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Centre Director Professor Enrico Coiera Translating AI technologies into health services Explore CHI’s research outputs

AI and digital technology for sustainable healthcare

The Centre for Health Informatics (CHI) has five research streams.

Read more about these streams, their projects and research teams involved.

AI for precision medicine

Advancing AI for precision medicine with a focus on improving cancer care.

This team leads a precision neuro-oncology research stream integrating artificial intelligence with multidisciplinary clinical expertise, including neurosurgery, pathology, radiology, and oncology.

We also explore cutting-edge AI applications in medical imaging, including the generation of synthetic PET and advanced MRI modalities. In parallel, we contribute to melanoma research.

Exemplar projects include:

  • early detection of melanoma
  • prediction of immunotherapy response in advanced-stage patients
  • development of AI-driven image analysis tools to support real-time, patient-specific precision treatment decisions using 3D oncology models.

Stream leader: Dr Sidong Liu

Team members

AI systems safety

To unlock the full potential of digital health and AI, we must address safety risks and integrate these technologies responsibly into care delivery.

Our research takes a cross-disciplinary approach to improving the safety of AI and digital health technologies. By combining theory, methodology and policy, we aim to influence real-world healthcare delivery and enhance patient outcomes.

We investigate how AI and digital health technologies can introduce safety risks, and work with stakeholders to co-design sociotechnical solutions and develop guidance for the safe use of AI. Our research also drives the development of AI-enabled tools to support the early detection and proactive management of emerging threats to patient safety.

Stream leader: Professor Farah Magrabi

Team members

Climate change and digital health

Climate change poses serious and growing risks to human health, exacerbating existing conditions, triggering new health events, and disrupting the operations of health systems. As extreme weather events become more frequent, the need for timely, effective health responses is critical.

We examine how digital health technologies can help consumers and healthcare systems adapt to climate-related challenges. We are focusing on core challenges, such as understanding consumer and system needs through exploring lived experiences.

Our approach involves co-designing digital solutions to support preparedness and response, ultimately creating efficient technologies tailored to meet users’ needs.

Stream leader: Professor Enrico Coiera

Team members

Consumer informatics

Focusing on those with the highest stake in our healthcare system, our research program investigates the impact, design, and science of information and communication technology (ICT) on consumers, patients and their carers.

We are passionate about understanding and improving the health of individuals through the use of digital technology, including artificial intelligence (AI).

We work closely with patients, consumers and multidisciplinary colleagues to develop innovative ideas and apply rigorous methods. We seek to test the boundaries of how digital technologies can improve our health.

Stream leader: Associate Professor Annie Lau

Team members
  • Walid Abdalla (Masters of Research)
  • Nida Afzal (PhD candidate)
  • Mayes Al Barak (PhD candidate)
  • Dr Tim Jackson (Postdoctoral Research Fellow)
  • Mehak Preet Kaur (Masters Public Health)
  • Dr Andrew Parsonson (PhD candidate)
  • Tamanna Jannat Promi (Masters Public Health)
  • Trijya Shrestha (Masters Public Health)
  • Saranjit Singh (PhD candidate)
  • Romy (Dan Khue) Tran (Masters Public Health)
  • Moomna Waheed (PhD candidate)
  • Kanesha Ward (PhD candidate)

Interactive clinical AI

This stream focuses on the use of artificial intelligence and machine learning to develop personalised predictions of diagnosis and care, and help clinicians interact effectively with Large Language Models.

The work undertaken by the stream can be partitioned into three research activities:

  1. How can AI and machine learning methods be applied to personalise healthcare?
  2. How can we improve interaction of people – both clinicians and customers – with clinical technologies?
  3. How can we integrate Large Language Models into the clinical workflows to ease the clinicians’ burden and improve patient care?

Stream leader: Professor Shlomo Berkovsky

Team members