Radiology in Latvia is caught in a paradox: the diagnostic equipment is ready, but the specialists to run it are stretched to their limits. State and municipal hospitals face a critical shortage of radiologists and radiologist assistants. This shortage reaches patients directly — average waiting times for state-funded MRI or CT scans range from a single week to 200 days or more.
At its core this is a double crisis: a shrinking workforce coupled with a rising caseload. Many specialists face severe burnout, driven by uncompensated hours, heavy administrative work and constant interruptions, and a large share of Latvia’s practising radiologists are already at or near retirement age. Together, these pressures raise the risk of diagnostic errors. With no quick fix for the workforce deficit, artificial intelligence (AI) can no longer be seen as a mere innovation — it is becoming vital infrastructure for scaling capacity and safeguarding diagnostic safety.
A “third pair of eyes” and smarter prioritisation
Solving the shortage requires pairing clinical expertise with the processing power of AI. Around the world, certified AI solutions already act as a tireless “third pair of eyes” for physicians, holding their accuracy even under heavy workloads. The AI tools of Gleamer, a leading French medical-technology company, process more than 40 million examinations a year, and these technologies are now being deployed in Latvia’s leading hospitals through their official Baltic partner, Datamed.
AI does more than help read X-rays, CT and MRI scans; it also streamlines daily workflow, sharply improving clinical efficiency. In practice, this changes care on the ground:
- The system automatically prioritises scans by potential pathology risk, so critical cases move to the front of the queue.
- It acts as a safety net, helping doctors catch easily missed details such as hairline fractures or subtle chest abnormalities.
- AI-assisted reporting drafts medical reports, saving an average of 20–40% of time per examination — the radiologist reviews and approves a generated draft rather than writing from scratch, getting patients into treatment faster.
Why an algorithm alone is not enough
The initial hype around AI algorithms has given way to a strict demand for transparent governance. Under the European Union AI Act, diagnostic tools are classed as “high-risk,” a designation that requires not only technical accuracy but also legal and ethical accountability.
In radiology, the real value of an AI tool goes beyond analysing images: what matters most is a secure, dependable system that physicians and patients can trust. That is why deployment should be handled exclusively by a certified partner. The internationally recognised ISO 42001 certification demonstrates that a company’s AI management system meets structured requirements for the responsible use of AI in clinical settings, including robust risk management and supplier oversight.
The most successful AI projects in Latvian hospitals are built on the infrastructure of certified partners, shielding institutions from the uncertainty and legal risk of unmanaged technology. The long-term sustainability of radiology depends on letting AI take over routine processing so physicians can focus on critical decisions. In 2026 the question is no longer what the technology can do, but whether the industry is ready to adopt it responsibly — so that every patient receives an accurate diagnosis without waiting for months.