Past Medical History
No known chronic medical conditions. No previous hospitalizations or surgeries. Up-to-date on vaccinations.
AI clinical reasoning simulator
Prepare for OSCEs and clinical practice.
Exam-pack readiness reports for students. Cohort pilots for faculties.
Clinical education partners
Simulation, medical education and teaching-hospital partners used in how students practise the clinical encounter.




For students
Each case carries a scoring file written before you open it: essential questions, essential examinations, essential tests, the accepted diagnosis and its aliases, and a management plan. Your score is computed against that file. The language model writes the narrative feedback; it does not decide the marks.
OSCE-style stations for the week before an exam. Each student works their own cases, and every case is marked on a fixed rubric.
A single score across diagnosis, management, investigations, history and communication, so students know where they stand.
A one-page readiness report students can keep and send to each other. Faculties can ask for the same page across a whole year.
For universities
A faculty can see which part of the encounter each student misses most, and teach to that weakness.
Lab
Every case follows the same structure a real encounter does. You decide what to ask, what to examine, what to order and what to do. The rubric is fixed before you start.
A presenting complaint and a patient who answers in their own words. Ask what you would ask on the ward; nothing is pre-filled.
Request specific physical examination findings. Irrelevant examinations return normal results and cost you time, not marks.
Laboratory panels return values with reference ranges. Instrumental studies return real imaging from the NLM Open-i archive.
Name your working diagnosis. Complex cases include distractor diagnoses that are plausible from the history alone.
Immediate, definitive and follow-up steps, with contraindications checked against the patient in front of you.
A weighted score, a breakdown by domain, the essential questions and tests you missed, and where you were unsafe.
Case study
The simulation is the encounter, not a summary of it. A student has to ask, examine, investigate and decide. The report shows what they covered and where they were unsafe, so they can do it again on a different case.
Chest pain · cardiology
The patient gives a presenting complaint in their own words. The student has to find the history that matters. Nothing is pre-filled.
They request specific findings and tests. An irrelevant examination returns a normal result. Laboratory values come back with reference ranges.
They commit to a working diagnosis and to what they would do now, next and in follow-up. On harder cases, a plausible distractor is there if they stop at the history.
The report marks what they asked, what they ordered, the diagnosis and the plan, and it flags an unsafe step even when the rest of the station went well.
Cases are unlimited, generated across disease categories and the clinical situations a student will meet.
Try a cardiology caseSources and governance
Each case is structured from medical reference content into a fixed case file: presentation, findings, investigation results, accepted diagnosis and management. Imaging is drawn from the NLM Open-i open-access archive.
During the encounter the patient model answers from the case file. It does not browse the web and it does not improvise findings that are not in the file.
Marks are computed by a rubric scorer (version grounded-v1) against the case file: essential questions, examinations, tests, the diagnosis and its aliases, the management plan. The language model writes the explanation of the score. It does not set it.
Urgency recognition and safety flags are recorded outside the weighted score so that a dangerous decision is visible even on an otherwise good attempt.