Systems and methods for generating diagnostic information as a function of an electrocardiogram signal

A system for generating diagnostic information as a function of an electrocardiogram (ECG) signal, the system comprising:

• at least a processor;
• and
• a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to:
• receive at least an ECG signal, wherein the at least an ECG signal corresponds to a single ECG acquisition of a single subject;
• receive, using a user interface, a configuration signal, wherein the configuration signal comprises a selection of at least two artificial intelligence (AI) models, wherein the at least two AI models is selected from a plurality of available AI models configured to perform disease-specific detection and at least one AI model of the at least two AI models comprises a neural network configured to process the at least an ECG signal;
• configure a medical AI module as a function of the configuration signal, wherein configuring the medical AI module comprises:
• executing the at least two AI models under control of an orchestration layer configured to dynamically determine execution order between the at least two AI models as a function of features associated with the at least an ECG signal, wherein execution of a first AI model triggers execution of a second AI model based on a result generated by the first AI model;
• and
• analyzing the at least an ECG signal using the selection of the at least two AI models;
• generate a unified output data structure as a function of one or more outputs generated by the at least two AI models, wherein generating a unified output data structure comprises:
• receiving a plurality of diagnostic outputs generated by each AI model of the at least two AI models, each diagnostic output comprising a model-specific result;
• and
• organizing the plurality of diagnostic outputs into a structured data collection by generating a composite risk profile by aggregating likelihood scores and confidence metrics associated with the plurality of diagnostic outputs of each of the at least two AI models, wherein the aggregation comprises weighted summation of the likelihood scores and the confidence metrics;
• and
• output the unified output data structure.