​​​​​​​​​​​​​鍾經略醫生【皮膚科碩士 x 美容醫學碩士】課程研究​

​​​​地址 香港中環皇后大道中9號嘉軒廣場

電話 Tel 23630598（註 6 Note 6）

Address: the Galleria, No. 9 Queen's Road Central, Hong Kong

（1）脫墨案例/價錢（2）脫墨影片（3）最新脫墨資訊​ （４）各種脫墨切除術的比較 （５）面部不同部位的縫合技術 （６）脫墨專題討論

 1. Everything about mole removal   2. Case studies & Surgical fees of diff moles   3. Mole removal video 4. Special Topic Discussion 

隨著人工智能（AI）在醫療領域的迅速發展，皮膚科診斷也迎來了前所未有的革新。在香港，脫墨手術日益普及，但痣的性質判斷仍然高度依賴醫生的臨床經驗。如今，AI影像診斷技術正逐步介入這一環節，為病人與醫生提供更精準、更安全的決策支持。

📸 AI如何分析痣的性質？

AI系統透過深度學習模型，分析大量皮膚影像資料，辨識痣的形狀、顏色、邊界、大小與演化特徵。這些特徵與皮膚癌（如黑色素瘤）或良性痣的典型表現有高度關聯。

常見的AI診斷流程包括：

病人拍攝痣的高清照片（手機或診所儀器）
系統比對資料庫中的數萬張已標註影像
判斷痣屬於低風險、中風險或高風險類別

提供建議：是否可安全脫墨、是否需進一步檢查或切片化驗

🧠 AI在香港脫墨診斷的潛力

墨痣的根部深淺、色素分佈與惡性風險難以單靠肉眼判斷。AI可在以下方面發揮作用：

術前風險評估：協助醫生判斷是否適合使用激光或需手術切除
個人化治療計劃：根據痣的深度與位置，建議分段式激光次數與能量設定
術後追蹤：建立病人皮膚檔案，追蹤痣的復發或色素沉着情況

📱 AI工具的實例與應用

目前已有多款AI皮膚診斷工具在國際市場推出，例如：

Helfie SKIN：手機拍照即時分析痣的風險等級
SkinVision：提供初步診斷並建議是否就醫
DermaCompare：建立皮膚影像檔案，追蹤痣的變化

雖然這些工具尚未全面進入香港市場，但已引起醫學美容界的高度關注。

⚠️ 技術限制與倫理挑戰

儘管AI診斷潛力巨大，但仍有以下限制：

影像品質影響判斷：模糊或光線不足的照片可能導致誤判
無法進行觸診或病理分析
膚色與文化差異：部分AI模型訓練資料偏向白人膚質，對亞洲人準確度仍待提升
病人隱私與資料安全：影像資料需妥善加密與儲存，避免洩漏

因此，AI應視為輔助工具，而非替代醫生的臨床判斷。

🔮 未來展望：AI與脫墨的融合

未來，AI有望與脫墨技術深度融合：

結合3D皮膚掃描與AI分析，精準判斷痣的根部深度
與分段式CO₂激光儀器連接，自動調整能量與焦點
建立全港皮膚資料庫，提升整體診斷準確率與安全性

📝 結語：科技與醫學的協奏

AI不只是冷冰冰的演算法，更是醫生的智慧延伸。在脫墨診斷中，AI能提升安全性、減少誤判、優化治療流程。但最終的決策，仍需由具備臨床經驗與美感判斷力的醫生作出。科技與人文的結合，才是醫學美容的真正未來。

🤖 AI in Mole Removal Diagnosis: How Technology Is Transforming Dermatology

With the rapid advancement of artificial intelligence (AI) in the medical field, dermatological diagnostics are undergoing unprecedented innovation. In Hong Kong, mole removal procedures are becoming increasingly common, but determining the nature of a mole still heavily relies on a doctor’s clinical experience. Now, AI imaging diagnostic technology is gradually entering this space, offering more precise and safer decision-making support for both patients and physicians.

📸 How Does AI Analyze Moles?

AI systems use deep learning models to analyze vast amounts of skin image data, identifying features such as shape, color, border, size, and evolution. These characteristics are closely associated with typical presentations of skin cancer (e.g., melanoma) or benign moles.

A typical AI diagnostic process includes:
Patients take high-resolution photos of their moles (via smartphone or clinic equipment)
The system compares them against tens of thousands of annotated images in its database
It categorizes the mole as low-risk, medium-risk, or high-risk

Then it provides recommendations: whether mole removal is safe, or if further examination or biopsy is needed.

🧠 AI’s Potential in Mole Diagnosis in Hong Kong

It’s difficult to assess mole depth, pigment distribution, and malignancy risk with the naked eye alone. AI can assist in several areas:

Preoperative risk assessment: Helping doctors decide whether laser treatment or surgical excision is appropriate
Personalized treatment plans: Suggesting the number and intensity of fractional laser sessions based on mole depth and location
Postoperative monitoring: Creating patient skin profiles to track mole recurrence or pigmentation changes

📱 Examples and Applications of AI Tools

Several AI skin diagnostic tools have already launched internationally, such as:

Helfie SKIN: Analyzes mole risk levels instantly via smartphone photos
SkinVision: Offers preliminary diagnosis and advises whether to seek medical attention
DermaCompare: Builds a skin image archive to monitor mole changes over time

Although these tools haven’t fully entered the Hong Kong market, they’ve garnered significant attention in the medical aesthetics community.

⚠️ Technical Limitations and Ethical Challenges

Despite AI’s vast potential in diagnostics, there are still limitations:

Image quality affects accuracy: Blurry or poorly lit photos may lead to misjudgment
Lack of tactile or pathological analysis
Skin tone and cultural bias: Some AI models are trained predominantly on lighter skin tones, and accuracy for Asian skin still needs improvement
Patient privacy and data security: Image data must be securely encrypted and stored to prevent leaks

Therefore, AI should be viewed as a supportive tool—not a replacement for clinical judgment.

🔮 Future Outlook: Integrating AI with Mole Removal

In the future, AI could be deeply integrated with mole removal technologies:

Combine 3D skin scanning with AI analysis to accurately assess mole depth
Connect with fractional CO₂ laser devices to automatically adjust energy and focus
Build a comprehensive skin database across Hong Kong to improve diagnostic accuracy and safety

📝 Conclusion: A Symphony of Technology and Medicine

AI is more than just cold algorithms—it’s an extension of medical wisdom. In mole diagnosis, AI can enhance safety, reduce errors, and optimize treatment workflows. But final decisions must still be made by doctors with clinical experience and aesthetic judgment. The true future of medical aesthetics lies in the fusion of technology and humanity.

🤖 AI在脫墨診斷中的應用：科技如何改變皮膚醫學