SavedAdd your journal

Journals / Akdeniz Tıp Dergisi / 2026 / Cilt: 12 Sayı: 1

The Behavioral Inflection Point in Aesthetic Dermatology: A Global Infodemiology Study on the Impact of Generative Artificial Intelligence on Procedure Trends and Sentinel Complications

Estetik Dermatolojide Davranışsal Dönüm Noktası: Üretken Yapay Zekanın İşlem Trendleri ve Önemli Komplikasyonlar Üzerindeki Etkisine Dair Küresel Bir İnfodemioloji Çalışması

Orhan Şen(Kayseri Şehir Hastanesi)0000-0002-8946-7330
Pages
—
Publication Date
2026-09-30
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Dermatologists and health authorities must assume an active role in curating LLM-mediated aesthetic complication content and developing evidence-based digital health literacy strategies for the AI era.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Objective The global surge in minimally invasive aesthetic procedures has been paralleled by an unprecedented shift in patient digital health-seeking behaviour, catalysed by the public release of Large Language Models (LLMs) in November 2022.
📊
Key Findings & Data:Material and Methods Relative Search Volume (RSV) data for 15 search terms across four categories were extracted from Google Trends (GT) under the "Health" category filter over an 84-month continuous time series (January 2019–December 2025).
💡
Conclusion & Impact:Dermatologists and health authorities must assume an active role in curating LLM-mediated aesthetic complication content and developing evidence-based digital health literacy strategies for the AI era.
🔬Design:Modeling & Simulation
📍Scope:2022 Verileri
⚙️Variables:Estetik Dermatoloji · İnfodemioloji · Büyük Dil Modelleri

Abstract

Objective The global surge in minimally invasive aesthetic procedures has been paralleled by an unprecedented shift in patient digital health-seeking behaviour, catalysed by the public release of Large Language Models (LLMs) in November 2022. The infodemiological signature of this transition, particularly its impact on complication awareness and do-it-yourself (DIY) aesthetic practices remains uncharacterised at the population level. This study aimed to quantify the infodemiological impact of generative artificial intelligence (AI) adoption on patient digital search behaviour in aesthetic dermatology, comparing Pre-AI and Post-AI periods through a validated retrospective infodemiology framework. Material and Methods Relative Search Volume (RSV) data for 15 search terms across four categories were extracted from Google Trends (GT) under the "Health" category filter over an 84-month continuous time series (January 2019–December 2025). November 2022, corresponding to the global launch of ChatGPT (OpenAI, San Francisco, USA), served as the statistical cut-off, defining Pre-AI (January 2019–October 2022; n=46 months) and Post-AI (November 2022–December 2025; n=38 months) cohorts. Between-period RSV differences were assessed using the Mann-Whitney U test (SciPy library, Python Software Foundation, Wilmington, USA). Statistical significance was set at p<0.05. Results Post-AI searches for core aesthetic procedures rose modestly (botox: +29.9%, p<0.001; dermal filler: +23.4%, p=0.003), yet complication-related searches exhibited a markedly disproportionate surge: "dissolve filler" +122.7% (p<0.001) and "laser burn" +54.5% (p<0.001). Among DIY terms, "hyaluron pen" searches collapsed by 66.4% (p<0.001), whereas "microneedling infection" searches increased by 753.6% (p<0.001), reflecting a delayed complication burden from prior pandemic-era DIY practices. AI-related dermatology queries virtually non-existent in the Pre-AI period exhibited logarithmic growth exceeding 1000% (p<0.001). Conclusion The launch of generative LLMs constitutes a measurable "behavioural inflection point" in aesthetic dermatology. The asymmetric surge in complication-focused searches alongside declining device- and chemical-based DIY practices represents a paradigm shift in patient information-seeking behaviour rather than simple tool substitution. Dermatologists and health authorities must assume an active role in curating LLM-mediated aesthetic complication content and developing evidence-based digital health literacy strategies for the AI era.

Özet

Amaç Minimal invaziv estetik prosedürlerdeki küresel artışa, Kasım 2022'de Büyük Dil Modellerinin (LLM'ler) halka açılmasının katalize ettiği, hastaların dijital sağlık arama davranışlarında benzeri görülmemiş bir değişim eşlik etmiştir. Bu geçişin infodemiyolojik imzası, özellikle komplikasyon farkındalığı ve kendin yap (DIY) estetik uygulamaları üzerindeki etkisi nüfus düzeyinde henüz karakterize edilmemiştir. Bu çalışma, onaylanmış bir retrospektif infodemiyoloji çerçevesi aracılığıyla yapay zeka öncesi ve sonrası dönemleri karşılaştırarak, üretken yapay zeka (AI) benimsenmesinin estetik dermatolojide hasta dijital arama davranışı üzerindeki infodemiyolojik etkisini ölçmeyi amaçlamıştır. Gereç ve Yöntemler Dört kategoriye ayrılmış 15 arama terimi için Göreceli Arama Hacmi (RSV) verileri, 84 aylık sürekli bir zaman serisi (Ocak 2019-Aralık 2025) boyunca "Sağlık" kategori filtresi altında Google Trends'ten (GT) çıkarılmıştır. ChatGPT'nin (OpenAI, San Francisco, ABD) küresel lansmanına karşılık gelen Kasım 2022 istatistiksel kesme noktası olarak hizmet etmiş ve Yapay Zeka Öncesi (Ocak 2019-Ekim 2022; n=46 ay) ve Yapay Zeka Sonrası (Kasım 2022-Aralık 2025; n=38 ay) kohortlarını tanımlamıştır. Dönemler arası RSV farklılıkları Mann-Whitney U testi (SciPy kütüphanesi, Python Software Foundation, Wilmington, ABD) kullanılarak değerlendirilmiştir. İstatistiksel anlamlılık p<0,05 olarak belirlenmiştir. Bulgular Temel estetik prosedürler için yapay zeka sonrası aramalar mütevazı bir artış göstermiştir (botoks: %29,9, p<0,001; dermal dolgu: +%23,4, p=0,003), ancak komplikasyonla ilgili aramalar belirgin şekilde orantısız bir artış sergilemiştir:"dolgu eritme" +%122,7 (p<0,001) ve "lazer yanığı" +%54,5 (p<0,001). DIY terimleri arasında, "hyaluron pen" aramaları %66,4 oranında çökerken (p<0,001), "mikroiğneleme enfeksiyonu" aramaları %753,6 oranında (p<0,001) artmış ve bu durum önceki pandemi dönemi DIY uygulamalarından kaynaklanan gecikmiş bir komplikasyon yükünü yansıtmıştır. Yapay zeka öncesi dönemdeneredeyse hiç var olmayan yapay zeka ile ilgili dermatoloji sorguları %1000'i aşan logaritmik bir büyüme sergilemiştir (p<0,001). Sonuç Üretken LLM'lerin piyasaya sürülmesi estetik dermatolojide ölçülebilir bir "davranışsal dönüm noktası" teşkil etmektedir. Cihaz ve kimyasal temelli DIY uygulamalarındaki azalmanın yanı sıra komplikasyon odaklı aramalardaki asimetrik artış, basit bir araç ikamesinden ziyade hastaların bilgi arama davranışlarında bir paradigma değişimini temsil etmektedir. Dermatologlar ve sağlık otoriteleri, LLM aracılı estetik komplikasyon içeriğinin küratörlüğünde aktif bir rol üstlenmeli ve yapay zeka çağı için kanıta dayalı dijital sağlık okuryazarlığı stratejileri geliştirmelidir.

🔖

Cite This Article

Şen, O. (2026). The Behavioral Inflection Point in Aesthetic Dermatology: A Global Infodemiology Study on the Impact of Generative Artificial Intelligence on Procedure Trends and Sentinel Complications. Akdeniz Tıp Dergisi, Cilt: 12 Sayı: 1. https://doi.org/10.53394/akd.1916102

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (26)

  1. Meghe S, Ramapure R, Jaiswal S, Jawade S, Singh S. A comprehensive review of minimally invasive dermatosurgical procedures. Cureus. 2024;16(3):e56152.
  2. Triana L, Palacios Huatuco RM, Campilgio G, Liscano E. Trends in surgical and nonsurgical aesthetic procedures: a 14-year analysis of the International Society of Aesthetic Plastic Surgery-ISAPS. Aesthetic Plast Surg. 2024;48(20):4217-27.
  3. Alibudbud R. Google Trends for health research: its advantages, application, methodological considerations, and limitations in psychiatric and mental health infodemiology. Front Big Data. 2023;6:1132764.
  4. Vourtsis SA, Alevrogianni K, Simou E. Aesthetic plastic surgery and dermatology medical procedures: the impact of internet and social media misinformation and the effect on public health. Aesthet Surg J. 2026:sjag023. doi:10.1093/asj/sjag023. Epub ahead of print.
  5. Wang JV, Saedi N, Geronemus RG. Do-it-yourself cosmetic injectables in dermatology: high-risk, no reward. Dermatol Surg. 2021;47(7):1032-3.
  6. Ali H, Qadir J, Alam T, Househ M, Shah Z. ChatGPT and large language models in healthcare: opportunities and risks. In: 2023 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things (AIBThings). IEEE; 2023. p. 1-4.
  7. Tekchandani N, Mukherjee A, Poonthottam N, Boussios S. Comparative analysis of large language models in dermatological diagnosis: an evaluation of diagnostic accuracy. Cureus. 2025;17(9):e92089.
  8. Eysenbach G. Infodemiology and infoveillance: framework for an emerging set of public health informatics methods to analyze search, communication and publication behavior on the Internet. J Med Internet Res. 2009;11(1):e11.
  9. Mavragani A, Ochoa G. Google Trends in infodemiology and infoveillance: methodology framework. JMIR Public Health Surveill. 2019;5(2):e13439.
  10. Sivesind TE, Szeto MD, Kim W, Dellavalle RP. Google Trends in dermatology: scoping review of the literature. JMIR Dermatol. 2021;4(1):e27712.
  11. Motosko C, Zakhem G, Ho R, Saadeh P, Hazen A. Using Google to trend patient interest in botulinum toxin and hyaluronic acid fillers. J Drugs Dermatol. 2018;17(11):1245-6.
  12. Liao D, Lee M, Shomorony A, Weitzman R, Sclafani AP. Zoom boom or bust? Understanding post-pandemic interest in facial plastic surgery via Google Trends. Laryngoscope. 2025;135(2):657-64.
  13. Azzam DB, Dai YL, North VS, Callahan AB, Heher KL, Kapadia MK, Vagefi MR. Global surveillance of public interest in cosmetic tourism for aesthetic eyelid surgery abroad: cross-sectional infodemiology investigation of internet search trends and social media content. JMIR Infodemiology. 2025;5:e64639.
  14. Ayo-Ajibola O, Davis RJ, Lin ME, Riddell J, Kravitz RL. Characterizing the adoption and experiences of users of artificial intelligence-generated health information in the United States: cross-sectional questionnaire study. J Med Internet Res. 2024;26:e55138.
  15. Farishta D, Shires CB, Dewan K, Knackstedt M, Soleja R, Bundrick P, Bhuiyan MAN, Sanders K, Nathan CO. Trends in cosmetic facial plastic surgery post COVID-19 pandemic. Plast Reconstr Surg Glob Open. 2025;13(7):e6950.
  16. Wang R, Li Y, Li Z, Yao H, Zhai Z. Hyaluronic acid filler-induced vascular occlusion-three case reports and overview of prevention and treatment. J Cosmet Dermatol. 2024;23(4):1217-23.
  17. U.S. Food and Drug Administration. FDA executive summary: general issues panel meeting on dermal fillers [Internet]. Silver Spring (MD): U.S. Food and Drug Administration; 2025 [cited 2026 Mar 20]. Available from: https://www.fda.gov/media/188185/download
  18. Khalifian S, Vazirnia A, Mohan GC, Thompson KV, Jalian HR, Avram MM. Causes of injury and litigation in cutaneous laser surgery: an update from 2012 to 2020. Dermatol Surg. 2022;48(3):315-9.
  19. Voelker R. Needle-free dermal filler injectors prompt safety warning. JAMA. 2021;326(19):1899.
  20. Mehta N, Tyagi M, Ramam M, Khaitan BK. Cutaneous atypical mycobacterial infections: a brief review. Indian Dermatol Online J. 2024;15(6):909-19.
  21. Kantor J. ChatGPT, large language models, and artificial intelligence in medicine and health care: a primer for clinicians and researchers. JAAD Int. 2023;13:168-9.
  22. Goktas P, Grzybowski A. Assessing the impact of ChatGPT in dermatology: a comprehensive rapid review. J Clin Med. 2024;13(19):5909.
  23. Yan S, Du D, Liu X, Dai Y, Kim MK, Zhou X, Wang L, Zhang L, Jiang X. Assessment of the reliability and clinical applicability of ChatGPT's responses to patients' common queries about rosacea. Patient Prefer Adherence. 2024;18:249-53.
  24. Young JN, O'Hagan R, Poplausky D, Levoska MA, Gulati N, Ungar B, Ungar J. The utility of ChatGPT in generating patient-facing and clinical responses for melanoma. J Am Acad Dermatol. 2023;89(3):602-4.
  25. De Angelis L, Baglivo F, Arzilli G, Privitera GP, Ferragina P, Tozzi AE, Rizzo C. ChatGPT and the rise of large language models: the new AI-driven infodemic threat in public health. Front Public Health. 2023;11:1166120.
  26. Liu W, Wei Z, Cheng X, Pang R, Zhang H, Li G. Public interest in cosmetic surgical and minimally invasive plastic procedures during the COVID-19 pandemic: infodemiology study of Twitter data. J Med Internet Res. 2021;23(3):e23970.