From seven minutes to 45 seconds: Dubai Medical University creates a smart chair for emergency triage and an offline aerial ambulance

Healthcare research in the region is shifting from mere conversational software to developing tangible clinical hardware that directly addresses emergency department bottlenecks. In this context, Dubai Medical University unveiled a suite of AI-led applied projects aimed at reducing the initial patient triage time from seven minutes to about 45 seconds, as well as delivering first-aid in remote areas via drones that operate completely offline.

The most prominent project in this initiative isAI-Based Vital Signs Diagnostic Chair, which is an examination platform equipped with a suite of cameras and precise sensors to measure the patient’s weight, height, and blood pressure, as well as heart rate, respiration rate, and skin temperature simultaneously, then automatically and directly transmit the recorded data to the treating physician. Hassa Yahya Loutah, head of the university’s artificial-intelligence and innovation department, explains that case triage in emergency departments traditionally takes between five and seven minutes, while current experimental measurements have succeeded in lowering this average to around four minutes, with a target of reducing the total duration to 45 seconds once the system is fully developed. This acceleration aims to free operational space in hospitals, alleviate patients’ psychological stress upon arrival, and redirect doctors’ and nurses’ effort toward actual diagnosis and treatment planning instead of expending minutes on routine vital-sign collection.

Despite the promising results, the prototype faces precise engineering challenges that the university is addressing in preparation for turning it into a fully commercial product. Foremost among these obstacles is ambient lighting, as the measurement algorithms rely partly on light reflected from the patient’s face, making dim lighting or strong glare a source of errant vital-sign readings. The research teams are currently training the system to adapt to variations in illumination and to handle diverse skin tones and skin conditions accurately, ensuring measurement reliability across all groups.

On the field-response front, the university is developing a medical-aid drone designed to reach remote locations and provide rapid first-aid support before emergency teams arrive. The drone carries an AI-driven first-aid kit organized into six functional compartments, allowing the system to assess the nature of an injury and guide responders through a sequential series of detailed steps to manage the case.The pivotal advantage of this drone lies in its ability to operate without an internet connection, providing a self-guiding support line in isolated environments where coverage is lost, while allowing the system to connect directly to a physician whenever a network is available. This system integrates with a third project called the Smart Safety Gate, a solution that uses thermal cameras to screen students in schools and universities, automatically detecting abnormal temperature spikes to alert nursing staff and curb infection spread.

This technological shift has practical implications for hospital administrators and health-procurement officials throughout the Gulf, Egypt, and the wider region. Shifting the initial examination to automated hardware necessitates redesigning patient-flow pathways in emergency departments and alters the nursing-staffing equation by freeing them from manual measurement routines toward direct care. Relying on computer-vision and facial-reading technologies also places regional health facilities before a need to locally validate these devices for compatibility with diverse skin tones and climate conditions. Moreover, the option of offline operation for drone systems opens a path to overcome ambulance challenges in desert areas, remote construction projects, and energy fields, where conventional cloud-based applications fail in the absence of wireless coverage.

These experiments conclude a trajectory in which medical schools aim to steer their young staff toward developing practical clinical solutions that address health-system burdens, yet the real gamble lies in awaiting validation, licensing, and rapid engineering maturity to transition these models from laboratory halls to real-world settings.