Understanding International Patient Experiences in South Korean Plastic Surgery: An AI-Assisted Analysis of Patient Reviews, Service Quality, Communication, and Trust
Ishaan Rastogi · Aug 15, 2026
South Korea is one of the most visible destinations in global cosmetic-surgery tourism, and the experience patients report there depends on far more than the surgical result: communication, consultation quality, pricing, staff interaction, and post-operative support all shape how a procedure is remembered and described. This paper reports a mixed-methods study of international patient experience in South Korean plastic surgery, built around a dataset of 74 patient narratives and service ratings across 23 clinics, assembled in three stages: a hand-compiled research document (9 clinics, 36 observations), a Google Maps-sourced expansion (6 clinics, 20 observations), and a deliberately non-Google expansion (8 clinics, 18 observations) drawn from a medical-tourism marketplace (WhatClinic), a Korean-language patient community (Gangnam Unni), a Korean booking/discount platform (Babitalk), a Korean review aggregator (Sungyesa), a medical-tourism referral marketplace (Bookimed), and a public social-media post (Threads). Every clinic in the combined dataset has at least three of five service dimensions populated by real review content. Price remained the most consistently low-scoring dimension (lowest or tied-lowest at 11 of 22 comparable clinics), while staff interaction and post-operative care were the most consistently praised. The three-stage collection design produced a direct, measured finding about data-collection method itself: the Google-sourced batch contained zero mixed-sentiment observations, while the non-Google batch recovered three, suggesting the earlier "no mixed sentiment" pattern was a property of Google's specific review-panel selection rather than of web-sourced data in general. The expansion also surfaced a substantive negative review alleging undisclosed additional charges before anesthesia, a documented historical "ghost doctor" controversy independently corroborated against a 2016 Korea Times report[2], and, via Bookimed[3], an unusually long first-person account of an entire multi-week patient journey that included a frightening but ultimately normal post-operative complication. An AI-assisted lexicon classifier was run against all 74 narratives and checked against human-coded labels, reaching 82% sentiment accuracy and a macro F1 of 0.74 on theme classification; a specific, honestly-reported failure mode emerged where the classifier's keyword-based "review credibility" trigger over-fires on the phrase "verified reviewer," a phrasing artifact of how the newer batches were written up rather than a deeper semantic problem. The paper does not rank clinics or make claims about surgical safety; its contribution is a research-based account of what shapes international patient experience, a demonstration of how data-source diversification changes what a review dataset can say, and an assessment of where AI-assisted analysis helps and where it needs human oversight.