AI transparency,
page by page.
We use AI for research synthesis, initial drafting, and structured-data generation. Every clinical claim is then revised by a human editor and checked against a primary source. No AI-generated patient photos. No deepfaked before-and-afters. Ever.
Where AI helps
Research synthesis (pulling together ASPS annual statistics, peer-reviewed Plastic and Reconstructive Surgery / Aesthetic Surgery Journal / Annals of Plastic Surgery citations, FDA-approved drug labels, ABPS board registry checks, and CMS coverage policy bulletins), initial drafting of explainer paragraphs, structured-data generation (JSON-LD schema), and internal-linking suggestions. These are editor-assisting tasks where AI speeds up first-pass work without replacing human or clinical judgment.
Where AI doesn’t — and never will
AI does not generate patient photos. No AI-generated patient faces, no AI-generated bodies, no AI-generated before-and-after imagery, ever. The Medvi case (FDA Warning Letter #721455, February 2026) — built on 800-plus AI-generated fake doctor profiles and deepfaked patient images — is the cautionary tale this site is built in deliberate contrast to. AfterLoss Atlas does not publish patient before-and-after photos at all; visual material is limited to anatomy diagrams and procedure illustrations.
AI does not write atomic answers we publish without human editing. AI does not generate cost figures from scratch — those are derived from documented ASPS statistics. AI does not decide candidacy criteria; those are drawn from peer-reviewed surgical literature and ASA perioperative guidance and confirmed by a human editor against the source. AI does not substitute for an editor’s read on whether a recommendation drifts into unauthorized practice of medicine.
How the editorial flow works
1. Researcher gathers primary sources (ASPS Plastic Surgery Statistics Report, peer-reviewed surgical journals, FDA-approved drug labels, ABPS public registry, CMS coverage criteria, ASA perioperative guidance).
2. AI helps draft and structure the explainer copy + JSON-LD schema.
3. Human editor revises for voice, accuracy, AEO hygiene, tone, checks every claim against its source, and removes any AI hallucinations or unsourced claims.
4. Publish with inline citations and a dated “last reviewed” stamp; any figure not yet individually verified is flagged on the page.
One specific AI risk we screen for
Pre-2026 LLM training data is heavy with content from compounding-pharmacy marketing pages and telehealth-brand SEO — the same content the FDA action against Medvi sought to deplatform. AI drafts can echo this pattern (over-promising candidacy, downplaying risks, conflating the “board-certified in cosmetic surgery” credential with ABPS certification). The human editor catches and rewrites this at the editor pass, checking each claim against its primary source. If you spot residual drift, email [email protected].
Why we disclose this
Body-contouring decisions are high-stakes — a botched surgical decision, an under-prepared patient, a wrong-credential surgeon choice can cause real, lasting harm. We’d rather be clear about AI involvement than pretend every word was typed by hand. The test is whether the final output is clinically accurate, sourced, and genuinely useful — not whether AI was involved in producing it. The non-negotiable layer is human-editor review against primary sources, inline citations on every claim, and never AI-generated patient imagery.