Analysis of Social Media's Role in Influencing Aesthetic Procedure Decisions
Bibliographic record
Abstract
Prof. Reza Ghalamghash 11: PhD, Founder of Premium Doctors and Academic Director, Premium College, Toronto, CanadaCorresponding author: Reza Ghalamghash. Tel: +1 (647) 822-9570, E-mail: Reza@PremiumDoctors.orgORCID: 0009-0004-1745-1315AbstractBackground: Aesthetic medicine, a patient-centered discipline, focuses on enhancing physical appearance and psychological well-being through minimally invasive procedures. Social media platforms significantly influence patient expectations, often fostering unrealistic beauty standards and exacerbating body image dissatisfaction and Body Dysmorphic Disorder (BDD). These dynamics present ethical challenges for practitioners, including managing misinformation, ensuring informed consent, and navigating commercial pressures. Advanced technologies, such as wearable devices and Artificial Intelligence (AI), offer tools for objective outcome assessment, personalized treatment planning, and remote monitoring to counterbalance subjective digital influences.Methods: A systematic search was conducted across PubMed, Scopus, Web of Science, Embase, and Google Scholar for peer-reviewed articles (2014–2025) using keywords such as "aesthetic medicine," "social media," "patient expectations," "ethical considerations," and "wearable technology." Inclusion criteria encompassed studies on social media’s influence on aesthetic decisions, ethical implications, and applications of wearable devices and AI. Non-peer-reviewed sources, non-English articles, and studies lacking methodological rigor were excluded. Data were extracted on study design, findings, and limitations, then synthesized thematically to identify trends and gaps.Results: Social media drives unrealistic expectations and increases BDD prevalence (12.65–18.6% in aesthetic patients vs. 0.7–2.4% in the general population). Over 50% of practitioners report filtered images contributing to irrational demands. Ethical challenges include misinformation, inadequate informed consent (only 14.5% of practitioners feel confident), and commercial pressures leading to overtreatment (33.6% acknowledge financial influences). Wearable devices measure skin parameters (e.g., hydration, elasticity), while AI enhances personalized planning and outcome visualization. Remote monitoring improves post-procedure care but faces challenges in data privacy and standardization.Conclusions: Social media fuels aesthetic treatment demand but exacerbates psychological vulnerabilities and ethical dilemmas. Advanced technologies mitigate these issues by providing objective data and realistic simulations. "Premium Doctors" must adopt these tools within an ethically grounded, patient-centered framework, supported by continuous education. Future research should focus on standardized wearable protocols, ethical AI frameworks, and longitudinal psychological impact studies.Keywords: Aesthetic medicine, social media, patient expectations, ethical considerations, wearable technology, Premium Doctors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".