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.. Clin Rheumatol, 43(5), 1503-1512.
    DOI
    2024 Zeng, M., Smith, L., Bird, A., Trinh, V. Q. -N., Bacchi, S., Harvey, J., . . . Palmer, L. J. (2024). Predictions for functional outcome and mortality in acute ischaemic stroke following successful endovascular thrombectomy.. BMJ Neurol Open, 6(1), e000707.
    DOI Scopus1 Europe PMC1
    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 Europe PMC1
    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.. Lancet Digit Health, 5(12), e872-e881.
    DOI Scopus31 Europe PMC17
    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 Scopus43 WoS11 Europe PMC28
    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 Scopus11 WoS3 Europe PMC4
    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 Scopus18 WoS7 Europe PMC11

  • 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 Scopus2
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