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Objective Psoricatic disease remains underdiagnosed and undertreated. We developed and validated a suite of novel, smartphone sensor-based assessments that can be self-administered to measure cutaneous and musculoskeletal signs and symptoms of psoriatic disease. Methods Participants with psoriasis, psoriatic arthritis, or healthy controls were recruited between June 5, 2019, and November 10, 2021, at two academic medical centers. Concordance and accuracy of digital measures and image-based machine learning models were compared to their analogous clinical measures from trained rheumatologists and dermatologists. Results Of 104 study participants, 51 (49%) were female and 53 (51%) were male, with a mean age of 42.3 years (SD: 12.6). Seventy-nine (76%) participants had psoriatic arthritis, 16 (15.4%) had psoriasis and 9 (8.7%) were healthy controls. Digital patient assessment of percent body surface area (BSA) affected with psoriasis demonstrated very strong concordance (CCC = 0.94, [95%CI = 0.91–0.96]) with physician-assessed BSA. The in-clinic and remote target-lesion Physician Global Assessments showed fair to moderate concordance (CCC erythema =0.72 [0.59–0.85]; CCC induration =0.72 [0.62–0.82]; CCC scaling =0.60 [0.48–0.72]). Machine learning models of hand photos taken by patients accurately identified clinically-diagnosed nail psoriasis with an accuracy of 0.76. The Digital Jar Open assessment categorized physician-assessed upper extremity involvement, considering joint tenderness or enthesitis (AUROC = 0.68 (0.47–0.85)). Conclusion The Psorcast digital assessments achieved significant clinical validity, although they require further validation in larger cohorts before use in evidence-based medicine or clinical trial settings. The smartphone software and analysis pipelines from the Psorcast suite are open source and freely available., info:eu-repo/semantics/published