Longer lifespans have brought more chronic conditions, such as cancer and cardiovascular disease, and with them more diagnostic imaging. Over 80 million CT scans are performed each year in the United States alone, and the US is the largest market for medical imaging software. More scans mean more images to read, label and store. This article covers where AI helps, the hidden costs of manual image review, how the main approaches compare, and how to keep clinicians in control.
What is medical imaging?
Medical imaging creates visual representations of the body for clinical analysis. Techniques include CT, ultrasound, X-ray and MRI, producing images from 2D up to 4D, where time is the fourth dimension.
Where software and AI help
Image capture and storage. Software converts images from scanning equipment to DICOM, the standard for storing and exchanging medical images, and keeps them in a central, backed-up archive.
Annotation and labeling. Clinical experts label images as normal or abnormal and classify findings. Software guides them through labeling step by step and keeps the results for later use, including training AI models.
Interpretation support. Image enhancement, segmentation and texture analysis help spot tumors, fractures and other findings, especially when images lack clarity.
De-identification. Software removes demographic, insurance and billing data from images so they can be shared safely with other specialists.
Triage and worklists. Tools prioritize urgent cases, organize work and make sharing records between clinics easier.
The hidden costs of manual image review
The radiologist's time is the visible cost. The hidden costs add up around it:
- Backlogs. Urgent findings wait in the same queue as routine scans.
- Inconsistent labels. Different reviewers describe the same finding differently, which weakens records and any future AI training data.
- Manual de-identification. Removing patient data by hand is slow and error-prone, and a miss is a privacy incident.
- Scattered archives. Images stored in different systems are hard to find and compare over time.
- Fatigue. High volumes increase the risk of missed findings.
Manual, rule-based and AI-assisted review compared
- Manual review: consistent only within one reviewer; slowest with high volumes; no training data needed; fully explainable; limited by staff capacity.
- Rule-based tools: fast for well-defined checks; consistent; no training data needed; easy to explain; weak on complex or unusual images.
- AI-assisted review: fast at scale; consistent once validated; needs large sets of expert-labeled images; less transparent, so results need clinical review; suited to triage, detection and measurement.
Keeping clinicians in control
AI in imaging works best as support, not replacement. Models must be validated against expert-labeled data, monitored after release, and built so a clinician makes the final call.
From our work: VARTEQ engineers built the back end of an AI tool that interprets genetic test data for GeneDx, part of the work we have done on its genetic testing platform since 2018. The same principles apply: reliable data pipelines, clear results for specialists, and decisions that stay with clinicians. Read the GeneDx case study.
Frequently asked questions
Can AI replace radiologists?
No. It helps prioritize and review images; diagnosis stays with clinicians.
How much data does a medical imaging model need?
Usually thousands of expert-labeled examples, depending on the task.
How is a clinical AI model validated?
Against expert-labeled data before release, then monitored on real cases after it goes live.
Final thoughts
Medical imaging software removes tedious manual work, reduces errors and gets urgent results to doctors faster. When off-the-shelf tools don't fit a clinic's workflow, custom tools built with an experienced partner can. VARTEQ provides AI development and software for healthcare. Read also: How Automation and IoT Are Transforming Healthcare. Contact us to discuss your project.
