Abstract: An automated computerized system for predicting treatment efficacy, comprising: a system server configured to: communicate with external medical sources; store medical information from the external medical sources in a database; and analyze the medical information using Natural Language Processing (NLP) and artificial intelligence tools; the system server comprises a machine learning module configured to: collect patients' profiles using a personalized interactive chatbot, treatments' protocols and patients' outcomes; analyze the patients' profiles, treatments' protocols and patients' outcomes; and find connections and/or correlations between the users' profiles, treatments' protocols and patients' outcomes thereby enabling to predict treatment efficacy.

Abstract: An automated method for screening and preventing unnecessary medical/surgical procedures, comprising: retrieving medical/surgical procedures data from external and internal sources; computerizing a dynamic questioner with complex relations between questions and answers and a different impact for each answer in the context of the specific scenario and procedure by using experts input, latest research, statistics and machine learning modules; receiving from a user a request to provide a recommendation for a given medical/surgical procedure; providing a customized dynamic questionnaire to said user; computing a relative indication according to said answer's relative importance and impact on the decision to conduct said medical/surgical procedure; and generating a specific output for said user based on said medical/surgical procedure and the relative impact, including a relative indication for said medical/surgical procedure.

Abstract: An automated method of creating synthetic and/or semi-synthetic medical files database for machine learning tasks, comprising: retrieving medical data from external sources; extracting information from the medical data; generating at least one first scenario comprising a plurality of medical factors using the medical data and a rules engine; receiving at least one contradiction marking; updating the rules engine; generating at least one second scenario comprising a plurality of medical factors using the medical data and the updated rules engine; and determining at least one medical procedure recommendation according to the at least one second scenario.

Abstract: An automated method for screening and preventing unnecessary medical/surgical procedures, comprising: retrieving medical/surgical procedures data from external and internal sources; computerizing a dynamic questioner with complex relations between questions and answers and a different impact for each answer in the context of the specific scenario and procedure by using experts input, latest research, statistics and machine learning modules; receiving from a user a request to provide a recommendation for a given medical/surgical procedure; providing a customized dynamic questionnaire to said user; computing a relative indication according to said answer's relative importance and impact on the decision to conduct said medical/surgical procedure; and generating a specific output for said user based on said medical/surgical procedure and the relative impact, including a relative indication for said medical/surgical procedure.

Abstract: An earphone for use with portable communication devices (PCDs), particularly cellular phones, where the electro-magnetic (EM) radiation of the device is distanced from the head of the user, while acoustic waves are collected and brought through an acoustic tube to the ear of the user. The earphone may employ a coupler where the coupler contains a microphone and speaker acoustically insulated from each other. The coupler is positioned distant from both the ear of the user and the PCD. The coupler delivers acoustic waves to an acoustic tube for transmittal to the ear.