01.
Requirement Gathering
At the dawn of development, we gather requirements from stakeholders, including healthcare providers,
administrators, and end-users. This involves understanding their needs, pain points, and objectives for
implementing AI in healthcare. Additionally, the Devox team reconciles the tech stack, team size,
milestones, and KPIs.
02.
Data Collection and Preparation
Next, our engineers identify and collect relevant data sources such as electronic health records (EHRs),
medical imaging databases, genomic information, readings from wearable devices, and clinical trial data.
The data is then cleaned, normalized, and preprocessed to ensure quality and consistency.
03.
Model Development
With a clean dataset in hand, we develop solution healthcare using machine learning, deep learning, or
other AI techniques, depending on the specific use case. This involves selecting appropriate algorithms,
feature engineering, and training the models using labeled datasets.
04.
Validation and Testing
We test the developed AI models rigorously and validate them using independent datasets to assess their
performance, accuracy, and generalizability. This ensures that the models are reliable and robust enough
for real-world deployment.
05.
Integration with Healthcare Systems
Once validated, we integrate the AI models into existing healthcare systems, such as electronic health
record (EHR) systems, imaging platforms, or telemedicine platforms. To reach this, the Devox team develops
APIs, interfaces, or custom integrations to enable seamless data exchange and interaction.
06.
Deployment and Implementation
Finally, we deploy the application into the production environment and implement it within healthcare
settings, pilot testing it in specific departments or clinics before full-scale deployment across your
organization. Our team ensures harmonious adoption, integrating AI technology into clinical workflows and
training your workers.
07.
Monitoring and Maintenance
After deployment, we continuously monitor the application to ensure it performs as expected and delivers
the intended benefits, monitoring model performance, data quality, and user feedback, as well as making
updates or improvements as needed. We assess its impact on clinical outcomes, optimizing and refining them
based on feedback and performance metrics to further improve their effectiveness and usability.