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עמוד בית
Thu, 13.06.24

REVIEWS

IMAJ | volume 26

Journal 2, February 2024
pages: 114-119

Artificial Intelligence and Prediction of Response to Biologics in Psoriatic Disease Using Immunophenotype Data: A Mini Review

1 Department of Rheumatology and Clinical Immunology, University Hospital of Larisa, School of Medicine, University of Thessaly, Larissa, Greece 2 Cyber Security and Artificial Intelligence Specialist, Tel Aviv, Israel 3 Zabludowicz Center for Autoimmune Diseases, Sheba Medical Center, Tel Hashomer, Israel 4 Reichman University, Herzliya, Israel

Summary

Over the last decade the use of artificial intelligence (AI) has reformed academic research. While clinical diagnosis of psoriasis and psoriatic arthritis is largely straightforward, the determining factors of a clinical response to therapy, and specifically to biologic agents, have not yet been found. AI may meaningfully impact attempts to unravel the prognostic factors that affect response to therapy, assist experimental techniques being used to investigate immune cell populations, examine whether these populations are associated with treatment responses, and incorporate immunophenotype data in prediction models. The aim of this mini review was to present the current state of the AI-mediated attempts in the field. We executed a Medline search in October 2023. Selection and presentation of studies were conducted following the principles of a narrative–review design. We present data regarding the impact AI can have on the management of psoriatic disease by predicting responses utilizing clinical or biological parameters. We also reviewed the ways AI has been implemented to assist development of models that revolutionize the investigation of peripheral immune cell subsets that can be used as biomarkers of response to biologic treatment. Last, we discussed future perspectives and ethical considerations regarding the use of machine learning models in the management of immune-mediated diseases.

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