Healthcare AI
AI in Medical Imaging in Vietnam: Opportunities and Prerequisites
AI in medical imaging refers to machine-learning algorithms that analyze X-ray, CT, MRI and mammography images to support physicians. AI does not replace doctors; it acts as a second reader that can reduce misses and save time.
Common use cases
- Triage and prioritization: move suspected critical cases to the top of the reading list.
- Lesion detection: highlight suspicious regions on chest X-ray, CT and mammography.
- Automated measurement: volumes, sizes and change over time.
- Report drafting support: suggested text that the physician confirms.
Prerequisites for hospital deployment
- DICOM digital data: a stable PACS.
- Digital workflow: AI results sit within the RIS-PACS workflow and display in the DICOM viewer.
- Open connectivity: PACS needs integration interfaces to attach multiple AI apps.
- Infrastructure and security: processing servers, access control and audit logs.
Challenges in Vietnam
- Imaging data is scattered and uneven in quality.
- Performance should be validated on Vietnamese patient data before adoption.
- Regulatory requirements for software as a medical device and personal data protection.
- Physicians need training to read AI output critically.
Where to start
Digitize the archive with PACS, standardize workflows, then pilot AI on one specific problem, measure results and scale. Saolasoft designs saolaPACS and saolaVIEW to be ready for advanced image-processing and AI applications. Contact us to discuss a roadmap.