MVP Case
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Mr. Richard Grant
52M · Epigastric Pain
AI-Powered Clinical Training Platform · Flagship Case Live Now
Standardized Patients,
Reimagined At Scale.
SimuMed AI replaces the scheduling bottleneck of human standardized-patient programs with realistic AI patients residents can practice with anytime — unlimited repeats, consistent clinical behavior, zero actor coordination.
- Build patients with specific personality profiles to simulate any patient background, condition, or disease
- Unlimited, on-demand practice
- The same clinically calibrated encounter, every single time
- Create unlimited library of cases across specialties
- Structured debrief data faculty can use instead of chase
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The Opportunity
Standardized-Patient Training Wasn't Built To Scale
Traditional programs require hiring and training human actors, coordinating faculty and schedules, and offer only limited practice windows before a resident's highest-stakes encounters. That scarcity is a solvable, fully digital problem.
With SimuMed AI
- Unlimited, on-demand practice — no actor or scheduling coordination
- The same clinically calibrated encounter, every single time
- A growing library of cases across specialties, not just one
- Structured debrief data faculty can use instead of chase
Not a Replacement — a Force Multiplier
SimuMed AI is a preparatory layer before live standardized-patient sessions, increasing access to deliberate practice without displacing human SPs.
Built on Real Clinical Methodology
Cases are adapted from real standardized-patient scripts and calibrated against dozens of physician–actor encounters for behavioral fidelity.
A Platform, Not a Demo
Mr. Grant is the first of a growing case library, with educator analytics and institutional integration already on the roadmap.
Flagship Case — Live Today
Meet Mr. Grant
A fully realized character — consistent clinical history, realistic emotional responses, and an embedded teaching objective. The first case in a library built to grow.
A security guard who drove himself to urgent care — vomiting, burning stomach pain, and a history that points somewhere his wife doesn't want to hear.
- Unstable angina masking as GI
- NSAID use + untreated HTN
- Teaching: Exertional epigastric pain
Expanding The Library
Four more cases already in development — each with its own diagnostic trap and teaching objective.
Karen Klein
Mary Johnson
Meera Patel
Dalia Reyes
Simple Workflow
How It Works
01
Meet Mr. Grant
A realistic AI patient with a presenting complaint, backstory, and embedded teaching objective — epigastric pain hiding a cardiac diagnosis.
02
Start the Encounter
Enter your name and role, then begin. The patient is waiting — take the history, ask about medications, request the right tests.
03
Build Clinical Skills
Practice history-taking, avoid anchoring bias, recognise atypical presentations, and debrief with the built-in clinical reference.
Live Today
Built for Clinical Training
Every feature below is fully built and running in the live demo — designed around the resident's workflow, not a tech demo.
Realistic AI Conversation
Mr. Grant responds naturally turn-by-turn. He withholds details unless probed, reacts emotionally, and reflects real clinical behaviour — not scripted Q&A.
Live Streaming Avatar
Powered by Anam.ai — real-time voice conversation with full lip-sync, no button-pressing, just talk.
Clinical Cheat Sheet
Draggable, always-on reference panel with Mr. Grant's workup guide, key history questions, full diagnostic interview, and teaching objectives.
Tools Sidebar
Physical exam, diagnostics, and scoring calculators accessible in a collapsible sidebar — built for the encounter workflow without disrupting the conversation.
Roadmap
What's Next
Mr. Grant proves the core simulation engine. Here's what turns it into an institution-ready platform.
In Development
Expanding Case Library
Four additional cases across cardiology, nephrology, endocrinology, and hematology already scripted — Karen, Mary, Meera, and Dalia.
In Development
Educator Dashboard
Session recording and structured transcript review so faculty can evaluate history-taking and diagnostic reasoning after the encounter.
In Development
Automated Evaluation
Checklists aligned to each case's teaching objectives — turning a live demo into a repeatable, gradable training tool.
In Development
Institutional Integration
LMS connectivity and program-level deployment for residency programs, simulation centers, and GME curricula.