Abstract: A system, method, and a computer program product for generating a clinical summary of a patient using artificial intelligence is provided. A patient data that includes unstructured data and structured data is collected from multiple computing devices. Natural language processing models determine clinical issues from the unstructured data. Active clinical issues are determined from the clinical issues. A knowledge graph generated using a relational language model determines treatments associated with the active clinical issues. Active diagnostic and treatment orders are determined from the structured data. Multiple summaries summarizing the active clinical issues, treatments, active diagnostic orders, and active treatment orders are determined using natural language generation models trained to summarize multiple tasks. The multiple summaries are aggregated into a single summary.

Abstract: A system, method, and a computer program product for generating a clinical summary of a patient using artificial intelligence is provided. A patient data that includes unstructured data and structured data is collected from multiple computing devices. Natural language processing models determine clinical issues from the unstructured data. Active clinical issues are determined from the clinical issues. A knowledge graph generated using a relational language model determines treatments associated with the active clinical issues. Active diagnostic and treatment orders are determined from the structured data. Multiple summaries summarizing the active clinical issues, treatments, active diagnostic orders, and active treatment orders are determined using natural language generation models trained to summarize multiple tasks. The multiple summaries are aggregated into a single summary.

Abstract: A system, method, and a computer program product for generating a clinical summary of a patient using artificial intelligence is provided. A patient data that includes unstructured data and structured data is collected from multiple computing devices. Natural language processing models determine clinical issues from the unstructured data. Active clinical issues are determined from the clinical issues. A knowledge graph generated using a relational language model determines treatments associated with the active clinical issues. Active diagnostic and treatment orders are determined from the structured data. Multiple summaries summarizing the active clinical issues, treatments, active diagnostic orders, and active treatment orders are determined using natural language generation models trained to summarize multiple tasks. The multiple summaries are aggregated into a single summary.