Direct medical treatment predictions using artificial intelligence

A non-transitory computer-readable medium comprising instructions encoded thereon for autonomously determining a treatment for a patient, the instructions when executed causing one or more processors to perform operations, the instructions comprising instructions to:

• receive image data of an anatomy of a patient;
• obtain scores for each of a plurality of anatomical features reflected in the image data by applying a feature extraction model that is a first machine learning model to the image data;
• apply the scores as input to a treatment model, the treatment model comprising a second machine learning model configured to output a prediction of a measure of efficacy of a particular treatment based on features of the patient's anatomy;
• and
• receive, as output from the treatment model, data representative of the predicted measure of efficacy of the particular treatment.

A method for autonomously determining a treatment for a patient, the method comprising:

• receiving sensor data from an electronic device that monitors a patient;
• accessing a machine learning model, the machine learning model configured to output a likelihood that a particular treatment would yield a positive result, where the machine learning model is trained using training data that pairs previously obtained sensor data for a plurality of patients to labels describing whether the particular treatment yielded a positive result for each of the plurality of patients;
• applying the received sensor data to the machine learning model;
• and
• receiving, as output from the machine learning model, data representative of a likelihood of whether the patient would benefit from the particular treatment.

A non-transitory computer-readable medium comprising instructions encoded thereon for autonomously determining a treatment for a patient, the instructions when executed causing one or more processors to perform operations, the instructions comprising instructions to:

• receive sensor data from an electronic device that monitors a patient;
• access a machine learning model, the machine learning model configured to output a likelihood that a particular treatment would yield a positive result, where the machine learning model is trained using training data that pairs previously obtained sensor data for a plurality of patients to labels describing whether the particular treatment yielded a positive result for each of the plurality of patients;
• apply the received sensor data to the machine learning model;
• and
• receive, as output from the machine learning model, data representative of a likelihood of whether the patient would benefit from the particular treatment.