JAAD Calls for Transparent Reporting as AI Enters Evidence Synthesis

The Journal of the American Academy of Dermatology has published a November 2026 article on artificial intelligence in evidence synthesis, arguing that the technology's value in dermatology depends on how transparently researchers report its use. The paper, authored by Michelle A. Kahn, Katharine Tepper and Shoshana Marmon, appears in Volume 95, Issue 5 of JAAD.

The title itself carries the thesis: "Artificial intelligence in evidence synthesis: Benefits, limitations, and the importance of transparent reporting." That framing puts the three authors — writing in dermatology's highest-impact journal — on record that AI can accelerate systematic review work, but only if its role is disclosed clearly enough for readers to judge the output.

Why does this matter for dermatology?

Evidence synthesis sits at the base of nearly every clinical decision dermatologists make. Reviews and meta-analyses shape guidance on psoriasis severity instruments such as PASI, eczema endpoints such as EASI, and the staging frameworks used in melanoma management. If AI tools start screening abstracts, extracting data or drafting syntheses inside that pipeline, the integrity of the entire evidence chain shifts.

The article's appearance in JAAD Volume 95, Issue 5 signals that the specialty's leadership journals now treat AI-assisted synthesis as a present-tense editorial concern, not a hypothetical. November 2026 is the publication date, and the authors — Kahn, Tepper and Marmon — represent the perspective of a leading academic dermatology department engaging directly with the methodology question.

What does the article actually argue?

The authors structure their analysis around three elements named in the title:

• Benefits: where AI can plausibly speed or scale evidence synthesis work in dermatology research.
• Limitations: the failure modes and constraints that clinicians and reviewers must weigh before trusting machine-assisted conclusions.
• Transparent reporting: the authors' central prescription — that any use of AI in the synthesis pipeline should be reported explicitly, so editors, reviewers and readers can evaluate what a tool did and what a human verified.

The ordering matters. The authors do not dismiss AI; they place reporting standards as the condition under which its benefits become usable. Without transparency, in their framing, the limitations can't be audited at all.

Who wrote it, and where does it land?

Michelle A. Kahn, Katharine Tepper and Shoshana Marmon authored the piece. Marmon is a dermatologist known for methodologically rigorous work, and the authorship mix suggests the article addresses both the research and clinical sides of the question.

Placement in JAAD — Volume 95, Issue 5, dated November 2026 — gives the argument institutional weight. JAAD is the flagship journal of the American Academy of Dermatology, and its viewpoint and commentary sections function as an early signal of where editorial policy and author guidance may head next.

What are the practical implications?

For dermatology researchers, the message is straightforward: if you use AI to screen studies, extract data or draft portions of a review, say so — and specify how. For journal editors, the article supplies a rationale for requiring AI-use disclosures in synthesis work. For clinicians reading the literature, it's a prompt to check methods sections for how conclusions were assembled.

The timing is also notable. AI-assisted tools have spread through biomedical literature faster than reporting norms have formed, and a November 2026 paper in JAAD positions dermatology among the specialties formalizing that conversation rather than deferring it.

What comes next?

Watch for follow-on guidance — disclosure checklists, editorial policies or formal reporting standards for AI in evidence synthesis — as journals respond to arguments like the one Kahn, Tepper and Marmon lay out in JAAD Volume 95, Issue 5.