Multimodal Machine Learning Approach for Diagnosing Atopic Dermatitis
The multimodal ResNet50-MPNet model shows near-perfect accuracy in diagnosing AD by mimicking clinician reasoning. It offers consistent, holistic assessment but requires external validation and improved interpretability for clinical adoption. Continued AI-clinician collaboration is vital to translating this promising technology into real-world dermatological care.
Authors
- Endi Novianto (ORCID: https://orcid.org/0000-0002-8688-4948)
- Sherly Dermawan
- Aria Kekalih (ORCID: https://orcid.org/0000-0001-7811-097X)
- Hendra Gunawan (ORCID: https://orcid.org/0000-0002-6924-0833)
- Alif Wicaksana Ramadhan (ORCID: https://orcid.org/0009-0002-4090-6950)
- Wisnu Aryo Jatmiko
- Alida Widiawaty
- Muhammad Febrian Rachmadi
- Tengku Laras Malahayati
- Wresti Indriatmi
- Pramudita Satria Palar
Institutions
- Bandung Institute of Technology (ID)
- University of Indonesia (ID)
- Padjadjaran University (ID)
- Riau University (ID)
Publication Details
- Journal
- F1000Research
- Published
- 2026-02-09
- DOI
- https://doi.org/10.12688/f1000research.169102.2
- Primary Topic
- Dermatology and Skin Diseases
- Type
- article
- Field-Weighted Citation Impact
- 0.00