Elimination Algorithm as the Basis for Human Intuition: A Study of a Successful Expert System for Eye Disease Diagnosis

This paper presents the results of an investigation into the hypothesis that an elimination algorithm may underlie human intuition. The study presents Expert-AI, a remarkable expert system that uses this algorithm to accurately diagnose 225 different eye diseases with speed and simplicity. It is proposed that combinatorial memories in nerve cell axon hillocks may use pattern recognition through this algorithmic search process. A combinatorial recognition model is suggested for the nervous system, instead of the conventional linear summation model. Unlike the latter, which is limited to basic input summation, combinatorial recognition permits the self-reliant storage of memories of intricate patterns and relationships between inputs. This, enables the nervous system to retain and recall more complex memories. By providing greater precision and accuracy, as well as the ability to store vast amounts of information, this model offers a more sophisticated and nuanced understanding of how the nervous system processes and responds to external stimuli. The paper also delves into existing scientific literature to identify previously unexplained neural phenomena that could be understood through this algorithmic process.