Comprehensive health assessment system driven by AI powered breast images analysis

A method comprising:

• receiving a first input comprising an image of a breast of a patient;
• receiving a second input comprising a patient data, wherein the patient data comprises a first genetic data of the patient;
• extracting features from the image and the patient data, using one or more machine learning models, wherein the features comprise a presence of a calcification and a calcification pattern to generate a breast calcification vector;
• augmenting the breast calcification vector with the first genetic data to generate a feature vector;
• determining, using the machine learning models, a first output comprising a first risk for a breast cancer;
• determining, using the machine learning models, a second output comprising a second risk to one or more organs of the patient, wherein the organs comprises one or more of a heart, a kidney, lungs, a pancreas, and a brain of the patient;
• predicting, using the machine learning models, a third output comprising a third risk based on one or more of a healing response, a tumor flow and growth, inflammation and degeneration, a disease relapse, an adverse event, and a clinical response of the patient;
• and
• determining, using a statistical model, a fourth output comprising an overall risk to the patient based on the first risk, the second risk, and the third risk;
• wherein the machine learning models are pre-trained, wherein pre-training comprises training the machine learning models using training data from plurality of patients, wherein the training data corresponding to each patient from the plurality of patients comprises one or more of breast images, chest images, organ images, second genetic data, demographic data, social determinants of health, clinical data, and clinicians' notes;
• and
• wherein the machine learning models comprise a feedback loop to consider one or more of the first input, the second input, and a clinicians' input and improve one or more of the first output, the second output, the third output, and the fourth output in real-time.

A non-transitory computer-readable medium having stored thereon instructions executable by a computer system to perform operations comprising:

• receiving a first input comprising an image of a breast of a patient;
• receiving a second input comprising a patient data, wherein the patient data comprises a first genetic data of the patient;
• extracting features from the image and the patient data, using one or more machine learning models, wherein the features comprise a presence of a calcification and a calcification pattern to generate a breast calcification vector;
• augmenting the breast calcification vector with the first genetic data to generate a feature vector;
• determining, using the machine learning models, a first output comprising a first risk for a breast cancer;
• determining, using the machine learning models, a second output comprising a second risk to one or more organs of the patient, wherein the organs comprises one or more of a heart, a kidney, lungs, a pancreas, and a brain of the patient;
• predicting, using the machine learning models, a third output comprising a third risk based on one or more of a healing response, a tumor flow and growth, inflammation and degeneration, a disease relapse, an adverse event, and a clinical response of the patient;
• and
• determining, using a statistical model, a fourth output comprising an overall risk to the patient based on the first risk, the second risk, and the third risk;
• wherein the machine learning models are pre-trained, wherein pre-training comprises training the machine learning models using training data from plurality of patients, wherein the training data corresponding to each patient from the plurality of patients comprises one or more of breast images, chest images, organ images, second genetic data, demographic data, social determinants of health, clinical data, and clinicians' notes;
• and
• wherein the machine learning models comprise a feedback loop to consider one or more of the first input, the second input, and a clinicians' input and improve one or more of the first output, the second output, the third output, and the fourth output in real-time.