MedsApp.ai trains robotic systems on archived procedure data, then executes real invasive and non-invasive medical, dental, cosmetic, dermatology, ophthalmology, and veterinary procedures — comparing every step against that training in real time, without a clinician guiding the instrument.
Doctors are trained the same way they've always been — by watching, repeating, and sometimes getting it wrong. MedsApp is trained the same way, minus the third option.
MedsApp.ai is built from two embodiments that work together: a system that learns, and a system that acts. Everything below is drawn directly from the filed claims.
Builds and refines the archived data pool a procedure will be measured against — from human-performed demonstrations, self-training, or both.
Executes the procedure through a robotic apparatus, comparing real-time sensor data to the archived pool at every checkpoint — with no real-time human guidance.
Named and defined in the filing — the mechanisms the controller uses to plan, validate, and correct a procedure in progress.
Step-by-step instructional sequences established from training data before a procedure ever starts — a rehearsed twin of the procedure to come.
Real-time patient data is continuously compared — "bounced" — against archived data from many prior patients, thousands of times per second, to select the next action.
Measured distances from reference markers on the patient, the operating surface, and the instrument — triangulated in a Cartesian frame to validate pose before every step.
The controller doesn't just execute — it watches its own confidence, and escalates the moment something doesn't check out.
Invasive and non-invasive procedures, plus the automated intake and vitals work that surrounds them.
Detection, Inc. has consolidated its autonomous-medicine and matter-detection research into two active patent families.
MedsApp autonomous procedures become available for select non-invasive applications as funding closes.