Inspiration
During mass casualty incidents, medical responders are forced to prioritize critical red and yellow zones. We realized that a massive operation blind spot exists right beside them: the green zone. Filled with the “walking wounded”, this area becomes a resource blackhead hole where patients wander off, block emergency vehicle lanes, and suffer from hidden, deteriorating internal injuries because human medics simply do not have the bandwidth to monitor hundreds of individuals at once. We wanted to build a solution that brings active, intelligent management directly to the green zone.
What it does
A.M.I. (Assistant Medical Interface) is a medical rover designed to take over routine green-zone triage during mass emergencies. Patrolling the disaster staging area, A.M.I. Initiates one-on-one conversational intakes with patients using intuitive voice prompts from Eleven Labs. Equipped with an interactive touchscreen featuring a custom, color-coded 6-pain scale and an onboard computer vision camera, the rover evaluates both internal symptoms and external cuts or scrapes. For stable patients, A.M.I.’s AI evaluation talks to the patient and has an nfc card that links to a database for that specific client. From the patients needs, A.M.I. links directly to physical storage drawers, automatically unlocking the exact first-aid supplies needed, such as bandages, gauze, or antiseptic. Beyond supply dispensing, A.M.I. Serves as a vital safety net by continuously monitoring for severe pain thresholds (7-10) or internal distress. If a patient crosses this threshold, the rover instantly locks down its drawers, flashes warning lights, sounds and audible alert chime, and pings human medics via a central command dashboard to ensure rapid re-triage.
How we built it
We designed A.M.I. Around a safety-first hierarchy and hardware-software integration. For the software logic and UI, we engineered conversational AI prompts and structured a dual-priority decision tree where internal systemic symptoms or high pain scores automatically override external injury care. For the visual system, we made a custom 6-face, color-coded scale mapping colloquial pain descriptions from stable green-zone care up to red-flag escalation thresholds. For the hardware architecture, we mapped software triggers to physical actions, linking AI evaluation outputs to solenoid-controlled storage drawers, warning lights, and audio alarm systems.
Challenges we ran into
The biggest challenge was getting all of our apis connected to specifically our hardware which no one had done before. We also had to define the exact threshold where an automated rover must stop treating a patient and hand them off to a human. Additionally, creating an interface that everyday people can understand instantly during an emergency required stripping away medical jargon and replacing it with clear, color-coded visual cues with intuitive prompts.
Accomplishments that we're proud of
We successfully design a specialized, non-generic green-zone pain scale that properly categorizes manageable issues (1-6) versus emergency escalations (7-10). We also created a clear operational scope where A.M.I. acts as a dedicated green-zone companion without overstepping into primary life-or-death triage, successfully blending software AI decision-making with physical hardware automation like drawers, lights, and alarms.
What we learned
Building A.M.I. taught us the importance of time management, and dividing tasks. Building for disaster scenarios taught us how crucial it is to use intuitive language and clear visual indicators so patients can interact with technology effortlessly under stress.
What's next for A.M.I.
To further help healthcare professionals within the area past the borders of the green zone, we aim for the rover to be able to send patient profiles to the nearest hospital or urgent care if the patient’s condition worsens or if they want additional dedicated care. These patient profiles would include anecdotes of the patient’s condition at the site of the incident and important vitals such as heart rate and blood pressure. We also aim to implement additional software to make the rover entirely autonomous to elevate the patient’s experience and being able to visually recognize patients within the green zone using green wristbands by using open CV.
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