AI in China’s medical aesthetics sector: From non-core services to clinical device integration

The beauty industry is starting to rethink how its services are delivered as AI moves further into consultation, operations and treatment devices

By CLS Marketwatch

The medical aesthetics industry has been growing briskly in recent years, driven by steady demand in China. But certain issues have also come to the fore, making it a high-risk sector. Against that backdrop, AI technology, with its strengths in data processing, process automation and decision-making support, is ideally suited to address many of the challenges. Accordingly, AI is gradually engaging in critical segments of China’s medical aesthetics industry, covering areas such as marketing, process management and even equipment operation. Yet a significant gap in technology adoption across facilities remains, and issues regarding efficacy and data security also pose real problems.

Holistic supporter in modern aesthetics

The current application of AI in medical aesthetics is concentrated in non-medical areas such as marketing, customer service response, database organization, content generation and process management, where it is an effective tool for reducing labor costs and standardizing service. Vision algorithms allow AI to quickly capture clients’ key facial features and convert them into data parameters, or to analyze patient consultations and summarize findings as electronic medical records. A notable example is the MMGPT AI assistant, designed specifically for the medical aesthetics industry, which has announced its integration with DeepSeek. This enables it to generate conversations through semantic analysis, match user needs via algorithms, and automatically trigger follow-up care.

Beyond this, AI applications are also spreading across optoelectronic medical devices. Leaning on AI’s precision and mechanical accuracy, devices equipped with intelligent ultrasound imaging and AI-controlled systems can automatically identify and annotate subcutaneous tissue layers and output real-time data on average skin thickness and specific fascia layer depth, empowering physicians to adjust treatment parameters accordingly. Similarly, another type of temperature-controlled therapeutic device uses AI algorithms to monitor temperature in real time and adjust radiofrequency output power in milliseconds to ensure treatments remain within effective and safe temperature ranges. These functions play to the core value of AI’s abilities, and can greatly reduce treatment deviations caused by human error.

Why most clinics hesitate on AI

While AI has shown potential in certain areas, it still grapples with multiple practical limitations, leading most medical aesthetic institutions to adopt a cautious stance toward its use. In upstream R&D, the clinical success rate of AI-driven drug development has fallen short of expectations. Similarly, when AI is used to identify potential materials for medical aesthetics, the candidates it proposes often fail due to insufficient efficacy or unexpected toxicity. This is because while AI excels at identifying patterns in known data, the variability in human immune responses and metabolic variations in human bodies far exceed existing clinical data, and the gap between animal models and the human body is difficult to bridge through computational power alone.

More importantly, data has become the biggest bottleneck right now. The severe shortage of valid data, such as data on medical procedures, treatment processes and postsurgical outcomes, directly hampers AI’s potential to evolve from an assistive role to taking on more essential functions.

At the same time, since medical aesthetics deals in large volumes of physiological and health data, the industry is sensitive and prone to public scrutiny. To address this, some firms, like Bloomage Biotechnology (688363.SH) and Beauty Farm (2373.HK), have begun using encrypted data transmission, real-time monitoring and risk control mechanisms to intercept abnormal access and detect data risks. Other companies have employed a dual-review mechanism combining system filtering and manual verification, and use on-premises infrastructure to ensure that sensitive data remains within their internal networks.

It’s also worth noting that industry associations have partnered with internet giants to begin constructing a comprehensive database for the medical aesthetics sector.

Tomorrow’s clinics: AI as future core

Despite the many challenges, insiders are generally optimistic about the prospects for AI in medical aesthetics. Most importantly, AI is expected to have a profound impact on all professions that profit from information gaps. There are already signs that a growing number of beauty seekers are shifting from traditional online media consultations to directly querying AI. And as AI continues to advance, new service models such as unmanned clinics and personal beauty consultants are expected to emerge, boosting efficiency for both upstream R&D and downstream clinics.

In terms of taking greater advantage of AI’s strengths, industry forecasts suggest a pivot from assisting with recommendations to semi-automated decision-making. For instance, So-Young International (SY.US) has already launched AI toolkits that provide end-to-end intelligent services, including medical aesthetics knowledge inquiries, clinic searches, appointment scheduling and reminders. Moreover, AI can also collaborate with cloud computing during treatment to streamline the entire patient journey from detection to consultation, treatment, and follow-up, and accumulate reusable data through post-treatment feedback.

Additionally, the integration of AI and devices is likely to emerge as a trend, with AI potentially used to manipulate devices or even work in conjunction with robotic arms to perform certain tasks. As this extends beyond simply commanding machines to working collaboratively with humans, AI is poised to play an even more central role in the future.

In summary, the application of AI in medical aesthetics has gradually expanded from non-core areas to equipment control and clinical workflows, laying a path for reducing risks associated with human error and improving service standardization. As clinical treatment data accumulates, AI will become better positioned to progress toward semi-automated decision-making, achieve deeper integration with devices, and facilitate new business models. The industry’s ability to pull off this transformation will also ultimately depend on development of its data governance framework.

CLS Marketwatch provides insights and analysis on China’s industries. You can contact the author at liujingyi@cls.cn

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