In Russia, a “Smart” Robot Patient Puts Medical Students to the Test
A new AI‑powered robotic patient developed in Russia simulates dynamic hypertension cases, allowing medical students and physicians to practice diagnosis and treatment in realistic clinical scenarios
Specialists at Perm National Research Polytechnic University (PNRPU), together with the regional cardiovascular surgery center and a medical university, have developed a robotic simulation system for training diagnostic and treatment skills related to hypertension.
Unlike standard mannequins, the robot behaves like a real patient. It can model various clinical scenarios — from elevated blood pressure to full hypertensive crisis.
Beyond Template-Based Treatment
Machine-learning algorithms analyze initial “patient” data — age, sex, heredity, lifestyle, and comorbidities — and generate real‑time changes in condition. This enables students to practice individualized diagnostic and treatment strategies rather than relying on rigid templates.
Both medical students and practicing physicians participate in training sessions. The simulator serves as a full‑scale training platform where users can practice history‑taking, prescribing therapy, and responding to complications.
Proven Effectiveness
The system’s effectiveness has been confirmed experimentally. Students who trained with the robot demonstrated diagnostic and treatment accuracy comparable to peers working with real patients. At the same time, the simulator makes it possible to reproduce rare or atypical cases seldom encountered in clinics, enhancing the completeness of medical training.
From Interns to Professors
The simulator can be used to train a wide range of specialists: interns, practicing therapists, and cardiologists. It builds skills that are difficult to develop in traditional classroom settings — especially when access to real patients is limited or rare pathologies must be studied.
Large‑scale implementation of the electronic “patient” could significantly improve medical training and raise the accuracy of early hypertension diagnostics. Over time, this may help reduce complications in real patients by giving physicians more real‑world‑like practice before they enter clinical work.
Only one thing is missing for the full picture: the robot patient has yet to be given a name.
