Rule based artificial intelligence expert system for determination of upper extremity impairment rating

Area of Science:

• Medical Informatics
• Artificial Intelligence in Medicine
• Biostatistics

Background:

• Quantitative evaluation of upper extremity impairment is crucial for disability assessment.
• Current methods often rely on manual, rule-based procedures which can be time-consuming and prone to errors.
• Standardization and consistency in impairment rating are essential for accurate and fair evaluations.

Purpose of the Study:

• To develop and implement an artificial intelligence (AI) system for quantitative evaluation of upper extremity impairment.
• To utilize the rules from the AMA Guides to the Evaluation of Permanent Impairment (Third Edition) within an AI framework.
• To enhance the efficiency, consistency, and precision of impairment rating processes.

Main Methods:

• Development of a rule-based expert system (AI system) on an Apple Macintosh personal computer.
• Implementation of the specific rules outlined in Chapter 3 of the AMA Guides to the Evaluation of Permanent Impairment (Third Edition).
• Application of the AI program to consistently and systematically apply the established impairment evaluation rules.

Main Results:

• The AI system provides a quantitative percentage rating for upper extremity impairment.
• The program demonstrated increased speed and reduced error rates compared to manual evaluation methods.
• The AI system achieved a higher degree of precision in results by avoiding truncation of intermediate values.

Conclusions:

• An AI system can effectively automate the quantitative evaluation of upper extremity impairment based on established guidelines.
• This AI approach offers significant advantages in terms of speed, accuracy, and precision over traditional manual methods.
• The developed system represents a valuable tool for consistent and reliable impairment assessment in clinical and legal settings.