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

Abstract

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.

Improving community health-care screenings with smartphone-based AI technologies

“…Researchers at Stanford University (Stanford, CA, USA) created a deep-learning platform that could identify malignant melanomas and keratinocyte carcinomas with performance that was comparable to certified dermatologists when grading clinical images. 7 Similarly, smartphone applications have been built to detect psoriasis and dermatitis with sensitivities and specificities of more than 90%. 7 These methods could profoundly increase access to routine skin disease screenings and enable front-line workers to triage these conditions early.…”

The Role of DICOM in Artificial Intelligence for Skin Disease

“…One of the main limitations of deep learning is the input data used to train the system ( 32 ). AI models trained and validated from the same dataset risk overfitting, which is a phenomenon of “knowing the training data too well ( 33 ).” Overfitting results in the predictive output of an AI model only being reliable for the population on which the AI model was trained. To overcome this limitation, external validation or cross-validation is necessary and can be achieved by training the AI model on datasets amalgamated from multiple clinical sources, including different populations and ethnicities.…”

<p>Conceptualising Artificial Intelligence as a Digital Healthcare Innovation: An Introductory Review</p>

“…For example, in dermatology, it has been noted that most algorithms are trained on Caucasian or Asian patients, but that these may yield inaccurate results if used for patients of other ethnicities unless the algorithms have been trained accordingly. 15 A wealth of information regarding individual patients is used by human doctors to assist decision making, including medical records, prescription history and real-time data. Much of the information, especially patients' symptoms, is not easily codifiable and therefore cannot easily be analysed.…”