Artificial intelligence (AI) is often discussed in broad terms across primary care, but new NICE guidance on AI-supported spirometry brings the focus firmly onto respiratory diagnostics. The guidance explores how algorithm-supported interpretation of spirometry tests could support the diagnosis of asthma and COPD in primary care and community diagnostic centres, with NICE proposing that AI tools could assist GPs in interpreting results, a move highlighted in recent coverage from Pulse Today. 

Rather than replacing clinical judgement, these tools are intended to improve the accuracy and consistency of spirometry interpretation, particularly in settings where access to specialist input may be limited, helping to support earlier and more reliable diagnosis. 

The guidance explores how algorithm-supported interpretation of spirometry tests could support the diagnosis of asthma and COPD in primary care and community diagnostic centres. Rather than replacing clinical judgement, these tools are intended to improve the accuracy and consistency of spirometry interpretation, particularly where specialist input may not always be available. 

Spirometry remains a key diagnostic tool in respiratory care, yet its use can be variable. Interpretation requires training and experience, and access to high-quality testing is not always consistent across regions. NICE’s early-use assessment highlights how digital tools could help reduce this variation and support earlier, more reliable diagnosis. 

The introduction of AI-supported spirometry raises practical considerations. Clinicians will need clarity on how these tools fit into existing respiratory pathways, how outputs should be interpreted alongside clinical assessment, and where responsibility for decision-making sits. Confidence in using these systems will be as important as the technology itself. 

There are also system-level questions. The NICE guidance focuses on early use, meaning evidence will continue to develop as adoption increases. Data quality, interoperability and governance will all play a role in determining how effectively these tools can support consistent spirometry interpretation across different settings. 

This sits alongside a broader shift in how respiratory diagnostics are delivered. With the growth of community diagnostic centres and neighbourhood-based care, spirometry is increasingly being delivered closer to patients. AI-supported interpretation could help support this shift, particularly where access to experienced respiratory clinicians is limited, as discussed in ‘NICE evaluates AI-powered spirometry tools’. 

For primary care, this presents a need for balance. Tools that improve the consistency of spirometry interpretation are valuable, but they must integrate into existing workflows without adding unnecessary complexity or burden. 

As these tools begin to move into routine use, the focus is shifting towards practical application, how they support clinical decision-making within respiratory care, how they affect workload, and how they sit alongside existing expertise. These are not theoretical questions, but ones already being worked through in clinical settings. 

They will continue to shape discussions in the months ahead, including at Best Practice Birmingham 2026, where both the Smart Practice Theatre and the co-located Respiratory Professional Care programme will explore how tools like these are being applied in practice.