Radiology is where diagnostic AI has made the most progress. The technology is genuinely useful now—not perfect, but useful. I’ve helped three health services implement radiology AI in the past eighteen months, and the lessons have been remarkably consistent.

This isn’t theoretical. It’s what actually works.

Before You Start: The Prerequisites

I’ve seen implementations fail before they began because basic prerequisites weren’t in place. Check these first:

PACS integration capability. Your radiology AI system needs to receive images from your PACS and send results back. This sounds obvious, but I’ve encountered PACS systems with APIs so outdated that integration required custom development. Know your PACS vendor’s integration pathway before you evaluate AI products.

Network infrastructure. Diagnostic AI is computationally intensive. Most systems run inference in the cloud or on dedicated on-premises hardware. Either way, you need reliable, low-latency connectivity. One regional health service I worked with discovered their network couldn’t handle the bandwidth requirements during peak hours. That’s an expensive surprise.

Clinical governance structure. Who signs off on AI-assisted diagnoses? How do you handle cases where AI and radiologist disagree? These aren’t technical questions, but you need answers before go-live.

Radiologist buy-in. This is the single biggest factor in success or failure. If your radiologists see AI as a threat or an annoyance, the implementation will struggle regardless of technical quality.

Step 1: Define Your Clinical Use Case (Specifically)

“We want AI for radiology” isn’t a use case. You need to be precise.

Good use cases I’ve seen succeed:

• Chest X-ray triage to identify urgent findings that need immediate radiologist attention
• CT head interpretation support for stroke detection in emergency departments
• Mammography screening to flag cases that warrant additional review
• Nodule tracking across longitudinal CT scans

Each of these has clear success criteria and measurable outcomes. “Using AI to improve radiology” has neither.

Pick one use case to start. Just one. You can expand later, but trying to implement multiple AI systems simultaneously is a recipe for failure.

Step 2: Vendor Evaluation Done Right

The radiology AI market is crowded. Some vendors have excellent products. Others have impressive demos and not much else.

Here’s how to separate them:

Ask for Australian clinical validation data. AI systems trained on US or European populations don’t always perform the same on Australian patients. Demographics, disease prevalence, and imaging protocols differ. Any vendor worth considering should be able to show you local validation studies.

Request TGA registration details. This should be straightforward—either they’re registered or they’re not. If they’re “in process,” get specifics about timeline and milestones.

Understand the deployment model. Cloud-based? On-premises? Hybrid? Each has implications for data sovereignty, latency, and cost. Australian health data going offshore creates additional governance requirements.

Talk to reference sites. Not the ones the vendor suggests—find your own. Reach out to your professional networks. Ask specifically about implementation challenges, not just outcomes.

Evaluate ongoing costs. The per-study licensing model is common. A system that looks affordable at your current volume might be expensive if volumes grow. Model out three-year costs under different scenarios.

Step 3: Technical Implementation

Assuming you’ve selected a vendor, here’s the technical implementation sequence that’s worked:

Phase 1: Shadow Mode (4-8 weeks)

The AI system processes images but results don’t go to clinicians. Instead, they’re captured for retrospective analysis. This serves two purposes: validating that the system works in your environment, and collecting baseline performance data.

During shadow mode, have your radiologists review a sample of AI outputs. Compare AI findings to their interpretation. This isn’t about catching every disagreement—it’s about building familiarity and identifying systematic issues.

Phase 2: Advisory Mode (8-12 weeks)

AI results become visible to radiologists, but as advisory information only. The radiologist retains full responsibility for interpretation. This is the standard model for most diagnostic AI currently deployed in Australia.

During advisory mode, track how radiologists use the AI. Are they ignoring it? Over-relying on it? Adjusting their workflow based on AI flags? This behavioural data is crucial for understanding actual clinical impact.

Phase 3: Workflow Integration (Ongoing)

Based on what you learn in advisory mode, refine how AI fits into clinical workflow. This might mean:

• Changing the order in which AI results appear
• Adjusting sensitivity thresholds based on clinical feedback
• Integrating AI flags into radiologist worklists for triage
• Developing specific protocols for AI-flagged cases

Step 4: Clinical Governance Framework

The TGA expects a clinical governance framework for AI medical devices. Here’s a practical structure:

AI Clinical Governance Committee. Monthly meetings reviewing AI system performance, incidents, and proposed changes. Membership should include radiologists, clinical informaticists, IT leadership, and quality/safety representation.

Performance Monitoring. Define metrics you’ll track: sensitivity, specificity, false positive rate, processing time, and any operational metrics relevant to your use case. Review these monthly at minimum.

Incident Reporting. When AI output contributes to a near-miss or adverse event, you need a clear reporting pathway. This should integrate with your existing clinical incident system.

Change Management. Any algorithm updates from the vendor should go through your governance process before deployment. This includes what the update changes, why, and any expected performance impact.

Step 5: Ongoing Optimisation

Implementation isn’t a project with an end date. It’s an ongoing program.

After six months of operation, conduct a formal review:

• Has AI achieved the outcomes you expected?
• How has workflow actually changed?
• What’s the financial impact (accounting for licensing, infrastructure, and any efficiency gains)?
• What do radiologists think about the system now versus at go-live?

Use this review to decide whether to expand (adding new use cases), optimise (adjusting the current implementation), or in rare cases, discontinue (if value isn’t materialising).

Common Pitfalls to Avoid

Pitfall 1: Treating AI as a technology project. This is a clinical transformation project that uses technology. Lead accordingly.

Pitfall 2: Expecting immediate productivity gains. Most implementations see an initial productivity dip as people learn new workflows. Benefits typically emerge at 6-12 months, not day one.

Pitfall 3: Ignoring radiologist concerns. If radiologists express concerns about AI accuracy or workflow impact, take them seriously. They’re usually right, and they’re the ones who will determine whether the implementation succeeds.

Pitfall 4: Under-investing in change management. Technical implementation is maybe 40% of the work. Change management, training, and governance are the other 60%.

The Bottom Line

Radiology AI implementation is achievable, but it requires methodical planning and realistic expectations. The technology is mature enough to deliver value in specific, well-defined use cases. But it’s not plug-and-play, and organisations that treat it as such will struggle.

Start small, prove value, then expand. That’s the pattern that works.

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Dr. Rebecca Liu is a health informatics specialist and former Chief Clinical Information Officer. She advises healthcare organisations on clinical AI strategy and implementation.