Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2024 Apr 16;14(4):516.
doi: 10.3390/life14040516.

Artificial Intelligence: A Snapshot of Its Application in Chronic Inflammatory and Autoimmune Skin Diseases

Federica Li Pomi  1 , Vincenzo Papa  2 , Francesco Borgia  3 , Mario Vaccaro  3 , Giovanni Pioggia  4 , Sebastiano Gangemi  2
Affiliations
Review

Artificial Intelligence: A Snapshot of Its Application in Chronic Inflammatory and Autoimmune Skin Diseases

Federica Li Pomi et al. Life (Basel). .

Abstract

Immuno-correlated dermatological pathologies refer to skin disorders that are closely associated with immune system dysfunction or abnormal immune responses. Advancements in the field of artificial intelligence (AI) have shown promise in enhancing the diagnosis, management, and assessment of immuno-correlated dermatological pathologies. This intersection of dermatology and immunology plays a pivotal role in comprehending and addressing complex skin disorders with immune system involvement. The paper explores the knowledge known so far and the evolution and achievements of AI in diagnosis; discusses segmentation and the classification of medical images; and reviews existing challenges, in immunological-related skin diseases. From our review, the role of AI has emerged, especially in the analysis of images for both diagnostic and severity assessment purposes. Furthermore, the possibility of predicting patients' response to therapies is emerging, in order to create tailored therapies.

Keywords: alopecia areata; artificial intelligence; atopic dermatitis; autoimmune disease; hidradenitis suppurativa; inflammation; machine learning; psoriasis; skin; vitiligo.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
The main findings about the role of AI in chronic inflammatory skin diseases are described. Created with BioRender.com.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Borgia F., Custurone P., Li Pomi F., Vaccaro M., Alessandrello C., Gangemi S. IL-33 and IL-37: A Possible Axis in Skin and Allergic Diseases. Int. J. Mol. Sci. 2022;24:372. doi: 10.3390/ijms24010372. - DOI - PMC - PubMed
    1. Zhang J., Zhong F., He K., Ji M., Li S., Li C. Recent Advancements and Perspectives in the Diagnosis of Skin Diseases Using Machine Learning and Deep Learning: A Review. Diagnostics. 2023;13:3506. doi: 10.3390/diagnostics13233506. - DOI - PMC - PubMed
    1. Du A.X., Emam S., Gniadecki R. Review of Machine Learning in Predicting Dermatological Outcomes. Front. Med. 2020;7:266. doi: 10.3389/fmed.2020.00266. - DOI - PMC - PubMed
    1. Allegra A., Tonacci A., Sciaccotta R., Genovese S., Musolino C., Pioggia G., Gangemi S. Machine Learning and Deep Learning Applications in Multiple Myeloma Diagnosis, Prognosis, and Treatment Selection. Cancers. 2022;14:606. doi: 10.3390/cancers14030606. - DOI - PMC - PubMed
    1. Tartarisco G., Tonacci A., Minciullo P.L., Billeci L., Pioggia G., Incorvaia C., Gangemi S. The Soft Computing-Based Approach to Investigate Allergic Diseases: A Systematic Review. Clin. Mol. Allergy. 2017;15:10. doi: 10.1186/s12948-017-0066-3. - DOI - PMC - PubMed

LinkOut - more resources