Effective problem representations, also known as diagnostic one-liners, include relevant demographics, pertinent history or epidemiological risk factors, duration and tempo of the illness, as well as key signs and symptoms and key data (e.g., physical exam data, demographic information, patient discharge summaries). When possible, signs, symptoms, and test results should be consolidated into a description of a clinical syndrome. Avoid abbreviations to improve the accuracy of Glass AI. This application is not intended to acquire, process or analyze a medical image or a signal from an in vitro diagnostic device or a pattern or signal from a signal acquisition system. Please do not submit that information.

Glass AI is an experimental feature in development for the Glass platform that uses an artificial intelligence (AI) model to draft a differential diagnosis or clinical plan based on a diagnostic problem representation. It is intended for the purpose of supporting or providing recommendations to a healthcare provider about the prevention, diagnosis or treatment of a disease or condition.

AI is a rapidly developing field with intensive and ongoing research and testing. This AI will attempt to output a differential diagnosis or clinical plan for the submitted diagnostic one-liner regardless of the quality of the input. Therefore, the quality and usefulness of the output is variable and highly dependent on the quality of the diagnostic one-liner submitted. Although we have taken precautions against this, depending on the input, AI may generate an output that could be incomplete, incorrect, or biased. All AI output must be interpreted carefully and it never replaces or serves as a substitute for the independent professional judgment of a healthcare provider. A healthcare provider should not rely primarily on the AI output to make a treatment decision or clinical diagnosis regarding an individual patient.

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