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Scaling Safe & Trustworthy
Clinical AI
+CLINICAL DECISION SUPPORT SYSTEM (CDDS)
+SIMULATION BASED TRAINING
+AI/ML PLATFORM
+IT INTEGRATION PLATFORM
As Head of Product & UX, I led the strategic vision, cross-functional execution, and systems architecture for a modular, AI-first clinical platform — combining explainable AI, synthetic data, immersive simulation, and enterprise-grade infrastructure into scalable, regulatory-compliant solutions.
A Framework for Real-World AI Adoption
To overcome the structural blockers limiting clinical AI scale, I applied a systems-based framework — mapping friction points to product modules that enhanced trust, usability, and enterprise integration.
From Friction to Function:
TRUST → Clinician confidence via explainable AI (Clinical Decision Support System)
TRAINING → Safe, repeatable skill-building environments (Simulation-Based XR Training)
DATA → Privacy-preserving, diverse training sets (AI/ML Platform)
DEPLOYMENT → Scalable, secure integration with enterprise systems (SDK/API Integration Platform)
This modular platform redefined clinical readiness for AI — advancing adoption by solving for the real-world barriers clinicians and health systems face.
A modular system designed to drive real-world AI adoption across clinical settings by solving for trust, training, data, and deployment.
USER & MARKET RESEARCH
We conducted 25+ interviews and contextual inquiries with clinicians, surgical residents, researchers, and hospital IT leaders.
The research revealed four key pain points blocking clinical AI adoption:
“I waste valuable time navigating disconnected systems just to access basic insights.”
“The AI needs to fit into my current workflow or I won’t use it.”
“I don’t understand what the diagnosis even means.”
“The patient summary helped reduce follow-up calls.”
🔹CLINICIANS
🔹SURGICAL RESIDENTS
“We train on mannequins and watch videos — but never practice how to interact with real AI tools.”
“The simulation felt real — I learned faster.”
“The XR interface taught me where to look and what to do.”
“I finally trusted the system, not because it was accurate, but because I could practice with it.”
“We don’t have enough data to build models, and the real data is locked behind red tape.”
“We’re blocked on privacy reviews for 4–6 months per project.”
“Being able to preview synthetic patient cases made it feel real.”
“Synthetic data gave us better generalization, without compromising patient privacy.”
🔹AI/ML RESEARCHERS
“We had no integration layer to plug the AI into our hospital systems.”
“This SDK structure saved our dev team months of setup.”
“Security, compliance, and infrastructure are non-negotiables for our IT group.”
🔹IT & DevOps
WHAT WE OBSERVED ACROSS ROLES
(& how each insight led to targeted product discovery)
Observation Mapped To Product
Clinicians experienced cognitive overload navigating fragmented systems
Patients were confused by unexplained diagnoses and medical language
Residents lacked realistic environments to practice AI-assisted procedures
Residents built more trust when AI was embedded in experiential training
Researchers lacked access to rare or diverse datasets for AI training
Synthetic Data Generator – GAN-based privacy-safe tools
IT and DevOps teams struggled to integrate external AI into secure systems
Dev teams needed sandboxed testing and better documentation
CDSS – Unified AI interface with EHR integration
CDSS – Patient summary with simplified language
Simulation Platform – VR + real-patient data
Simulation Platform – HUD feedback + AI rationale in sim
AI/ML Platform – GAN-based privacy-safe tools
AI/ML Platform – Differential privacy layers
IT Integration Platform – Secure integration tooling + test environments
IT Integration Platform – Developer portal with logs + onboarding
Each product originated from deep-rooted behavioral friction and unmet needs across domains.
ARTIFACTS - RESEARCH & PRODUCT DISCOVERY
FRUSTRATION FREQUENCY HEATMAP
JOBS TO BE DONE MATRIX
Persona
JTBD
Friction Point
Product Module
AI/ML Researcher (Lena Zhao)
When I build models, I want privacy-safe, real-feeling data
IRB delays, data gaps
AI/ML Platform
Resident
(Raj Mehta)
When I’m training, I want realistic practice with AI
No hands-on AI training
Simulation Platform
Clinician
(Dr. Emily Carter)
When I'm diagnosing, I want AI that fits my workflow and explains itself
Siloed systems, unclear outputs
CDSS
IT/DevOps
(Omar Rios)
When I deploy, I want secure, low-risk AI integrations
Integration friction, compliance
IT INTEGRATION PLATFORM
Developer
(Peter Tran)
When I evaluate AI, I want insight into usage + impact
Weak docs, no sandbox
IT INTEGRATION PLATFORM +
AI/ML PLATFORM
Clinical Educator (Kim Owens)
When onboarding clinicians, I want simulated AI decisions
Gaps in decision safety training
SIMULATION + CDSS
JOBS TO BE DONE ALIGNMENT BOARD
STRATEGY
A modular architecture with a flagship CDSS and three enabling product modules
PRODUCT 1 - Clinical Decision Support System (Core)
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Vision: Deliver real-time, explainable AI support embedded directly
within clinical workflows.
Goals:
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≥95% diagnostic accuracy
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↑ Clinician trust and decision confidence
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↓ Patient anxiety
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↑ Documentation speed
Strategic Pillars:
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Real-Time AI Diagnosis
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Explainability-First UX
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Clinical Workflow Integration
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Patient-Centered Summaries
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Security & Compliance
EXPERIENCE BREAKDOWN FLOW - BEFORE AI
EXPERIENCE BREAKDOWN FLOW - DISCOVERING AI-FIRST PRODUCT OPPORTUNITIES
North Star Product Experience Sketch
PRODUCT 2 - Simulation-Based Training (VR)
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Vision: Empower trainees and surgeons to safely build confidence in using AI through immersive simulation.
Goals:
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↓ Training time by 40%
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↑ Procedural accuracy
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↑ AI adoption via practice
Strategic Pillars:
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Scenario-Based VR Training
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AI-Assisted Procedural Prompts
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DICOM-Based Realism
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Feedback & Performance Analytics
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Credentialing Alignment
PRODUCT 3 - AI/ML PLATFORM
Vision: Enable creation of privacy-preserving datasets for robust, fair AI training.
Goals:
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↓ Model bias by 30%
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↑ Research speed
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↓ Compliance delays
Strategic Pillars:
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GAN-Based Synthetic Generation
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Differential Privacy
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Dataset Customization UI
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Rare Condition Simulation
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Research Enablement
PRODUCT 4 - IT INTEGRATION PLATFORM
Vision:
Equip IT teams with secure, compliant SDKs to easily integrate and manage clinical AI products.
Goals:
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↓ Time to integrate
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↑ Developer self-service
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↑ Compliance and logging
Strategic Pillars:
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Dev Portal
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FHIR/HL7 API Support
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Sandbox Environment
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Logging & Role-Based Access
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Compliance Reporting
STRATEGY MATRIX
Module Vision Summary Key Goals Strategic Pillars
CDSS Explainable, real-time AI ↑ Accuracy, ↓ Anxiety Trust, UX, Compliance
Simulation Practice AI in VR ↓ Time, ↑ Confidence Realism, Feedback, Credentialing
Synthetic Generate privacy-safe data ↓ Bias, ↑ Coverage GAN, Privacy, Dataset UX
SDK/API Deploy AI with dev tools ↓ Integration Time, ↑ Uptime Testing, Logs, Compliance
ROADMAP
Quarter | Key Initiatives
Q1: Workflow mapping, CDSS UX prototyping
Q2: Simulation MVP with DICOM + Unity
Q3: Synthetic data UI and GAN engine
Q4: API console and FHIR-ready SDK launch
PRODUCT 1 - CDSS Clinical Decision Support System
FOR PHYSICIANS & PATIENTS
RESEARCH & TESTING METHODS:
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Observational studies inside hospital diagnostic rooms
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Think-aloud usability testing with 8 physicians
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Iterative UI prototyping of explanations, confidence indicators, and patient summaries
KEY INSIGHTS:
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“The confidence bar helped me trust the system, but I still needed to know why.”
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“If I’m giving this to a patient, it has to be clear, not just accurate.”
PRODUCT RESPONSE:
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Designed trust-building confidence indicators with explanations
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Added rationale layering for transparency and compliance
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Built printable patient-facing summaries directly from the diagnostic UI
IDEATION
MVP
PRODUCT 2 - SURGICAL SIMULATION & PLANNING XR/VR
FOR SURGEONS, MEDICAL STUDENTS, CLINICAL EDUCATORS
RESEARCH & TESTING METHODS:
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VR-based simulation trials using real anonymized patient data
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Hands-on HUD flow walkthroughs in immersive environments
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Post-session interviews and scoring analysis
KEY INSIGHTS:
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“Simulation wasn’t a bonus — it was the bridge between the model and the surgeon.”
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“I perform better when the system gives me timely, non-intrusive feedback.”
PRODUCT RESPONSE:
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Designed immersive simulation loop: Select → Perform → Score → Learn
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Integrated HUD overlays for performance metrics and feedback
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Built responsive feedback modules to reinforce real-time learning
IDEATION
MVP
PRODUCT 3 - AI/ML DEVELOPMENT PLATFORM
FOR DATA SCIENTISTS, PRIVACY OFFICERS, QA TEAMS
RESEARCH & TESTING METHODS:
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Design workshops with researchers and QA leads
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Prototyped toggles for anonymization layers and test population builders
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Pre/post utility tests comparing real vs. synthetic output behavior
KEY INSIGHTS:
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“It finally feels like we can test AI the way we test humans.”
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“Control is key — if I can’t tune it, I can’t trust it.”
PRODUCT RESPONSE:
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Integrated AI/ML Developer Platform with an intuitive UI providing AI/ML Developers access to a single venue for:
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AI model training/testing (python IDE)
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Data engineering (SQL)
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Tickets to address (translating feedback from clinicians into technical requirements)
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Selecting Compute Environment and Resources (VM- Virtual Machines)
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MLOps to track Model Health (Accuracy, Patient Safety)
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Generative AI tool to generate and integrate synthetic data for rare diseases and edge cases for whom data isn't available
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Aligning AI/ML developers' backend efforts with frontend user experience (healthcare professionals) by displaying front end tabs to AI/ML developers so that they can see the impact of their work on the end-users before committing/deploying changes
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MVP
PRODUCT 4 - IT INTEGRATION PLATFORM
RESEARCH & TESTING METHODS:
Code walkthroughs with in-house and partner engineering teams
Dev onboarding journey mapping
Sandbox testing with usage logs and deployment speed benchmarks
KEY INSIGHTS:
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“We deployed CDSS in half the time once we used the sandbox.”
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“Real-time logs gave us confidence to roll out updates on Friday.”
PRODUCT RESPONSE
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A fully-documented SDK/API Suite with sandbox environments
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FHIR/HL7-ready integrations and role-based access control
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Real-time logs, compliance dashboards, and faster go-live cycles
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Included visual log viewers for debugging and version tracking
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Documented modular integration patterns for faster deployment
MVP
IMPACT
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Diagnostic Accuracy↑ to 95%
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Training Time ↓ by 40%
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Model Bias ↓ by 30%
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Integration Time ↓ from 24 to 6 months
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Patient Anxiety ↓ 50% via summary redesign
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Documentation Speed ↑ 28% with AI-supported UI
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Clinician Confidence ↑ 2x post-pilot
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Hospital Deployments - 8 enterprise integrations in 12 months
REFLECTION
We didn’t just build a product - We removed the blockers that made adoption impossible
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Trust → Explainable CDSS
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Training → Simulation
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Data → Synthetic
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Integration → SDK
WHAT'S NEXT
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Multi-user VR training
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CDSS voice UX for mobile
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Open synthetic dataset for research
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Integration with robotic surgery
