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The Non-Contact Infrared Thermometer, model HL710H is intend for the measurement of human body temperature from the forehead. The device is indicated for use by people of all ages in the home.

The Non-Contact Infrared Forehead Thermometer, Model HL710H is an electronic thermometer using an infrared sensor which can put out different signal when measuring different object temperature or in different ambient temperature to measure infrared energy radiated from the forehead. This energy is collected through the Infrared sensor and converted to a temperature value, then display it by LCD. The subject device HL710H features a Bluetooth transmission function, which enables the device transmit measured results to paired Bluetooth-enabled device after measurement. Additional, HL710H has a built-in "High Temperature Notice" feature.

The provided text is a 510(k) summary for a non-contact infrared thermometer (model HL710H). It focuses on demonstrating substantial equivalence to a predicate device rather than providing a detailed study report for meeting specific acceptance criteria for an AI/ML diagnostic device.

Therefore, I cannot extract the information required to fill out the table and answer the study-related questions as they pertain to the evaluation of an AI-based diagnostic algorithm. The document discusses regulatory standards for medical devices and thermometers (e.g., ISO, ASTM, IEC), but it does not describe a clinical study of an AI/ML algorithm that would have acceptance criteria for performance metrics like sensitivity, specificity, or AUC, nor does it mention a ground truth established by experts, an MRMC study, or training/test set details for an AI model.

The document is about a hardware medical device (a thermometer) and its compliance with standards for temperature measurement, electrical safety, biocompatibility, and software development, not about an AI diagnostic algorithm's performance.

To provide the requested information, the input would need to be a clinical study report or an FDA submission that evaluates the performance of an AI/ML-based medical device against specific clinical endpoints using a defined ground truth, separate test/training sets, and expert evaluations.

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