Advancing early diagnosis and monitoring for chronic respiratory diseases

Written by:

Rod Hughes

Executive Medical Director, Early R&I, AstraZeneca

Marko Topalovic

CEO, ArtiQ

In order to transform respiratory care, we need to identify better, more targeted therapies and enable more patients to be diagnosed earlier. Supporting the development of accessible and reliable tests for detecting and monitoring factors such as declining lung function is key to achieving both of these goals.

Improving access to reliable respiratory testing

Closer monitoring of people living with respiratory diseases has the potential to accelerate the development and delivery of new medicines as well as improving the timely delivery of clinical care for patients. As such, tools that enable closer monitoring of respiratory diseases could transform care and improve quality of life for millions of people worldwide.

Currently, accurate testing can only be carried out in a clinical setting and is highly dependent on specialist expertise. Not only can this be challenging and inconvenient for patient but it can delay diagnosis and limits the potential for ongoing monitoring. For example, spirometry is a valuable tool for accurately assessing lung function, but it has several limitations:

  • Patients need to attend a clinic to be tested and many patients miss appointments due to inconvenience or reluctance to attend
  • Testing requires administration by a trained specialist
  • In clinical trials, data quality needs to be subjectively assessed by an expert

If patients could perform these tests for themselves at home it could improve the patient experience, while also enabling early diagnosis and closer disease monitoring and reducing demand on limited healthcare resources. It could also accelerate the delivery of new medicines by enabling closer monitoring of clinical trial participants and the use of novel endpoints.


AI-enabled home spirometry represents a transformative shift in respiratory healthcare, empowering individuals to monitor their lung function in the comfort of their own homes. Simultaneously, it provides physicians with the confidence that the data collection is not only robust but also risk-based monitored, ushering in a new era of strategic and personalized respiratory health management.

Marko Topalovic CEO, ArtiQ

Innovating in clinical trials to transform care

In partnership with ArtiQ we are investigating using artificial intelligence to allow patients to test lung function for themselves at home. If successful, this could allow closer monitoring of clinical trial participants while still ensuring the same standards of data quality. ArtiQ’s AI technology has been validated to be at least as accurate as humans in on-site spirometry quality control and we are working with Evinova, AstraZeneca’s health-tech business, to explore how this technology could be used at home. 


The collaboration uses AI in two ways. The first, ensures quality data collection by providing real-time feedback to patients each time they perform an at-home spirometry test. This means they can repeat the test if the results aren’t consistent, helping to ensure rapid and accurate data collection. The hope is that this could remove the need for tests to be administered by a trained specialist because the AI can help to ensure that patients do the test correctly every time.


Digital health solutions and artificial intelligence are becoming key to optimising clinical trial design and delivery. The real-time feedback on spirometry helps AstraZeneca to increase the reliability of our data, allowing us to improve patient care and drive healthcare transformation.

Rod Hughes Executive Medical Director, Early Respiratory and Immunology, AstraZeneca

The second approach replaces a process called over-reading, where a human expert manually reviews and assesses the consistency of all the data collected in a trial. Currently, this is a time-consuming process that can delay the confirmation of patient assessments and is based on subjective judgement calls. Our aim is that, in future, AI could identify spirometry fingerprints for each patient allowing inconsistent data to be identified and excluded from further analysis.

Together, these methods could remove the need for expert oversight of spirometry in clinical trials, enabling faster trials, more detailed data collection and the potential for at-home testing to develop into a key clinical tool for ongoing respiratory patient monitoring.

To find out more about our clinical trials, visit our clinical trials website, which has been created to increase knowledge and awareness of our trials and what it means to participate in them.



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Veeva ID: Z4-61214
Date of preparation: January 2024