Artificial Intelligence in Support of Differentiating Diagnostic Tools for Basic Two-Point and Three-Point Grips

This article presents the application of artificial intelligence (AI) methods in the process of differentiating and improving diagnostic tools used to assess basic hand grips, including two-point and three-point grips. Particular emphasis was placed on the use of machine learning (ML) algorithms for the analysis of multidimensional data sets and methods for automatic classification of hand functional parameters. The use of AI technology enabled the identification of characteristic movement patterns, precise interpretation of measurement results, and support for diagnostic decision-making. The developed approach contributed to increasing objectivity, repeatability, and effectiveness of manual dexterity assessment, providing significant support for modern functional diagnostics and rehabilitation, particularly in stroke survivors and the elderly, where these parameters can serve as important diagnostic markers.