Abstract: Leveraging generative artificial intelligence to provide diagnoses for patients. In some examples, elimination rules based on patient intake data are applied to a very large corpus of candidate diagnoses, such as all known human medical diagnoses, to narrow down the very large corpus of candidates to a meaningful differential diagnosis or final diagnosis. In some examples, targeted information is elicited to determine and apply additional elimination rules to further narrow down the candidate diagnoses. In some examples, patient intake data is classified into risk data and symptom data and processed by data type-specific machine learning models. In some examples, patient intake data is reconciled and correlated between risk and symptom data by specific machine-learning models to improve diagnostic clustering and accuracy.

Abstract: A conversational and embodied Virtual Assistant (VA) with Decision Support (DS) capabilities that can simulate and improve upon information gathering sessions between clinicians, researchers, and patients. The system incorporates a conversational and embodied VA and a DS and deploys natural interaction enabled by natural language processing, automatic speech recognition, and an animation framework capable of rendering character animation performances through generated verbal and nonverbal behaviors, all supplemented by on-screen prompts.