Intelligence In Dermatology Research Articles

Smartphone-Assisted Artificial Intelligence in Dermatology- A Novel Approach to Help General Practitioners in Underserved Areas

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.

Artificial intelligence in dermatology – Where do we stand?

Artificial intelligence in dermatology – Where do we stand?

Harnessing the Power of Artificial Intelligence in Dermatology: A Comprehensive Commentary.

This special article provides a comprehensive commentary on the significant role of artificial intelligence (AI) in the field of dermatology. It explores the potential of AI in various aspects of dermatologic practice, including diagnosis, treatment planning, research and patient management. The article discusses the current state of AI in dermatology, its challenges and the ethical considerations surrounding its implementation. It highlights the transformative impact of AI on dermatologic care and offers insights into the future directions of AI in the field.

Informing a position statement on the use of artificial intelligence in dermatology in Australia.

Artificial Intelligence (AI) is the ability for computers to simulate human intelligence. In dermatology, there is substantial interest in using AI to identify skin lesions from images. Due to increasing research and interest in the use of AI, the Australasian College of Dermatologists has developed a position statement to inform its members of appropriate use of AI. This article presents the ACD Position Statement on the use of AI in dermatology, and provides explanatory information that was used to inform the development of this statement.

Development of High-Quality Artificial Intelligence in Dermatology: Guidelines, Pitfalls, and Potential

Artificial intelligence (AI) or the development of computer systems and algorithms to perform tasks that normally require human intelligence is an active area of investigation, with speculation over future innovations as well as integration of current technology seamlessly into everyday life with applications such as face identification on smartphones, recommendations of shows on streaming applications, use of voice to text, and Global Positioning System optimization by ridesharing services. The continuous development of technology and tools within AI also offers great potential for clinical applications within medicine.

Modern technológiai eszközök (fényképezés, telefonos applikációk és mesterséges intelligencia) alkalmazása a bőrgyógyászatban

Technology is gaining ground in the field of dermatology. Technological advances, mainly provided by mobile phones, offer almost limitless opportunities for medical applications. Mobile devices make it extremely easy to import and transmit information (e.g. photographs, patient data) for further analysis. However, it is important to note that the handling of health-related information, which is sensitive data, requires particular care - which in many cases is not given due attention. New mobile phone applications are appearing almost daily that can benefit both lay people and doctors in diagnosing and treating skin conditions. Nowadays, the analysis of large amounts of data is carried out by computer systems capable of learning and adapting, i.e. artificial intelligence. In addition to analyzing image data in dermatology, artificial intelligence is now successfully used in prevention, patient management, and even cosmetology. This publication provides a brief overview of the basics of dermatological photography, digital tools, applications, and the current state of the art application of artificial intelligence in dermatology.

P54: Development of a national dataset of skin images to support evidence‐based artificial intelligence in dermatology

P54: Development of a national dataset of skin images to support evidence‐based artificial intelligence in dermatology

BT12: Key steps in the coding of an image dataset for the development of artificial intelligence in dermatology

BT12: Key steps in the coding of an image dataset for the development of artificial intelligence in dermatology

BT10 (P54): Development of a national dataset of skin images to support evidence‐based artificial intelligence in dermatology

BT10 (P54): Development of a national dataset of skin images to support evidence‐based artificial intelligence in dermatology

Therapeutic application of machine learning in psoriasis: APrisma systematic review.

Dermatology, being a predominantly visual-based diagnostic field, has found itself to be at the epitome of artificial intelligence (AI)-based advances. Machine learning (ML), a subset of AI, goes a step further by recognizing patterns from data and teaches machines to automatically learn tasks. Although artificial intelligence in dermatology is mostly developed in melanoma and skin cancer diagnosis, advances in AI and ML have gone far ahead and found its application in ulcer assessment, psoriasis, atopic dermatitis, onychomycosis, etc. This article is focused on the application of ML in the therapeutic aspect of psoriasis.

The Role of Standards in Accelerating the Uptake of Artificial Intelligence in Dermatology

Background The use of artificial intelligence (AI) for dermatology is showing great promise in research contexts. However, the clinical use of AI in dermatology is still limited. The uptake of medical imaging standards for dermatology imaging is also limited. Standards adoption is more widespread in other imaging specialties (eg, radiology) as is the clinical use of AI. Digital Image Communication in Medicine (DICOM) is the standard for medical imaging. DICOM standardizes image formats and associated metadata. Further, DICOM facilitates interoperability between actors in the digital health ecosystem. Objective This study aimed to identify how medical imaging standards, in particular DICOM, can support the clinical use of AI. Methods Design Science Research Methodology was used to determine the role of DICOM in AI-based medical imaging workflows. Scenarios were identified and synthesized using expert consensus. Results The key benefits of using DICOM to improve the clinical use of AI were the potential to encode artefacts derived from the AI process as DICOM objects and store them alongside the original images. Such objects include downsized or down-sampled images, segmentation objects, or visual explainability maps (eg, class activation maps). DICOM can facilitate interoperability between actors in the medical imaging workflow pipeline and permits the inclusion of AI evidence creators in this pipeline. Owing to standardized image formats and metadata, DICOM can be beneficial for the curation of multi-institutional data sets. The key challenge of using DICOM is limited uptake in some specialties including dermatology. Conclusions DICOM offers potential to accelerate the clinical adoption of AI in dermatology by addressing several technological issues. More widespread uptake of DICOM in dermatology imaging is required to achieve this potential. Conflicts of Interest None declared.

WITHDRAWN: Artificial intelligence in dermatology: A threat or an opportunity?

Abstract The worldwide explosion of interest in artificial intelligence (AI) has created a before-and-after moment in our lives by generating great improvements in such sectors as the automotive and food production industries. AI has even been called the fourth industrial revolution. Machine learning through AI is helping to improve professional processes and promises to transform the health care sector as we know it in various ways: 1) through applications able to promote health in the general population by providing high-quality information and offering advice for different segments of the population based on prediction models; 2) by developing prediction models based on anonymized clinical data, for preventive purposes in primary care; 3) by analyzing images to provide additional decision-making support for health care providers, for improving specialist care at the secondary level; and 4) through robotics applied to processes that promote health and well-being. However, the medical profession harbors doubts about whether this revolution is a threat or an opportunity owing to a lack of understanding of AI technology and the methods used to validate its applications. This article outlines basic aspects of AI as it is applied in dermatology and reviews the main advances achieved in the last 5 years.

Unintentional consequences of artificial intelligence in dermatology for patients with skin of colour

Recently, there have been concerns that dermatology resources and education lack sufficient material regarding patients with skin of colour(SOC) in the Western world.1 In the UK, we have an increasingly diverse population, of which 14% have non-Caucasian skin types, necessitating a shift in our educational focus to reflect our changing environment in order for dermatologists to feel comfortable managing conditions in SOC.2.

Artificial intelligence in dermatology and healthcare: An overview.

Many aspects of our life are affected by technology. One of the most discussed advancements of modern technologies is artificial intelligence. It involves computational methods which in some way mimic the human thought process. Just like other branches, the medical field also has come under the ambit of artificial intelligence. Almost every field in medicine has been touched by its effect in one way or the other. Prominent among them are medical diagnosis, medical statistics, robotics, and human biology. Medical imaging is one of the foremost specialties with artificial intelligence applications, wherein deep learning methods like artificial neural networks are commonly used. artificial intelligence application in dermatology was initially restricted to the analysis of melanoma and pigmentary skin lesions, has now expanded and covers many dermatoses. Though the applications of artificial intelligence are ever increasing, large data requirements, interpretation of data and ethical concerns are some of its limitations in the present day.

Artificial intelligence in dermatology

Artificial intelligence (AI) refers to the ability of a machine to communicate, reason, and operate independently. There is a need to understand this technology’s progress for future medical care. AI has many applications in the field of medicine, but its use in dermatology is comparatively new. Applications that commonly analyse and classify images and tools like risk assessment calculators are available. Even though many applications exist, the important implementation barriers inclue difficulty in standardization, interpretability, and acceptance by patient and doctor.

Artificial intelligence in dermatology

Dermatology and medicine are producing data at an increasing rate that are progressively difficult to sort and manage. Artificial intelligence (AI) and machine learning are examples of tools that may have the capability to produce significant and meaningful results from these data. Currently, AI and machine learning have a variety of applications in medicine including, but not limited to, diagnostics, patient management, preventive medicine, and genomic analysis. Although the role of AI in dermatology is greater than ever, its use is still extremely limited. As AI is continually developed and implemented, it is essential that stakeholders understand AI terminology, applications, limitations, and projected uses in dermatology. With the continued development of AI technology, however, its implementation may afford greater dermatologist efficiency, greater increased patient access to dermatologic care, and improved patient outcomes.

Artificial intelligence in dermatology for the clinician

As medicine enters the era of artificial intelligence (AI)–augmented practice, dermatology is beginning to witness the integration of AI into the daily practice, particularly in the areas of diagnosis, prognosis, and treatment of skin diseases. Many of the current electronic medical records that dermatologists have incorporated provide guidance in billing, a form of AI at work. The recent advances in visual recognition AI make application and integration of the technology particularly suited for perceptual specialties such as radiology and dermatology. In dermatology, AI is poised to improve the efficiency and accuracy of traditional diagnostic approaches, including visual examination, skin biopsy, and histopathologic examination. This review highlights the current progress of AI in dermatology and provides a basic overview of the technology.

Multiclass Artificial Intelligence in Dermatology: Progress but Still Room for Improvement

We tested a previous 12-disease version of the Han et al. (2018) algorithm and found limited external validity on a 100-image dataset of skin cancer (Navarrete-Dechent et al., 2018). Here, we evaluated the external validity and reliability of the newly updated 174-disease Han et al. (2020b) algorithm and a recently published region-based 178-disease algorithm (Han et al., 2020a) using the same dataset.

Perceptions and attitudes of medical students regarding artificial intelligence in dermatology.

Background: Artificial intelligence (AI) is progressing rapidly, and its application in the field of medicine is increasing. AI algorithms have been shown to be as accurate as dermatologists in classifying various skin diseases based on clinical images. Objectives: To investigate the attitudes of medical students towards the application of AI in dermatology Methods: Between January and February 2020, we performed a survey to medical students from Seoul National University College of Medicine with approval by the Institutional Review Board of Seoul National University Hospital. Results: One hundred students (male/female: 53/47) completed the survey. The median age was 22.5 years. Only 10% stated that they know AI well, whereas 42% stated they do not. Thirty-one percent expressed interest in AI, and 51% declared that they have received AI-related education. Regarding the source of AI-related knowledge, students responded that they mostly received this information from newspapers and television (82/100, 82%), followed by social networking services (41%), lectures (32%), friends or family (29%), and academic papers or books (22%). Most students (83%) agreed that AI education is necessary in medical school curricula. Conclusion: The discrepancy between the degree of interest among medical students and their level of AI education requires a resolution. Medical school curricula should equip students with the skills and knowledge required to understand and apply AI.

Machine Learning in Dermatology: Current Applications, Opportunities, and Limitations.

Machine learning (ML) has the potential to improve the dermatologist’s practice from diagnosis to personalized treatment. Recent advancements in access to large datasets (e.g., electronic medical records, image databases, omics), faster computing, and cheaper data storage have encouraged the development of ML algorithms with human-like intelligence in dermatology. This article is an overview of the basics of ML, current applications of ML, and potential limitations and considerations for further development of ML. We have identified five current areas of applications for ML in dermatology: (1) disease classification using clinical images; (2) disease classification using dermatopathology images; (3) assessment of skin diseases using mobile applications and personal monitoring devices; (4) facilitating large-scale epidemiology research; and (5) precision medicine. The purpose of this review is to provide a guide for dermatologists to help demystify the fundamentals of ML and its wide range of applications in order to better evaluate its potential opportunities and challenges.