How a digital health startup created an AI platform to diagnose vision axis deviations that cause chronic pain, with patient app and medical dashboard.
Slow and subjective manual diagnosis process was transformed into automated AI platform, enabling visual pattern analysis and real-time symptom correlation.
About the project
A digital health startup identified an opportunity to apply artificial intelligence to diagnose vision axis deviations that cause chronic muscle pain and migraines. The challenge was to create a platform that would allow remote data collection through mobile app, automated AI analysis and integration with electronic medical record systems, all in a multitenant architecture that could serve multiple clinics simultaneously.
The problem that motivated the project
Main challenges faced
- Slow and subjective manual diagnosis process to identify vision axis deviations
- Dependence on in-person exams limited access to patients in remote regions
- Lack of standardization between clinics made result comparison difficult
- Need for continuous symptom tracking for correlation with deviations
- Rigorous security and audit requirements for sensitive health data
How we solved the problem
We developed a complete platform with three main components: a mobile app for patients to collect sensor and symptom data, a medical dashboard with AI for pattern and visual deviation analysis, and a secure integration layer with electronic medical records. The multitenant architecture allowed multiple clinics to operate in isolation, while an immutable audit trail ensured compliance with health regulations.
Architecture Highlights
- Cross-platform mobile app for sensor and symptom data collection
- Medical dashboard with AI algorithms for visual pattern analysis and symptom correlation
- Multitenant architecture with complete data isolation between clinics
- Immutable audit trail for regulatory compliance
- Integration APIs with existing electronic medical record (EMR) systems
- Scalable cloud infrastructure with complete DevOps
The project journey
A structured and predictable process, with incremental deliveries and continuous validation.
Discovery and Validation
3 weeksWorkshops with medical professionals to understand current diagnosis process, scientific validation of AI approach and mapping of regulatory requirements.
Architecture Design
4 weeksMultitenant architecture design, AI algorithm definition, data modeling and patient and doctor experience design.
Pilot Implementation
12 weeksMVP development focusing on core features: mobile app, basic medical dashboard and integration with pilot clinic for validation.
Rollout and Integrations
8 weeksExpansion to multiple clinics, integration with different electronic medical record systems and AI algorithm refinement based on medical feedback.
Support and Evolution
OngoingContinuous monitoring, fine AI adjustments, new features based on feedback and preparation to scale to more clinics.
Results and metrics
Measurable impact of the project in numbers.
Concepts in 30 seconds
Important technical concepts explained simply and connected to the project.
AI applied to healthcare
Machine learning algorithms that identify patterns in medical data difficult to detect manually. In this project, analysis of vision axis deviations correlated with symptoms.
Multitenant Architecture
A single system serving multiple clinics with complete data isolation. Each clinic accesses only its patients, sharing infrastructure securely and economically.
Immutable audit trail
Permanent record of all system actions. Essential in healthcare for regulatory compliance and traceability of diagnoses and clinical decisions.
Medical record integration
Secure connection between platform and existing electronic medical record systems, enabling data flow without manual duplication.
Lessons and learnings
Reflections and recommendations based on the experience of this project.
AI in healthcare requires rigorous validation with medical professionals before any production deployment
Multitenant architecture from the start saves months of refactoring when new clients emerge
Integration with legacy health systems requires patience: formats and protocols vary greatly between clinics
Audit trail is not optional in healthtech — it's a compliance and trust requirement
Related solutions
Other solutions that can solve similar challenges.
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