Background: Psoriatic arthritis (PsA) is common in psoriasis patients; nevertheless, it is sometimes overlooked. Delayed diagnosis of PsA can lead to joint erosion, axial damage, and impaired physical function. Screening tools are essential for early diagnosis and selecting the right patients for rheumatological evaluation. We aimed to develop a practical and comprehensive screening tool using the ChatGPT and compare its performance with that of validated questionnaires.

Objectives: The objective of this study was to evaluate the performance of the AI-powered PsA screening (AIPS) tool, developed by the ChatGPT, in detecting psoriatic arthritis (PsA) compared with the validated Psoriasis Epidemiology Screening Tool (PEST) and Early ARthritis for Psoriatic patients (EARP) screening questionnaires.

Methods: A prospective study was conducted on adult psoriasis patients who had musculoskeletal complaints but were not diagnosed with PSA. Artificial intelligence (AI)-powered PsA screening (AIPS) was developed by selecting questions on peripheral arthritis, axial inflammation, and enthesitis from multivariate analyses conducted via Chat GPT 4.0. The Psoriasis Epidemiology Screening Tool (PEST), the Early Arthritis for Psoriatic Patients Questionnaire (EARP), and the AIPS questionnaires were completed concurrently by all psoriasis patients. All patients were evaluated for PSA diagnosis by three rheumatologists who were blinded to the questionnaire responses.

Results: The study included 199 patients, 115 (57.8%) of whom were female. The mean age was 44.4 ± 13.3 years. PSA was detected in 84 psoriasis patients (42.2%). The sensitivity of the EARP the questionnaire, 98%, was greater than those of the AIPS and PEST questionnaires, which had sensitivity values of 92% and 83%, respectively. However, the AIPS had a higher specificity at 96% than did the PEST and EARP, with specificities of 91% and 80%, respectively.

Conclusion: The AIPS questionnaire is an effective tool for screening for PsA, exhibiting high sensitivity and specificity. Artificial intelligence can help screen patients, saving time and money.

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[2] Gottlieb AB, Merola JF. A clinical perspective on risk factors and signs of subclinical and early psoriatic arthritis among patients with psoriasis. J Dermatolog Treat. 2022;33(4):1907-1915.

[3] Mishra S, Kancharla H, Dogra S, Sharma A. Comparison of four validated psoriatic arthritis screening tools in diagnosing psoriatic arthritis in patients with psoriasis (COMPAQ Study). Br J Dermatol. 2017;176(3):765-770.

[4] Landini Enríquez VV, Jurado Santa-Cruz F, Peralta-Pedrero ML, Morales- Sánchez MA. Content validity of psoriatic arthritis screening questionnaires: systematic review. Int J Dermatol. 2020;59(8):902-914.

[5] Merola JF, Patil D, Egana A, Steffens A, Webb NS, Gottlieb AB. Prevalence of Musculoskeletal Symptoms in Patients with Psoriasis and Predictors Associated with the Development of Psoriatic Arthritis: Retrospective Analysis of a US Claims Database.

Acknowledgements: NIL.

Disclosure of Interests: None declared.

© The Authors 2025. This abstract is an open access article published in Annals of Rheumatic Diseases under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ). Neither EULAR nor the publisher make any representation as to the accuracy of the content. The authors are solely responsible for the content in their abstract including accuracy of the facts, statements, results, conclusion, citing resources etc.