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Multimodal Machine Learning Approach for Diagnosing Atopic Dermatitis.

Alida Widiawaty^1,2, Wresti Indriatmi^2,3, Wisnu Jatmiko⁴

¹Faculty of Medicine, Universitas Riau, Pekanbaru, Riau, Indonesia.

|February 20, 2026

Summary

This summary is machine-generated.

A new artificial intelligence (AI) model accurately diagnoses atopic dermatitis (AD) by combining image analysis and patient history. This AI tool mimics clinical reasoning, improving diagnostic accuracy for this common skin condition.

Keywords:

Atopic dermatitis Clinical decision support Dermatology diagnosis Explainable AI (XAI)MPNet Machine learning Multimodal Artificial Intelligence (AI)ResNet50

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Area of Science:

Dermatology
Artificial Intelligence
Medical Diagnostics

Background:

Atopic dermatitis (AD) is a common, chronic inflammatory skin disease with varied presentations.
Clinical diagnosis of AD can be subjective and inconsistent, especially among general practitioners.
Developing objective diagnostic tools is crucial for improving patient care.

Purpose of the Study:

To develop and evaluate a multimodal artificial intelligence (AI) model for enhanced atopic dermatitis (AD) diagnosis.
To integrate lesion image analysis and structured patient history (anamnesis) for improved diagnostic accuracy.
To compare the performance of the multimodal AI model against image-only and text-only models.

Main Methods:

A two-phase diagnostic study utilizing retrospective and prospective data.
Developed a late fusion model combining ResNet50 image features and MPNet text features.
Classified cases as AD or non-AD based on integrated visual and clinical data, adhering to AAD 2014 criteria.

Main Results:

The multimodal AI model achieved 98.28% accuracy in classifying AD vs. non-AD.
The integrated model outperformed models relying solely on images or text.
The AI mimics physician reasoning, offering consistent and less subjective diagnostic assessments.

Conclusions:

The ResNet50-MPNet multimodal AI model demonstrates high accuracy in diagnosing AD.
The model offers consistent, holistic assessment by mimicking clinician reasoning.
Further external validation and explainable AI (XAI) are necessary for widespread clinical adoption.