Systems and methods for use of generative artificial intelligence (ai) in cardiac patient care

A system for training a whole medical image foundation model, the system comprising:

• at least one memory storing instructions;
• and
• at least one processor configured to execute the instructions to perform operations, including:
• receiving a plurality of medical image datasets;
• extracting local sections of image data from the plurality of medical image datasets;
• obtaining one or more causal variables associated with the local sections and/or patient;
• training one or more self-supervised learning models based on the local sections of image data and the causal variables;
• combining the one or more trained self-supervised learning models with a deep learning network configured to combine a latent representation of the local sections of image data from the one or more trained self-supervised learning models into a patient-level representation;
• and
• combining, with the one or more trained self-supervised learning models and the deep learning network, at least one further network or function configured to accept the patient-level representation as input, the at least one further network or function operable to perform one or more patient-specific prediction tasks.

A non-transitory computer-readable medium storing instructions that, when executed by a computer, cause the computer to perform a method for training a whole medical image foundation model, the method comprising:

• receiving a plurality of medical image datasets;
• extracting local sections of image data from the plurality of medical image datasets;
• obtaining one or more causal variables associated with the local sections and/or patient;
• training one or more self-supervised learning models based on the local sections of image data and the causal variables;
• combining the one or more trained self-supervised learning models with a deep learning network configured to combine a latent representation of the local sections of image data from the one or more trained self-supervised learning models into a patient-level representation;
• and
• combining, with the one or more trained self-supervised learning models and the deep learning network, at least one further network or function configured to accept the patient-level representation as input, the at least one further network or function operable to perform one or more patient-specific prediction tasks.