Alix Bird

Alix Bird

Higher Degree by Research Candidate

School of Public Health

Faculty of Health and Medical Sciences


Dr Alix Bird is a medical doctor and PhD candidate, working in the field of medical artificial intelligence. Their research aims to develop a scalable method of assessing rheumatoid arthritis severity, to aid in early diagnosis, the evaluation of drug efficacy in clinical trials, and decisions regarding therapy escalation.

Alix completed a Bachelor of Medicine and Surgery at the University of Adelaide and worked as a junior doctor at the Royal Adelaide Hospital before moving into research. 

Their supervisors are Professor Lyle Palmer, Professor Susanna Proudman and Dr Lauren Oakden-Rayner.

  • Journals

    Year Citation
    2024 Bird, A., McMaster, C., & Liew, D. (2024). Clinical evaluation is critical for the implementation of artificial intelligence in health care: comment on the article by Mickley et al. Arthritis Care and Research, 76(6), 904.
    DOI
    2024 Bird, A., Oakden-Rayner, L., Smith, L. A., Zeng, M., Ray, S., Proudman, S., & Palmer, L. J. (2024). Prognostic modeling in early rheumatoid arthritis: reconsidering the predictive role of disease activity scores. Clinical Rheumatology, 43(5), 1503-1512.
    DOI
    2023 Bird, A., Zavaletta, V., Carroll, E. F., McGinnis, H., Newsome, J., Gichoya, J., & Oakden-Rayner, L. (2023). Fostering an inclusive workplace for LGBTQIA+ people in radiology and radiation oncology. Journal of Medical Imaging and Radiation Oncology, 67(2), 193-199.
    DOI Scopus1 WoS1
    2023 Smith, L. A., Oakden-Rayner, L., Bird, A., Zeng, M., To, M. S., Mukherjee, S., & Palmer, L. J. (2023). Machine learning and deep learning predictive models for long-term prognosis in patients with chronic obstructive pulmonary disease: a systematic review and meta-analysis. The Lancet Digital Health, 5(12), e872-e881.
    DOI Scopus1
    2022 McMaster, C., Bird, A., Liew, D. F. L., Buchanan, R. R., Owen, C. E., Chapman, W. W., & Pires, D. E. V. (2022). Artificial Intelligence and Deep Learning for Rheumatologists. Arthritis and Rheumatology, 74(12), 1893-1905.
    DOI Scopus25 WoS11 Europe PMC13
    2022 Zeng, M., Oakden-Rayner, L., Bird, A., Smith, L., Wu, Z., Scroop, R., . . . Palmer, L. J. (2022). Pre-thrombectomy prognostic prediction of large-vessel ischemic stroke using machine learning: A systematic review and meta-analysis. Frontiers in Neurology, 13, 14 pages.
    DOI Scopus6 WoS3
    2022 Bird, A., Oakden-Rayner, L., McMaster, C., Smith, L. A., Zeng, M., Wechalekar, M. D., . . . Palmer, L. J. (2022). Artificial intelligence and the future of radiographic scoring in rheumatoid arthritis: a viewpoint. Arthritis Research & Therapy, 24(1), 1-10.
    DOI Scopus11 WoS7 Europe PMC4
  • Conference Papers

    Year Citation
    2023 Zeng, M., Xie, Y., To, M. S., Oakden-Rayner, L., Whitbread, L., Bacchi, S., . . . Jenkinson, M. (2023). Improved Flexibility and Interpretability of Large Vessel Stroke Prognostication Using Image Synthesis and Multi-task Learning. In H. Greenspan, A. Madabhushi, P. Mousavi, S. Salcudean, J. Duncan, T. Syeda-Mahmood, & R. Taylor (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 14224 LNCS (pp. 696-705). CANADA, Vancouver: SPRINGER INTERNATIONAL PUBLISHING AG.
    DOI

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