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                • Armstrong 2018 — Online vs in-person care for psoriasis: equivalency RCT
                • Chuchu 2018 — Cochrane review: teledermatology for diagnosing skin cancer in adults
                • Eminović 2009 — Cluster RCT: teledermatology reduces dermatology referrals
                • Finnane 2017 — Teledermatology for the diagnosis and management of skin cancer: systematic review
                • Giavina-Bianchi 2020 — Teledermatology reduces referrals and waiting times (São Paulo, 30,976 patients)
                • Jain 2021 — AI tool for skin-condition diagnosis by PCPs and NPs in teledermatology
                • Moreno-Ramirez 2007 — Store-and-forward teledermatology in skin-cancer triage (Seville, 2,009 teleconsultations)
                • Snoswell 2016 — Cost-effectiveness of store-and-forward teledermatology: systematic review
                • Whited 2013 — Clinical-course outcomes: store-and-forward teledermatology vs. conventional consultation RCT
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  • Jain 2021 — AI tool for skin-condition diagnosis by PCPs and NPs in teledermatology

Jain 2021 — AI tool for skin-condition diagnosis by PCPs and NPs in teledermatology

Citation​

Jain A, Way D, Gupta V, Gao Y, de Oliveira Marinho G, Hartford J, et al. Development and assessment of an artificial intelligence–based tool for skin condition diagnosis by primary care physicians and nurse practitioners in teledermatology practices. JAMA Netw Open. 2021 Apr 1;4(4):e217249. DOI: 10.1001/jamanetworkopen.2021.7249. PMID 33909055.

Study design and population​

Retrospective diagnostic study with randomised case-assignment. 20 primary-care physicians and 20 nurse practitioners reviewed 1,048 teledermatology cases covering 120 skin conditions, with and without AI decision support.

Reported metrics​

  • Agreement with dermatologist reference, PCPs: 48 % → 58 % (+10 pp); OR 2.0 (95 % CI 1.7–2.4)
  • Agreement with dermatologist reference, NPs: 46 % → 58 % (+12 pp); OR 2.2 (95 % CI 1.9–2.6)

Surrogate-to-outcome linkage​

Quantifies the AI-decision-support uplift for non-specialists in the exact care-pathway context where CDS devices are deployed. The +10–12 pp correct-triage uplift maps directly to improved referral appropriateness at the primary-care step, on the causal path to appropriate specialist referral and treatment.

CRIT1–7 appraisal​

CriterionScoreJustification
CRIT1 Relevance3Direct — AI decision support for PCPs / NPs in teledermatology triage.
CRIT2 Methodology2Large prospective diagnostic study; randomised case assignment; within-reader AI-vs-no-AI comparison.
CRIT3 Reporting3ORs with 95 % CIs reported.
CRIT4 Applicability3Non-specialist readers — exact intended-use population for AI CDS.
CRIT5 Evidence weight2Large prospective diagnostic study.
CRIT6 Risk of bias2Retrospective image-only cases; dermatologist-panel reference standard imperfect; limited darker-skin representation.
CRIT7 Contribution3Core anchor for the non-specialist AI-uplift claim in Domain 3.

Aggregate: strong.

Limitations and notes​

Retrospective case bank; US teledermatology context; limited FST IV–VI coverage.

Strength as anchor​

Strong — the most rigorous published quantification of AI-decision-support uplift specifically for PCPs / NPs. Pairs with Liu 2020 (DLS non-inferiority to dermatologists and superiority to PCPs / NPs) to anchor the primary-care-triage mechanism.

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Moreno-Ramirez 2007 — Store-and-forward teledermatology in skin-cancer triage (Seville, 2,009 teleconsultations)
  • Citation
  • Study design and population
  • Reported metrics
  • Surrogate-to-outcome linkage
  • CRIT1–7 appraisal
  • Limitations and notes
  • Strength as anchor
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