Application, Progress, Challenges and Coping Strategies of Artificial Intelligence in Laser Diagnosis and Treatment of Dermatology

Abstract

Selective photothermolysis is the basic theoretical foundation of laser therapy, and now a highly used diagnostic and treatment method in dermatology is based on it. Although laser therapy has shown some good curative effects on various skin diseases, clinical application still faces many problems: the parameters are not standardized, the criteria for choosing them are inconsistent, the assessment of efficacy is mostly based on subjective visual checks, and dark-skinned people are more prone to adverse effects such as burns, pigment changes, etc. The general process and applications of artificial intelligence in laser dermatology will be introduced in this paper, such as pre-operative quantitative analysis of lesion images, intelligent adjustment of laser energy during surgery, and objective quantification of the effect of post-operative treatment. There are many serious problems in the actual operation that have not been solved yet; there is a lack of high-level clinical validation data, an opaque "black-box" mechanism for algorithms, data bias due to racial imbalance, a fragmented industrial supervision system, unreliable content hallucination from large language models, etc. The four problems that need to be solved in the new round of targeted countermeasures are large-scale multicenter cohort studies, the development of interpretable AI models, fair and standardised patient data privacy governance, and optimisation of human-machine collaborative clinical workflows. The aims of this paper are to offer theoretical support for the standardisation of clinical application of AI-assisted laser intervention and to help realise the goal of personalised and precise skincare.