Advancements in medicine learning (ML) have brought dermatology to the ridge of an artificial intelligence (AI) revolution. 

ML is developed to simulate human pattern distinction and problem-solving. After invading our daily lives in innumerable ways ML is now set to enter clinical medicine which is yet to integrate into daily practice. 

ML was once regarded as a tool well outside of a typical clinician’s purview, but it is now witnessing an increased demand for high-quality research which will advance healthcare. 

ML can easily amalgamate in dermatology as this specialty greatly utilizes visual evaluation and pattern recognition.

The search of the literature revealed the frequent use of digital photography for lesion image analysis and classification. 

Additionally, ML has found its potent role regarding: 

• Electronic medical records (EMR)
• Patient laboratory data
• Genomic data ranging from next-generation sequencing to investigate the genetic cause of diseases, to determine relationships between comorbidities, risk factors and disease prognosis, and to develop and anticipate responses to pharmacologic therapies. 

It can be applied for the prediction of adverse drug reactions, responses to therapy in oncologic dermatology and autoimmune and rheumatologic skin disease. 

Bringing together ML and next-generation sequencing will aid in advancing precision dermatology.

Physicians may fear that machines will largely replace clinicians in dermatology, as well as in radiology and pathology but it is not so. Instead, these tools will allow a higher quality of care to the patients and probably will find a crucial place in the daily clinical practice of the dermatologist shortly.

Source: Lee EY, Maloney NJ, Cheng K, et al. Machine learning for precision dermatology: advances, opportunities, and outlook. J Am Acad Dermatol. 2021;84(5):1458-9.