ArticleAesthetic plastic surgery2026
AI-Enabled Surveillance and Modelling for Counterfeit Botulinum Toxin A: Risk Projection, Patient Safety, and Systemic Reform of Pharmacovigilance.
Article in Aesthetic plastic surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
- Erratum issued
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCounterfeit Botulinum Toxin A (BoNT-A) poses a growing global threat, particularly in aesthetic medicine where regulatory oversight is minimal and underreporting is widespread. Traditional pharmacovigilance systems such as FAERS and EudraVigilance fail to detect early counterfeit exposure due to reliance on delayed, structured reporting. As counterfeit incidents increase across multiple regions, a proactive, data-driven approach is urgently needed.
objectivesThis study aimed to develop and validate an AI-enabled surveillance system capable of detecting counterfeit BoNT-A exposures in real time, projecting regional risk through 2035, and reforming pharmacovigilance in deregulated aesthetic markets.
methodsOver 2.5 million data points from 2015 to 2025 were analyzed, integrating adverse event databases, customs seizure records, patient forums, social media platforms, and global market data. Natural language processing models (BioBERT, RoBERTa, XLM-R) processed multilingual narratives. Counterfeit exposure probabilities were derived using probabilistic inference and anomaly detection. Forecasting models (ARIMA, Prophet, GNNs) projected long-term risk, and robustness was assessed through simulated crises.
resultsThe AI system detected counterfeit exposure signals an average of 31 days before regulatory alerts, with over 86% spatial match accuracy. Platforms like RealSelf and Reddit showed >91% concordance with known adverse event profiles. Forecasts project a global counterfeit exposure increase of 4.9% annually through 2035, with regional peaks in Turkey (risk score 0.76), Brazil, and India. South America is expected to exceed counterfeit-related AEs annually by 2035, a 70% rise. The model maintained >87% accuracy in stress simulations and achieved a mean F1-score of 88.7% across six languages.
conclusionsThis study demonstrates the feasibility and urgency of AI-driven pharmacovigilance in aesthetic medicine. The BoNT-A Risk Burden Index offers a predictive, patient-centred tool to detect and mitigate counterfeit exposures before widespread harm occurs. LEVEL OF EVIDENCE IV: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .
Indexed as
Identifiers
41673282What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.