Most discussion of radiology AI focuses on detection—AI that finds things in images. Nodules, fractures, haemorrhages. This makes sense; detection was the first wave of radiology AI and remains the most deployed category.

But detection is just the beginning. The next wave of radiology AI does more: quantification, characterisation, workflow optimisation, and reporting assistance. Some of these applications may ultimately be more impactful than detection.

The Detection Foundation

Detection AI has become fairly mature in several areas:

• Chest X-ray abnormality detection and triage
• Mammography computer-aided detection (CAD)
• CT pulmonary embolism detection
• Brain haemorrhage identification

In major Australian radiology groups and public health networks, these applications are increasingly deployed in production. They’re not universal, but they’re no longer experimental.

Detection AI typically works as a second reader or triage tool, flagging potential findings for radiologist attention. The AI doesn’t diagnose—it highlights areas for human review.

This is valuable but limited. The radiologist still does most of the work: confirming findings, characterising them, measuring them, comparing to prior studies, synthesising with clinical context, and communicating results.

Quantification AI

Quantification AI goes beyond “something is there” to “here’s how much”:

Volumetric measurement. AI that measures lesion volumes over time, tracking changes with precision that manual measurement can’t match. Particularly useful for treatment response assessment.

Organ segmentation. Automatic identification and measurement of organ volumes. Liver volume for surgical planning. Brain atrophy for dementia assessment. Cardiac chamber volumes for function assessment.

Texture and composition analysis. Characterising tissue composition—fat content, fibrosis scoring, calcium quantification—in ways that inform diagnosis and management.

Biomarker extraction. Deriving quantitative markers from imaging that predict outcomes or guide treatment. Coronary calcium scores are a simple example; more sophisticated radiomics approaches are emerging.

Quantification AI addresses a real clinical need. Manual measurement is time-consuming, operator-dependent, and prone to variability. Automated quantification is faster, more consistent, and can detect subtle changes that humans miss.

The clinical impact is potentially significant. Better treatment response assessment means earlier switching of ineffective therapies. More precise surgical planning reduces complications. Consistent quantitative monitoring improves care pathways.

Workflow Optimisation AI

Beyond image analysis, AI is being applied to radiology workflow:

Intelligent worklist prioritisation. AI that analyses incoming studies and prioritises the worklist based on clinical urgency, not just arrival order. Critical findings get reviewed faster.

Protocol optimisation. AI that suggests optimal imaging protocols based on clinical indication, patient history, and prior imaging. Reduces inappropriate studies and improves yield.

Hanging protocols. Automated selection and arrangement of prior studies for comparison. AI that understands which comparisons are clinically relevant and presents them optimally.

Schedule optimisation. AI that optimises appointment scheduling, equipment utilisation, and staff allocation. Not image analysis but operational efficiency.

These applications address the radiologist experience as well as patient outcomes. Radiologist burnout and workload are significant problems. AI that makes work more efficient and less frustrating has value beyond direct clinical impact.

Reporting Assistance

AI is increasingly being applied to radiology reporting:

Structured reporting. AI that extracts structured data from free-text reports, or assists in creating structured reports. This enables better data use for research, audit, and decision support.

Report generation. AI that drafts radiology reports based on image analysis and clinical context. The radiologist reviews and modifies rather than creating from scratch.

Error checking. AI that reviews draft reports for inconsistencies, missed findings, or discrepancies with the images. A safety layer before reports are finalised.

Natural language processing for prior reports. AI that extracts key information from prior radiology and clinical reports to inform current interpretation.

Microsoft’s DAX and similar products are bringing ambient documentation to radiology, though adoption is earlier than in other specialties. The potential for significant time savings is clear, though accuracy and liability questions remain.

What’s Actually Deployed in Australia

Of these applications, what’s actually in clinical use in Australian radiology?

Detection AI: Increasingly common. Multiple products deployed in major practices and health networks.

Quantification AI: Earlier stage. Some deployment in specialised applications (cardiac, oncology) but not yet widespread.

Workflow AI: Emerging. Some worklist prioritisation deployed. Protocol optimisation being piloted.

Reporting AI: Nascent. Limited deployment. More experimentation than production.

The maturity curve is detection → quantification → workflow → reporting. Most Australian organisations are somewhere in the detection to early quantification phase. AI consultants Sydney working with radiology groups report that quantification is the area of most interest for the next wave of implementations.

Barriers to Broader Adoption

Several factors slow adoption of beyond-detection AI:

Integration complexity. These applications require deeper integration with RIS (radiology information systems), PACS, and clinical workflows than simple detection tools.

Evidence gaps. While detection AI has substantial validation literature, evidence for workflow and reporting AI is thinner.

Regulatory uncertainty. Some quantification applications have regulatory implications. If quantitative measurements inform clinical decisions, regulatory requirements may apply.

Workflow disruption. Changing how radiologists work is harder than adding a tool they can check or ignore. Workflow AI requires more change management.

Vendor landscape fragmentation. Many vendors focus on detection. Fewer offer sophisticated quantification, workflow, or reporting tools. The market is less developed.

Clinical Value Considerations

When evaluating beyond-detection AI, consider:

Does it address a real bottleneck? AI that saves time on activities that aren’t bottlenecks doesn’t help much. Identify where radiologist time and cognitive effort are actually consumed.

What’s the accuracy requirement? Detection AI that misses some findings is supplemented by radiologist review. Quantification AI that produces inaccurate measurements might not be corrected. Different applications have different accuracy needs.

How does it integrate? Tools that require separate logins, different screens, or workflow interruption get used less. Seamless integration matters more for workflow tools than detection tools.

What’s the total cost? Including integration, training, workflow redesign, and ongoing maintenance—not just licensing. Complex tools cost more to operationalise.

Looking Forward

My expectations for radiology AI evolution:

Next 1-2 years: Quantification AI becomes more common in oncology, cardiac, and neuro imaging. Worklist prioritisation sees broader deployment.

2-4 years: Integrated platforms emerge that combine detection, quantification, and workflow tools. Reporting assistance becomes more mature.

5+ years: AI becomes invisible infrastructure in radiology, not a distinct category. Every part of the workflow is AI-augmented without being AI-dependent.

For organisations planning radiology AI strategy, thinking beyond detection matters. AI consultants Melbourne and radiology AI specialists can help assess which emerging applications fit specific practice contexts.

Recommendations for Radiology Leaders

Start with detection if you haven’t. The foundational applications have the most evidence and clearest value propositions.

Explore quantification for high-volume specialised areas. Oncology response assessment, cardiac function, liver disease—areas with significant quantification workload.

Pilot workflow optimisation. Worklist prioritisation is relatively low-risk and can meaningfully improve radiologist experience.

Watch reporting AI closely. This is the space with most potential for dramatic time savings, but also the most immature.

Build evaluation capability. As more AI options emerge, you’ll need systematic approaches to assess them. Not every shiny new tool is worth deploying.

Radiology AI is maturing from a narrow focus on detection to a broader reimagining of how radiology work is done. Detection was the proof of concept. The coming years will show whether AI can transform radiology productivity and quality more comprehensively.

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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.