Qualisera AI is a proprietary quality engineering platform for healthcare AI. It validates data and API behaviors with synthetic and public datasets in a controlled QA workflow.
One test run, with dataset-specific scenarios reported separately from the full automated suite.
Those are the dataset-focused cases. The other 13 tests cover supporting API, dashboard, and loader behavior; all 113 tests are included in the pass/fail totals.
These are software quality checks against test data. They are not clinical validation, medical advice, regulatory certification, or evidence of production readiness.
Hospitals want to deploy AI, but critical roadblocks prevent safe adoption.
Healthcare AI teams need repeatable ways to test data and software behavior while managing sensitive information responsibly.
Changes in data and operating conditions can affect model behavior, making ongoing evaluation and monitoring important.
Healthcare systems rely on consistent data formats and dependable interfaces across applications and services.
A proprietary QA workflow for healthcare datasets and API behavior, using synthetic Synthea data alongside FHIR resources and public NIH chest X-ray metadata.
Includes Synthea patient records and FHIR resource checks to exercise common healthcare data workflows in a test environment.
Automated checks cover required fields, unique identifiers, date consistency, supported values, and selected vital-sign data-quality ranges.
Pytest verifies API responses and dataset-loader behavior. The current suite is a QA prototype, not a deployed drift-monitoring service.
A modular workflow for exercising healthcare test data and API behaviors. Each component can be evaluated independently.
Synthea™ generates HL7 FHIR synthetic patient records.
Python validators check schema, identifiers, dates, labels, and selected data-quality rules.
FastAPI tests the AI model's predictions and latency.
Document results and add monitoring integrations as the workflow evolves.
Healthcare AI depends on dependable inputs, interfaces, and evaluation processes. Qualisera AI is a proprietary prototype that brings selected quality checks together in a reviewable workflow.
Moumita Ghosh is a seasoned Quality Assurance Engineer II with 10+ years of experience specializing in API testing (REST & GraphQL), mobile testing (iOS/Android), and automated testing frameworks. She holds an MS in Computer and Information Systems from Kean University and a BTech in Computer Science.
With deep expertise in Postman automation, SQL/PostgreSQL database validation, and CI/CD pipelines, Moumita has delivered high-quality software across healthcare, e-commerce, e-learning, and retail industries. She is AWS Certified Cloud Practitioner and holds advanced certifications in Python programming and database systems from Stanford.
Moumita created Qualisera AI to apply established software quality practices to healthcare data and AI workflows, with a focus on clear, repeatable validation and transparent test results.