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TRUVACE RECORD VERSION
record: TRV-2026-2252
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-25T18:34:12.480795Z
status: published
lens: p_space
sector: health
headline: Emerging Artificial Intelligence: A New Digital Age Across The Aesthetic Landscape
dek: Patients increasingly form expectations about plastic surgery from social media and other online sources, and artificial intelligence (AI) now functions both as a tool for analyzing that content and as a source of the content itself. This review maps original empirical studies that applied an AI or computational method beyond conventional statistical analysis to social media, online reviews, public digital images, surveys, or crowdsourced data in plastic surgery while reporting an outcome related to the percepti…
gain_title: (none)
problem_title: Research using AI to analyze patient-facing plastic surgery content is mostly descriptive, single-platform, and rarely validated, limiting how far its findings on patient perceptions can be trusted.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: Research using AI to analyze patient-facing plastic surgery content is mostly descriptive, single-platform, and rarely validated, limiting how far its findings on patient perceptions can be trusted.
problem_evidence: remains descriptive, single-platform, and largely unvalidated
quick_read: A review mapped original empirical studies that used AI or computational methods on social media posts, online reviews, public images, surveys, or crowdsourced data in plastic surgery. It counted 26 studies from 2019 to 2026, with most published since 2024, and tracked how they measured patient or public perceptions.

The pattern matters because patients form expectations about surgery from online content, and AI both analyzes that content and generates it. The evidence base for interpreting patient views is thin: few studies were validated, none linked data to patient-reported outcome measures, and it is unclear how well the findings generalize beyond single platforms.
limitation: The source states the field is descriptive, mostly single-platform, and largely unvalidated, with no study linking data to validated patient-reported outcomes or surgical education.
tag: Evidence-backed problem
key_points: Twenty-six studies met inclusion criteria, and 19 of them (73%) were published since 2024. | Facial aesthetic surgery was the most represented domain, with 9 studies (35%). | RealSelf was the most common data source (8 studies, 31%), and 18 studies (69%) analyzed a single platform. | Sentiment or emotion analysis was the most common method, used in 13 studies (50%).
rundown: The review covered studies published between 2019 and 2026 that applied AI or computational methods beyond conventional statistics to patient-related digital data in plastic surgery, and that reported outcomes on perceptions, questions, experiences, or information exposure.

On data and methods, 18 studies (69%) analyzed text alone, and 16 studies (62%) used pretrained models or lexicons as released without training on study data.

On validation, a human comparator appeared in 10 studies (38%) and external validation in only 1 (4%). No study linked patient-generated digital data to a validated patient-reported outcome measure or evaluated such data for surgical education.
sources:
- peer_reviewed | Aesthetic Surgery Journal | https://doi.org/10.1093/asj/sjag241 | 2026-10-24
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