System and method for remote ai-based diagnosis

A method for generation of a remote patient diagnosis based on patient-related data, comprising:

• acquiring, by a diagnosis server (DS) node configured to host a machine-learning (ML) module, sensory data from a plurality of biosensors encapsulated into the user wearable device;
• extracting, by the DS node, a plurality of features from the sensory data;
• receiving, by the DS node, blood biomarkers-related data from the portable blood testing device;
• deriving, by the DS node, a plurality of key features from the blood biomarkers-related data;
• querying, by the DS node, a local patients' database to retrieve local historical patients'-related data related to previous patients' engagements on the plurality of features and the plurality of key features;
• generating, by the DS node, at least one feature vector based on the plurality of features, the plurality of key features and the local historical patients'-related data;
• and
• providing, by the DS node, the at least one feature vector to the ML module configured to generate a predictive model based on an underlying neural network for producing at least one diagnosis parameter for generation of the patient-related diagnosis for the at least one medical entity node.

A non-transitory computer readable medium comprising instructions, that when read by a processor, cause the processor to perform:

• acquiring sensory data from a plurality of biosensors encapsulated into the user wearable device;
• extracting a plurality of features from the sensory data;
• receiving blood biomarkers-related data from the portable blood testing device;
• deriving a plurality of key features from the blood biomarkers-related data;
• querying a local patients' database to retrieve local historical patients'-related data related to previous patients' engagements on the plurality of features and the plurality of key features;
• generating at least one feature vector based on the plurality of features, the plurality of key features and the local historical patients'-related data;
• and
• providing the at least one feature vector to a machine-learning module configured to generate a predictive model based on an underlying neural network for producing at least one diagnosis parameter for generation of the patient-related diagnosis for the at least one medical entity node.