI) has emerged as a transformative tool in modern medicine, particularly in dermatology, where it supports the diagnosis and management of various skin diseases, including skin cancer. Through machine learning and deep learning techniques, AI enables accurate analysis of clinical and dermoscopic images, improving early detection and clinical outcomes. Objective: This systematic review aimed to evaluate the clinical applications of AI in dermatology, focusing on its impact on diagnostic accuracy, workflow efficiency, and access to specialized care. Methods: The review was conducted according to PRISMA guidelines. Peer-reviewed studies published between January 2020 and March 2025 in English or Spanish were included if they evaluated AI-based tools for dermatological diagnosis, classification, or treatment. Animal studies, editorials, non-peer-reviewed articles, and studies with an unclear methodology were excluded. A comprehensive search was performed in PubMed, Scopus, IEEE Xplore, and Google Scholar between December 2024 and March 2025. The risk of bias was assessed qualitatively, using a tailored framework based on study design, dataset transparency, and clinical applicability. Results: A total of 29 studies met the inclusion criteria. AI tools demonstrated high performance in melanoma detection, achieving up to 90% accuracy and 85% sensitivity. In clinical settings, AI support reduced mismanagement of malignant lesions from 58.8% to 4.1% and avoided 27% of unnecessary procedures in benign cases. Additional tools such as convolutional neural networks and imaging systems like FotoFinder also showed promising results. Limitations: Limitations of the evidence include the heterogeneity of AI models, lack of external validation, and a moderate-to-high risk of bias. Conclusions: AI has demonstrated robust clinical potential in dermatology, particularly in cancer detection and workflow optimization. However, further studies are required to address challenges such as algorithmic bias, data privacy, and regulatory oversight. Funding and registration: This review received no external funding and was not registered in a systematic review registry.

Dermatology is an independent clinical discipline established based on cognitive features. With the emergence of digital technology, remote transmission and internet technologies, dermatology has become a very well-suited discipline to integrate these technologies and apply them in clinical practice due to its characteristics. At present, the development of skin imaging technologies (dermoscopy, reflectance confocal microscopy (RCM), optical coherence tomography OCT), etc.), teledermatology, and artificial intelligence (AI) has profoundly and comprehensively changed the nature, service model, and public recognition of dermatology (1). Skin imaging, as an important technical system in modern dermatology, continues to gain the attention of researchers and the wider community alike. In this spirit, we have proposed a research topic titled “Progress and Prospects on Skin Imaging Technology, Teledermatology and Artificial Intelligence in Dermatology” and are very pleased indeed that 9 manuscripts on the various aspects of skin imaging technology, 5 manuscripts on teledermatology and 7 manuscripts on AI in dermatology have been published under the banner of this specific research topic.

Skin cancer is one of the most common malignancies worldwide with melanoma being the deadliest form due to its high metastatic potential. Early and accurate detection is crucial for improving patient outcomes. Traditional diagnostic methods, including visual inspection and dermoscopic evaluation are limited by inter-observer variability and require substantial clinical expertise. In recent years, artificial intelligence (AI) has shown promise in enhancing dermatological diagnostics by automating skin lesion analysis with high accuracy. This systematic review aims to evaluate and synthesize current literature on the application of AI for the detection and classification of skin cancer focusing on current trends, AI technologies, data sources, clinical relevance, limitations and future direction. A comprehensive literature search was conducted across PubMed, Scopus, IEEE Xplore, and Web of Science for articles published between January 2010 and May 2025. The PRISMA guidelines were followed, and quality assessment was performed using the PROBAST tool. Out of 6296 identified records, 163 studies met the inclusion criteria. Convolutional neural networks (CNNs) were the most commonly used AI models, frequently trained on publicly available datasets such as ISIC and HAM10000. The reported classification accuracy ranged from 82% to 95%, with several models achieving dermatologist-level performance. However, the generalizability of these models was often limited due to dataset bias, lack of external validation, and insufficient reporting of demographic diversity. Few studies addressed model explainability or clinical integration. AI-based approaches demonstrate strong potential for enhancing the detection and classification of skin cancer with several models showing performance comparable to expert clinicians. Performance metrics including accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were discussed alongside the skin image datasets and the relevant AI tools used in the studies. However, significant challenges remain, including the need for standardized evaluation frameworks, diverse and well-annotated datasets, and rigorous clinical validation. Future research should prioritize model transparency, fairness, and integration into real-world clinical workflows to ensure safe and equitable deployment.

Artificial intelligence (AI) has gained more and more importance in the diagnosis of dermatologic conditions since the COVID-19 pandemic. Most of the literature on AI in dermatology focus on melanoma and non-melanoma skin cancer detection, reporting from 81.0% to 99.0%. Other commonly studied diseases include psoriasis, acne vulgaris, onychomycosis, atopic dermatitis. Although AI has the potential to improve access to dermatologic care, especially in underserved communities, challenges remain in its implementation. Here we review the different applications of AI in dermatology and their outcomes, focusing on the accuracy, sensitivity, specificity of different AI algorithm in the diagnosis of different skin conditions. This review may provide an organized summary of the various applications of AI in dermatology and their potential outcomes.

Recent interest in AI had been driven by an evolution in machine learning resulting in the arrival of ‘deep learning.’ Given sufficient dataset size and processing power, deep learning utilizes Convolutional Neural Networks (CNNs). Deep learning technique is basically the modernized extended version of classical neural networks. The current neural network that is used is more superior in terms of the classical neural network as the current deep learning neural networks had multiple layers [2]. The deep learning method tends to deal with more complex and non-linear data. The deep learning in comparison with the classical neural networks can handle the larger volume and wide complex of data. As it learns directly from the dataset without human direction, deep learning is able to account for inter-data variability as well as process unstandardized data. AI algorithms have been currently used in the diagnosis of diabetic retinopathy, congenital cataracts, melanoma, and onychomycosis [3]. Outside clinical care, AI is being employed to support and potentially replace the roles of healthcare managers in resource, staffing, and financial management.