A new synesthesia-based approach for AI in healthcare

Abstract

Recent changes in all fields have rapidly taken place, causing radical changes in various spheres of our society. They have particularly affected artificial intelligence, whose priority is to adopt several human qualities to adapt to the changes. AI algorithms inspired by human characteristics and behavioral patterns vary with the paradigms of big data for satisfying individual requirements and treating the problems of the development of creative reasoning. Therefore, getting inspired by highly complex neurological phenomena and behaviors, whether typical or atypical such as synesthesia, is recommended in learning. The evolution of science confirms the theory and methodology of synesthesia. Therefore, it has been focused on the individual's problem identifying different neuroscientific characteristics and explaining their specific potential in developing creativity. Inspired by this, our work focuses on understanding the phenomenon of sound synesthesia in creative thinking, which is a kind of additional mechanism for the perception and development of skills and abilities to analyze the relationship between sound and color in the text. The approach based on the system-activity correlation was applied in work, ensuring the research's integrity, complexity, structure, and purpose. Finally, we aim to find the symbolism of sounds that supports the semantic core of the meaning of linguistic signs. The NLP (Natural Language Processing) is producing new based models while developing the creative thinking of machines to evolve that of people, aiming to help them reach the rapid changes of the modern world. Key words: synesthesia, artificial intelligence, algorithms, neurodiverse, learning, correlation.