A system (100, 400) configured to guide a physician in detecting and identifying an origin of a cardiac arrythmia through pace mapping, comprising: at least one processor;; a trained neural network, the neural network trained based on the training dataset,, to predict a degree of correlation between electrophysiological data and a pace-mapping dataset associated with a new patient, the training dataset associated with patients' hearts, for each patient the training dataset comprising: electrophysiological data associated with a cardiac arrhythmia in the patient, pace-mapping datasets, each dataset obtained from an electrode when positioned at a cardiac location in the patient's heart, and correlation data, measuring the degree of correlation between each of the pace-mapping datasets and the electrophysiological data; and memory storing instructions that, when executed by the at least one processor, cause the system to: during a pace-mapping procedure of the heart of a new patient: receive input data, comprising: electrophysiological data associated with a cardiac arrhythmia in the new patient,, and a pace-mapping dataset, obtained from an electrode when positioned at a cardiac location in the heart of the new patient;; feed the pace-mapping prediction model with the input data to obtain a predicted degree of correlation between the electrophysiological data and the pace-mapping dataset of the input data;; and (i) predict, based on the predicted degree of correlation, a cardiac location in the heart of the new patient to be used in a next pace-mapping,, or (ii) track a movement of the electrode and evaluate, based on the tracked movement and based on the predicted degree of correlation, whether the movement is in a direction that corresponds to an increasing degree of correlation. at least one processor;; a trained neural network, the neural network trained based on the training dataset,, to predict a degree of correlation between electrophysiological data and a pace-mapping dataset associated with a new patient, the training dataset associated with patients' hearts, for each patient the training dataset comprising: electrophysiological data associated with a cardiac arrhythmia in the patient, pace-mapping datasets, each dataset obtained from an electrode when positioned at a cardiac location in the patient's heart, and correlation data, measuring the degree of correlation between each of the pace-mapping datasets and the electrophysiological data; and electrophysiological data associated with a cardiac arrhythmia in the patient, pace-mapping datasets, each dataset obtained from an electrode when positioned at a cardiac location in the patient's heart, and correlation data, measuring the degree of correlation between each of the pace-mapping datasets and the electrophysiological data; and memory storing instructions that, when executed by the at least one processor, cause the system to: during a pace-mapping procedure of the heart of a new patient: receive input data, comprising: electrophysiological data associated with a cardiac arrhythmia in the new patient,, and a pace-mapping dataset, obtained from an electrode when positioned at a cardiac location in the heart of the new patient;; feed the pace-mapping prediction model with the input data to obtain a predicted degree of correlation between the electrophysiological data and the pace-mapping dataset of the input data;; and (i) predict, based on the predicted degree of correlation, a cardiac location in the heart of the new patient to be used in a next pace-mapping,, or (ii) track a movement of the electrode and evaluate, based on the tracked movement and based on the predicted degree of correlation, whether the movement is in a direction that corresponds to an increasing degree of correlation. receive input data, comprising: electrophysiological data associated with a cardiac arrhythmia in the new patient,, and a pace-mapping dataset, obtained from an electrode when positioned at a cardiac location in the heart of the new patient;; electrophysiological data associated with a cardiac arrhythmia in the new patient,, and a pace-mapping dataset, obtained from an electrode when positioned at a cardiac location in the heart of the new patient;; feed the pace-mapping prediction model with the input data to obtain a predicted degree of correlation between the electrophysiological data and the pace-mapping dataset of the input data;; and (i) predict, based on the predicted degree of correlation, a cardiac location in the heart of the new patient to be used in a next pace-mapping,, or (ii) track a movement of the electrode and evaluate, based on the tracked movement and based on the predicted degree of correlation, whether the movement is in a direction that corresponds to an increasing degree of correlation. (i) predict, based on the predicted degree of correlation, a cardiac location in the heart of the new patient to be used in a next pace-mapping,, or (ii) track a movement of the electrode and evaluate, based on the tracked movement and based on the predicted degree of correlation, whether the movement is in a direction that corresponds to an increasing degree of correlation.