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Artificial intelligence in diagnosis and monitoring of atopic dermatitis: From pixels to predictions

Pratheek Jain1,2 Farhan Zameer1* Kounaina Khan1 Vinay Alva1,3 Ravish Huchegowda4 Ali Jawad Akki5 Raghu Anjanapura Venkataramanaiah5 Muthuchelian Krishnasamy6 Dilip Apturkar3 Raghavendra Hallur Laxmanashetty2*
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1 PathoGutOmics Laboratory, Alva’s Traditional Medicinal Archive (ATMA), Department of Ayurveda Pharmacology, Alva’s Ayurveda Medical College, Moodubidire, Karnataka, India
2 Centre for Biotechnology, Pravara Institute of Medical Sciences, Ahmednagar, Maharashtra, India
3 Department of Surgery, Dr. Balasaheb Vikhe Patil Rural Medical College, Pravara Institute of Medical Sciences (Deemed to be University), Ahmednagar, Maharashtra, India
4 Department of Neurochemistry, National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru, Karnataka, India
5 Department of Chemistry, Faculty of Science and Technology, Bijapur Lingayat District Educational (Deemed to be University), Vijayapura, Karnataka, India
6 Department of Biotechnology, M.G.R. College, Dr. MGR Nagar, Hosur, Tamil Nadu, India
AIH 2024, 1(2), 48–65;
Submitted: 18 January 2024 | Accepted: 1 March 2024 | Published: 18 April 2024
© 2024 by the Author (s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( )

In any ailment, the identification of the symptoms, detection, and diagnosis plays a pivotal role in treatment and therapy. However, certain diseases share similar symptoms, lacking signature key indicators, which can lead to fallacious or incorrect inferences. Skin disorders, such as pruritus, dermatitis, eczema, psoriasis, and ichthyosis, all present similar symptoms, which confound clinicians. One such commonly misunderstood condition is atopic dermatitis (AD), a chronic inflammatory skin condition characterized by its relapsing nature, which heightens the importance of diagnosis and disease monitoring for effective management. Recent strides in artificial intelligence (AI) have opened avenues for precise diagnosis and continuous monitoring of AD. This review explores and evaluates current applications of AI in the diagnosis and monitoring of individuals with AD emphasizing the need to address challenges and collaborate across intra-, inter-, trans-, and multi-disciplinary domains to maximize the benefits of AI in enhancing the precision of AD diagnosis, ultimately leading to improved patient care and satisfaction through technologically-driven biomedical tools in customized healthcare.

Deep learning
Machine learning
Convolutional neural networks
Artificial neural network
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Conflict of interest
The authors declare that they have no conflicts of interest.
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