Medical & nursing schools
On-demand history-taking and reasoning practice beyond scheduled SP sessions.
Simulation & Education Agent
Medical training and education AI
A simulation agent that plays a synthetic patient so learners can practice history-taking, differential reasoning, and communication as many times as they need: safe, repeatable, non-clinical, built on synthetic data with no real PHI.
Synthetic patientsNon-clinicalNo real PHIStructured feedback
Trusted by global innovators
What is an AI medical training agent?
An AI medical training agent is simulation software that plays a synthetic patient so medical and nursing learners can practice history-taking, clinical reasoning and communication, then scores each session against a faculty rubric. Agnotic Technologies builds these training agents for schools and residency programs, using synthetic cases with no real patient data.
Clinical reasoning takes repetition, but standardized-patient sessions are costly and hard to schedule. The agent supplies those reps with a consistent case: a history, symptoms and a personality a learner can interview and reason about.
It is explicitly educational and non-clinical: it never touches a real patient or real PHI and does not give clinical advice. After each session it explains what a strong workup would have covered.
Simulation architecture
Synthetic case data drives a patient-simulation agent and a separate assessment agent, structured feedback, no real PHI in the loop.
What can the simulation agent do?
Education and simulation built on synthetic patients: repeatable practice with rubric-based feedback and hard non-clinical guardrails.
Synthetic patient simulation
A consistent virtual patient with a defined history, presentation, and affect that responds in character: the same case, runnable as many times as needed.
History-taking & communication practice
Learners rehearse open questioning, difficult disclosures, and shared decision-making, with the agent reacting realistically to tone and completeness.
Synthetic data only
Every case is generated or curated synthetic data. No real patient records and no PHI enter the system, so practice carries zero patient-privacy risk.
Rubric-based assessment
A separate assessment agent scores each session against an educator-defined rubric and returns structured, explainable feedback, staying strictly non-clinical.
Who runs it
Who uses AI medical training?
Residency programs
Residents rehearse difficult disclosures and complex cases repeatedly, safely.
OSCE preparation
Learners drill exam-style stations with rubric feedback before high-stakes assessments.
Continuing education
Practicing clinicians refresh communication and reasoning through simulated encounters.
What simulation-based learning changes
Expected impact, not guarantees: depends on case design and how programs integrate the agent.
On-demand
Reps available any time, no scheduling
0
Real patient records or PHI in the loop
Every session
Rubric-scored with explainable feedback
Compliance-First Healthcare App Development Services Backed by Global Standards
15-Minute Scoping Call01
HIPAA
Health Insurance Portability and Accountability Act
Protect PHI with privacy-first architecture, encrypted storage and transmission, strict access controls, and traceable audit logs.
02
GDPR
General Data Protection Regulation
Implement lawful consent flows, data minimization, retention controls, and secure processing for sensitive health data.
03
FHIR
Fast Healthcare Interoperability Resources
Enable standardized health data exchange across apps, care teams, and systems through standards-based FHIR APIs.
04
HL7
Health Level Seven International
Support enterprise-grade interoperability with HL7-based integrations for records, events, and clinical messaging workflows.
05
HITRUST
Health Information Trust Alliance
Align security programs to healthcare-specific control and risk management practices trusted by providers and ecosystem partners.
06
HITECH
Health Information Technology for Economic and Clinical Health Act
Design with breach notification readiness, digital record safeguards, and operational controls that support regulated care programs.
07
SaMD
FDA Software as a Medical Device
Plan software quality, traceability, and documentation pathways for products that may require SaMD review and submission.
08
MDR (EU)
Medical Device Regulation (European Union)
Prepare EU market-ready processes for risk classification, evidence tracking, and lifecycle governance under MDR expectations.
09
SAMHSA
Substance Abuse and Mental Health Services Administration
Apply confidentiality controls and consent-aware sharing models for behavioral and mental health data experiences.
Standards we build against
Which standards does it integrate with?
LTIxAPIWCAGSynthetic
How It Works
How does a simulation session run?
Load a synthetic case, run the encounter under guardrails, assess against a rubric, and update the learner's record.
1.
Capture: the case & scenario
An educator selects or generates a synthetic case with defined history, findings, and learning objectives. No real PHI is involved.
Synthetic input
2.
Processing: the encounter + guardrails
The learner interviews the synthetic patient; the agent responds in character while non-clinical guardrails keep it educational and block real medical advice.
Non-clinical guardrails
3.
Action: rubric-based feedback
An assessment agent scores the session against the educator's rubric (thoroughness, reasoning, communication) with what a strong workup would have covered.
Human-reviewed rubric
4.
System update: the learning record
Scores, feedback, and progress write back to the LMS, so educators track competency over time and learners see their growth.
Progress logged
Facts and sources
Which rules apply to training data?
Short facts we design training agents around, each linked to its primary source.
FERPA regulations protect the privacy of student education records and apply to educational agencies and institutions that receive funds under any program administered by the U.S. Department of Education.
Source: 34 CFR 99.1 and 99.2 (eCFR)HIPAA allows two ways to de-identify health data: expert determination, or the safe harbor method, which removes 18 categories of identifiers.
Source: 45 CFR 164.514(b) (eCFR)The NIST AI Risk Management Framework is a voluntary framework that organizes AI risk work into four functions: Govern, Map, Measure and Manage.
Source: NIST AI 100-1WCAG 2.2 is the W3C Recommendation commonly used as the technical benchmark for accessible web and mobile interfaces.
Source: W3C
Related proof
Read Case StudyLera Health: compliant women's health platform
Related proof of compliant, AI-assisted product delivery, not this exact agent. For Lera Health we built engaging, guardrailed AI experiences over health content, the craft a simulation-and-assessment agent depends on.
Compliance & Guardrails
How is simulation kept safe and educational?
A training agent earns trust by never touching real patients and never pretending to be a clinical tool.
PHI handling
There is no PHI to handle: every case is synthetic. Learner data is limited to performance records, kept inside your training platform's boundary.
Human-in-the-loop
Educators author the cases and rubrics and review progress. The agent supports instruction; faculty own assessment standards and sign-off.
Explainability
Feedback is tied to the rubric, each score comes with the reasoning and what a strong response would have included.
Non-clinical framing
The agent is clearly labeled educational, refuses real medical advice, and redirects if used as a live clinical tool.
Frequently Asked Questions
By design there is no PHI to protect: every case is synthetic and no real patient records enter the system. The agent handles learner performance records, which stay inside your training platform's boundary with role-based access and audit logging. In a school, those records may be education records under FERPA, so we follow your institution's FERPA policies.
No. Faculty author the cases and rubrics and own assessment standards; the agent gives learners more reps and consistent first-pass feedback. It augments teaching capacity, it doesn't replace the educator's judgment.
A general chatbot answers questions freely. This is a structured simulation agent that plays a consistent synthetic patient, stays explicitly non-clinical, refuses real medical advice, and pairs with a rubric-based assessment agent.
It depends on how many synthetic cases and rubrics you need, how closely faculty review each case, and whether the agent must launch from your LMS through LTI or report through xAPI. We give you a timeline after the scoping call.
Only when real patient data is involved. With synthetic cases no PHI reaches the system, so no BAA is needed. If faculty want cases adapted from real records, we de-identify them first or sign a BAA before any PHI is shared.
Yes. The agent can launch from your learning management system through LTI and send session scores and progress back, or report learning records through xAPI where your platform supports it.
Yes. The first release covers a small set of synthetic cases for one course or OSCE station, with a faculty rubric and LMS launch in place. More cases and competencies follow once faculty are happy with the feedback.
AI tools help our engineers draft code and tests, but a person reviews every change, and it passes the same automated tests and security checks as any other code. Which tools may see your code or data is agreed with you at the start.
Give your learners safe, unlimited practice
Tell us your curriculum and competencies. We'll design synthetic cases, rubrics, and a non-clinical simulation agent that integrates with your LMS, and prove it with a cohort pilot.
contact@agnotic.com
Partnerships
contact@agnotic.com