The integration of artificial intelligence (AI) into aesthetic medicine is rapidly transforming the landscape of clinical diagnostics and patient care. One of the most innovative developments in this field is AIDOCVISION, a platform that leverages the power of multimodal large language models (LLMs) to deliver highly detailed, objective, and clinically relevant analyses of the lips and perioral region. This blog entry explores how AIDOCVISION utilizes cutting-edge multimodal AI—specifically models like Llama 4—to elevate lip analysis in both clinical and aesthetic contexts.

What is AIDOCVISION?

AIDOCVISION is a specialized application within the broader Virtual Patient AI platform, developed by Eurodoc. It is designed to support aesthetic medicine and plastic surgery by providing evidence-based, AI-powered consultations. At its core, AIDOCVISION combines advanced computer vision with the reasoning capabilities of large language models to analyze high-resolution images of the lips and surrounding facial structures. The system is powered by clinical foundation models such as MedlibreGPT and incorporates state-of-the-art multimodal LLMs, including Llama 4, which are capable of processing and integrating text, images, and structured clinical data for comprehensive assessments.

How Does Multimodal Lip Analysis Work?

The Multimodal LLM Approach

Unlike traditional teleconsultation platforms that treat images as supplementary to text, AIDOCVISION mirrors the natural flow of in-person consultations by interpreting images alongside patient history and subjective input. Multimodal LLMs like Llama 4 use an “early fusion” transformer architecture to jointly process visual and textual information, allowing the AI to contextualize anatomical findings within the broader clinical picture. This integration enables nuanced, context-aware assessments that go beyond simple pattern recognition.

Technical Implementation

AIDOCVISION follows a structured, multi-phase protocol for lip analysis:

1. Technical Image Assessment: The system first ensures that the uploaded images meet strict clinical standards—evaluating lighting, resolution, angle, and the visibility of key anatomical landmarks.

2. Facial Context Analysis: The AI systematically analyzes facial proportions, such as the golden ratio, facial thirds, and fifths, to assess lip harmony within the overall facial structure.

3. Systematic Lip Structure Analysis: Using advanced computer vision, the model measures and documents specific lip features, including philtral length, vermillion border definition, Cupid’s bow morphology, volume distribution, and symmetry. The perioral region is also assessed for skin quality, rhytids (wrinkles), commissure position, and the presence of marionette lines.

4. Dynamic and Symmetry Evaluation: The AI can analyze changes during facial animation (e.g., smiling), measuring mobility, incisor show, and dynamic symmetry shifts.

5. Aging and Pathology Markers: The system identifies age-related changes such as lip elongation, volume loss, border definition degradation, and the development of vertical rhytids.

6. Comprehensive Output and Recommendations: The analysis is presented in a structured report, including objective measurements (in millimeters and ratios), a summary of key findings, and evidence-based recommendations for enhancement or treatment.

Example Features and Measurements

AIDOCVISION’s multimodal LLMs provide a granular breakdown of the lips, including:

• Philtrum length and definition (e.g., 13mm, moderate definition)

• Vermillion border integrity (percentage loss or irregularity)

• Cupid’s bow angle and symmetry (e.g., 120°, Type II)

• Volume distribution (central vs. lateral segments, quantified reductions)

• Upper-to-lower lip ratio (e.g., 1:1.37 vs. ideal 1:1.618)

• Commissure position (measured deviation from horizontal)

• Rhytid patterns (number, depth, and distribution of vertical lines)

• Dynamic assessments (mobility percentage, incisor show at rest and smile)

• Symmetry analysis (side-to-side projection differences, commissure height, Cupid’s bow offset).

Clinical Benefits of AIDOCVISION Lip Analysis

Enhanced Diagnostic Precision

By leveraging the reasoning and pattern recognition abilities of multimodal LLMs, AIDOCVISION delivers analyses that are both highly accurate and comprehensive. The AI’s outputs are grounded in objective, quantifiable data, reducing subjectivity and inter-observer variability—a common challenge in aesthetic assessments. The system’s ability to reference and ingest up-to-date medical literature further ensures alignment with the latest clinical standards.

Personalized and Scalable Consultations

Clinicians can tailor the AI’s prompts to reflect their own expertise and patient-specific details, making AIDOCVISION a flexible tool adaptable to various practice needs. This personalization extends to the integration of patient-reported outcomes and preferences, supporting shared decision-making and enhancing patient engagement.

Regulatory and Data Privacy Compliance

AIDOCVISION’s locally hosted architecture allows sensitive patient data to remain on-premises, ensuring compliance with stringent data protection regulations such as the EU’s GDPR and the upcoming AI Act. This approach enables direct human oversight, transparent risk management, and full clinician control over AI-driven decision support.

Clinical Outcomes and Case Examples

In practice, AIDOCVISION has demonstrated its utility in both research and routine clinical settings. For example, a typical analysis might reveal moderate age-related changes—such as upper lip elongation, volume loss in lateral segments, and early commissural drooping—while providing precise measurements and tailored recommendations for non-surgical enhancement (e.g., dermal fillers, neuromodulators, skin quality treatments) or surgical considerations (e.g., lip lift eligibility). The system documents changes over time, enabling longitudinal tracking and personalized maintenance plans.

The Future of Multimodal LLMs in Aesthetic Medicine

The rapid evolution of multimodal LLMs, as seen in state-of-the-art models like Llama 4, is opening new frontiers in aesthetic medicine. These models are capable of integrating complex visual and textual data, providing detailed anatomical measurements, and supporting causal reasoning about clinical features and treatment outcomes. As instruction-tuned facial analysis models continue to advance, the potential for even greater accuracy, personalization, and clinical value in lip and facial assessments will only grow.

Conclusion

AIDOCVISION, powered by multimodal LLMs such as Llama 4, represents a paradigm shift in how lip and perioral analyses are conducted in aesthetic medicine. By uniting advanced computer vision with the interpretive power of large language models, this technology delivers objective, reproducible, and clinically actionable insights—benefiting both clinicians and patients. As AI continues to evolve, platforms like AIDOCVISION are poised to set new standards for precision, personalization, and safety in aesthetic consultations.

For clinicians and researchers interested in integrating AIDOCVISION into their practice, the platform offers a glimpse into the future of AI-driven, patient-centered aesthetic medicine—where technology augments human expertise for optimal outcomes.