AI Expert Inference for Standardized Cosmetic Laser Settings
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Solution Overview
Problem
There is a high demand for laser and injectable neurotoxin treatments in cosmetic dermatology, but the availability of trained professionals is limited, and there is a lack of standardization in classifying skin types and conditions, leading to inefficiencies and complications in treatment planning.
Innovation Solution
An AI system that uses an expert system and machine learning to predict appropriate laser settings and injectable medication plans based on patient characteristics, standardizing skin classification and expanding the expertise to a wider user base.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If advanced training in laser use is provided through fellowships, then treatment quality and safety improve, but the number of available training spots remains extremely limited
Solution Approach 1:
The patent creates a digital twin or virtual replica of expert dermatologist knowledge and decision-making processes through AI models. These models are trained on extensive datasets of patient cases, treatment parameters, and outcomes to replicate expert-level judgment. This allows the system to copy and distribute expert knowledge without requiring physical presence of trained physicians, thereby expanding access while maintaining treatment quality.
Solution Approach 2:
The AI system acts as an intermediary between untrained providers and expert-level treatment standards. Rather than requiring providers to become experts themselves, the system mediates by providing real-time guidance, treatment recommendations, and parameter optimization based on patient-specific characteristics, bridging the gap between current provider capability and desired treatment outcomes.
2Adaptability or versatility
If multiple laser brands with unique configuration options are used, then treatment versatility improves, but device complexity and training requirements increase
Solution Approach 1:
The AI system is designed to work across multiple laser platforms and brands, creating a universal interface that translates diverse device-specific parameters into standardized treatment recommendations. The system recognizes different laser types, wavelengths, and configuration options, then provides unified guidance that adapts to each device's capabilities, allowing providers to use various laser brands without needing to master each one's unique controls.
Solution Approach 2:
The system automatically adjusts and optimizes treatment parameters based on the specific laser device being used, patient skin characteristics, and treatment goals. Rather than requiring providers to manually configure complex parameters for each laser brand, the AI dynamically modifies settings such as wavelength, pulse duration, fluence, and spot size to achieve optimal results across different platforms.
3Measurement precision
If standardized classification of skin types and conditions is implemented, then treatment precision improves, but the system must handle diverse patient characteristics across multiple classification scales
Solution Approach 1:
The AI system integrates multiple skin classification scales and patient characteristic assessment tools into a unified evaluation framework. Rather than requiring separate assessments for Fitzpatrick skin type, wrinkle severity, pigmentation, redness, and other parameters, the system combines these evaluations into a comprehensive patient profile that correlates all characteristics with evidence-based treatment recommendations, simplifying the clinical workflow while maintaining precision.
Data Source
AI summary
Systems and methods for predicting cosmetic dermatology treatment plans and human skin characteristics with artificial intelligence are disclosed. According to an aspect, a system comprises one or more processors and memory comprising an expert inference engine. The expert inference engine is configured to receive data that indicates skin characteristics of a patient. Further, the expert inference engine is configured to maintain an expert system knowledge base for operating a therapeutic laser. The expert inference engine is also configured to determine settings for the therapeutic laser based on the expert system knowledge base and the skin characteristics of the patient. Further, the expert inference engine is configured to a user interface configured to present the determined settings for treating the patient.