SYNTERAResearch Group

Research / SYNTERA Health

AI for Health

  • Disease prediction
  • Medical imaging
  • Remote monitoring
  • Wearables

Why it matters

Health systems generate more data than clinicians can read. AI can turn images, signals and records into earlier, fairer and more personal care, provided it is trustworthy and fits clinical practice.

Key challenges

  • Data quality, bias and missing context in health records
  • Explaining model output to clinicians and patients
  • Privacy, consent and ethical use of sensitive data
  • Moving from prototype to everyday clinical workflow

Our approach

We combine machine learning, medical image and signal analysis, digital health and bibliometric methods, working with clinicians and health information researchers.

Publications

Selected publications

  1. Journal2026

    Beyond Coding: Balancing Interpretive Depth and Rigour in Ethnographic Data Analysis

    Nguyen, T. N. M.; Dermody, G.; Saunders, R.; Whitehead, L.

    Journal of Advanced Nursing

  2. Journal2026

    Beyond innovation: Reimagining inclusive and ethical technologies for ageing populations

    Gough, C.; Dermody, G.; Palmeira, A.

    Digital Health 12, 20552076261418907

  3. Journal2026

    Bridging the digital divide: A multi-method evaluation of nursing readiness for digital health technology

    Dermody, G.; Wadsworth, D.; El Haddad, M.; Prichard, R.; Benson, A.; Benson, T.; …

    Journal of Advanced Nursing, 82(4), 3752–3766

  4. Journal2026

    Electronic assistive technology use in the home (EAT-H) by people with spinal cord injury in Australia

    Verdonck, M.; Craven, D.; Kean, B.; Fowler, J.; Merollini, K.; Dermody, G.; Ripat, J.

    Disability and Rehabilitation: Assistive Technology, 1-17

  5. Preprint2026

    Intrusion detection for the Internet of Medical Things: A cost-aware comparative benchmark of machine learning approaches

    Ahmed, H.; Dermody, G.; Mateen, A.; Saremi, S.; Shibl, R.

    Under review

  6. Conference2026

    Mapping artificial intelligence and machine learning research in sports injury prediction: A bibliometric analysis

    Kamalpour, M.; Saremi, S.; Shibl, R.; Mirzaei, M.; Bedford, A.; Mealy, E.

    18th Australasian Conference on Mathematics and Computers in Sport

All SYNTERA Health papers →