Artificial intelligence for the diagnosis of erythematous-squamous dermatological diseases: technological contributions to primary care.

ABSTRACT:

Background

Accurate diagnoses in dermatology can be challenging for general practitioners. In this context, the support of artificial intelligence tools can be beneficial in the Brazilian primary care setting.

Objectives

To develop an interpretable machine-learning algorithm capable of assisting in the diagnosis of erythematous-squamous dermatological diseases through clinical data, without histopathological support.

Methods

The random-forest algorithm was trained with the public Dermatology database of 366 patients diagnosed with: chronic dermatitis, lichen planus, pityriasis rosea, pityriasis rubra pilaris, psoriasis, or seborrheic dermatitis. The model was evaluated by performance metrics and interpretability techniques.

Results

The model showed good predict

SUBMITTER: Brito Cipriano R 

PROVIDER: S-EPMC12362416 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature