I want to make an AI guided experience through a web UI for researchers to walk through the analytical process to include agents capable of doing things like choosing and applying analytical techniques throughout the process. I’d eventually like for each user to have their own list of in process “analysis in progress” and “assessments being authored” after their analysis is done.
I would appreciate hearing everyone’s thoughts as it relates to UI, credential handling, state management for each individual analysis in progress and assessment being authored. I’d like users to be able to intuitively go backwards between phases of analysis/authoring but “unlock” future phases as they progress.
Think of the overall app as a guided AI experience for a new medical analyst at a research institute which has a very particular writing style and industry requirements for justifications and such.
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Also, I have a personal budget to pay for assistance/lead on building this out!
If I were you I’d make another post with this in the title, definitely would get more traction!
Let me get this straight. Is this what you mean?
AI-Guided Medical Analysis Platform Architecture
Core Concept: A web-based platform guiding medical researchers through structured analytical workflows with AI assistance at each stage.
UI Design Considerations
- Progressive Disclosure Interface
- Present only relevant controls for the current analytical phase
- Use visual indicators (breadcrumbs, progress bars) to show position in workflow
- Implement a collapsible sidebar showing completed, current, and locked future phases
- Workspace Organization
- Dashboard with “Analysis in Progress” and “Assessments Being Authored” sections
- Card-based UI for each project with status indicators and last-edited timestamps
- Split-view option showing analytical data alongside writing interface
- AI Interaction Elements
- Contextual AI suggestion panels that don’t interrupt workflow
- Option to expand AI reasoning for transparency in recommendations
- Interactive elements allowing researchers to modify/guide AI recommendations
Credential & Security Architecture
- Role-Based Access Control
- Tiered permissions for researchers, reviewers, and administrators
- Audit logging for compliance with medical research standards
- Fine-grained access controls for patient data
- Authentication Options
- SSO integration with research institution’s existing systems
- MFA for sensitive medical data access
- Session management with appropriate timeouts for medical context
State Management
- Project State Architecture
- Database schema with projects → analysis sessions → phases → artifacts
- Versioning system for both analysis attempts and document drafts
- Metadata tracking for AI interventions vs. researcher decisions
- Synchronization Strategy
- Optimistic UI updates with background syncing
- Conflict resolution protocols for collaborative editing
- Local storage backup for unstable connections
- Progress Tracking
- Completion criteria for each analytical phase stored as workflow rules
- “Checkpoint” system allowing validated backtracking without losing progress
- AI validation of completed phases before unlocking subsequent steps
Implementation Considerations
- Tech Stack Options
- Frontend: React/Next.js with Tailwind for medical-appropriate UI
- Backend: Node.js/Python with specialized medical analysis libraries
- Database: PostgreSQL with proper schemas for medical data compliance
- AI Integration Approaches
- LLM for writing assistance and style conformance
- Specialized medical analysis models for technique selection
- Human-in-the-loop validation points for critical decisions
- Compliance Features
- HIPAA-compliant data handling
- Explainability features for AI recommendations
- Citation and reference management meeting medical standards
Phased Development Plan
- Basic workflow engine with manual transitions
- AI assistance for individual analytical steps
- Full project lifecycle management
- Collaborative features for team-based analysis