Abstract: Systems and methods for chest condition determination can leverage one or more machine-learned models to process radiograph data to determine risk data (e.g., a preliminary diagnosis). For example, systems and methods can utilize a pathology model to process a chest x-ray to generate a tuberculosis diagnosis. The one or more machine-learned models can segment the lungs, can detect features in the data, and can pool the segmentation and located features to determine the diagnosis.

Abstract: Systems and methods for artificial intelligence enabled instant diagnostic follow-up can provide efficiency to the medical diagnosis process by providing multiple examinations in the same medical visit for medical workflows that previously required multiple visits. The systems and methods can provide further medical benefit by implementing triaging systems to ensure the most urgent cases are seen immediately.