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                • EMA 2004 — Guideline on clinical investigation of medicinal products for psoriasis (CHMP/EWP/2454/02)
                • Fink 2018 — Inter- and intra-observer variability of image-based PASI
                • Huang 2023 — AI-based PASI severity assessment: real-world study (SkinTeller)
                • King 2022 — Baricitinib BRAVE-AA1 / BRAVE-AA2 (SALT as FDA / EMA primary endpoint)
                • Mattei 2014 — PASI ↔ DLQI correlation in biologic RCTs (r² = 0.80)
                • Mrowietz 2011 — European treat-to-target consensus for moderate-to-severe psoriasis
                • Olsen 2004 — Alopecia areata investigational assessment guidelines (SALT definition, NAAF)
                • Schaap 2022 — CNN-based automated PASI scoring
                • Schmitt 2014 — HOME IV: EASI as core instrument for clinical signs of atopic eczema
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  • Huang 2023 — AI-based PASI severity assessment: real-world study (SkinTeller)

Huang 2023 — AI-based PASI severity assessment: real-world study (SkinTeller)

Citation​

Huang Y, Wei Q, Li Y, et al. Artificial Intelligence–Based Psoriasis Severity Assessment: Real-World Study With PASI as a Benchmark. JMIR Dermatol. 2023;6:e44932. DOI: 10.2196/44932.

Study design and population​

Development and prospective validation of a deep-learning system for automated PASI scoring. Training set: 14,096 images from 2,367 patients. Internal validation cohort: 405 patients. Comparator: 43 experienced dermatologists from 18 hospitals. Subsequent real-world deployment via the SkinTeller app (3,369 uses across 18 hospitals).

Reported metrics​

  • Mean absolute error (MAE) 2.05 PASI points using 3 input images
  • AI outperformed the 43-dermatologist mean by 33.2 % on PASI estimation
  • Lin's concordance correlation ≈ 0.86; Pearson r ≈ 0.90 vs. trained-dermatologist PASI
  • Sub-score improvements: erythema 23 %, induration 7 %, desquamation 11 %, area ratio 12 %

Surrogate-to-outcome linkage​

Confirms that AI-automated PASI achieves not only acceptable concordance with expert-panel scoring but actively reduces rater variability — directly supporting the clinical claim that automated severity scoring is a valid and (at sub-score level) superior surrogate to manual scoring. Real-world deployment data across 18 hospitals adds ecological validity.

CRIT1–7 appraisal​

CriterionScoreJustification
CRIT1 Relevance3Direct — AI PASI, intended-use device modality.
CRIT2 Methodology2Large training set; prospective validation cohort; multi-centre dermatologist comparator (43 readers, 18 hospitals).
CRIT3 Reporting2MAE, concordance correlation and sub-score gains reported; 95 % CIs not all reported.
CRIT4 Applicability3Image-based, matches CDS modality; real-world deployment data.
CRIT5 Evidence weight2Prospective validation with large real-world comparator cohort.
CRIT6 Risk of bias2Single-country (China); dermatologists used for ground truth rather than biopsy; MAE 2.05 may still cross threshold for individual patients.
CRIT7 Contribution3Strong modern anchor — AI PASI outperforms dermatologist mean, not merely matches it.

Aggregate: strong.

Limitations and notes​

Single-country (China); dermatologist consensus reference standard; MAE 2.05 points can still flip borderline treatment thresholds; no phototype stratification.

Strength as anchor​

Strong complement to Schaap 2022 — where Schaap demonstrates CNN-in-physician-range agreement, Huang shows CNN-beats-dermatologist-mean at sub-score level across 43 readers. Together they sufficiently anchor the AI-PASI analytic-validity claim.

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  • Citation
  • Study design and population
  • Reported metrics
  • Surrogate-to-outcome linkage
  • CRIT1–7 appraisal
  • Limitations and notes
  • Strength as anchor
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