PhD Study : An evaluation of the impact of different types of AI on clinical decision making when reviewing medical images

Apply and key information  

Summary

AI is being used increasingly in image interpretation tasks. There are challenges for its optimal use in reporting environments. Human reliance on technology and bias can cause decision errors. Previous studies have demonstrated that trust issues exist amongst radiologists and radiographers in both over-reliance (automation bias) and reluctance in AI use for decision support.

AI feedback can be provided in many different forms, such as heat maps, AI binary, textual description, regions of interest, the AI quality (percentage) or the AI interpretation with clinical codes to allow for automated patient journey. These many forms of AI feedback exist and there remains a variation in the use and provision of the forms in clinical departments. Understanding which form of AI feedback proves most useful will promote good decision hygiene and the use of AI in the clinical setting.

This proposed study aims to determine the most useful/preferred format of AI feedback and to provide this to aid the image interpretation process. This aims to improve the integration of AI into the decision making process and to optimise the performance of reporting clinicians.

Aim: To explore the most useful forms of AI to aid the clinical decision making process.

Objectives of the research:

To determine the impact of EUI and the type of AI information provided  on

-the decision switching and thought processes.

-the accuracy and automation bias.

-the user performance and confidence of prediction.

To investigate the user preference of AI type and explanation user interface (EUI).

To co-design, develop and test the use of an EUI. Methods:

Participants will be asked to interpret images with the assistance of AI feedback to determine its use in the decision making process. Accuracy in diagnosis, confidence, user preference and decision switching will be investigated to determine the impact of the AI feedback.

AccessNI clearance required

Please note, the successful candidate will be required to obtain AccessNI clearance prior to registration due to the nature of the project.

Essential criteria

Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.

We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.

In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.

  • Experience using research methods or other approaches relevant to the subject domain
  • A demonstrable interest in the research area associated with the studentship

Desirable Criteria

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

  • Completion of Masters at a level equivalent to commendation or distinction at Ulster
  • Practice-based research experience and/or dissemination
  • Experience using research methods or other approaches relevant to the subject domain
  • Relevant professional qualification and/or a Degree in a Health or Health related area

Funding and eligibility

The University offers the following levels of support:

Vice Chancellors Research Studentship (VCRS)

The following scholarship options are available to applicants worldwide:

  • Full Award: (full-time tuition fees + £19,000 (tbc))
  • Part Award: (full-time tuition fees + £9,500)
  • Fees Only Award: (full-time tuition fees)

These scholarships will cover full-time PhD tuition fees for three years (subject to satisfactory academic performance) and will provide a £900 per annum research training support grant (RTSG) to help support the PhD researcher.

Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Please note: you will automatically be entered into the competition for the Full Award, unless you state otherwise in your application.

Department for the Economy (DFE)

The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £19,000 (tbc) per annum for three years (subject to satisfactory academic performance).

This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.

  • Candidates with pre-settled or settled status under the EU Settlement Scheme, who also satisfy a three year residency requirement in the UK prior to the start of the course for which a Studentship is held MAY receive a Studentship covering fees and maintenance.
  • Republic of Ireland (ROI) nationals who satisfy three years’ residency in the UK prior to the start of the course MAY receive a Studentship covering fees and maintenance (ROI nationals don’t need to have pre-settled or settled status under the EU Settlement Scheme to qualify).
  • Other non-ROI EU applicants are ‘International’ are not eligible for this source of funding.
  • Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Due consideration should be given to financing your studies. Further information on cost of living

Recommended reading

Badgeley M..A., Zech, J.R, Oakden-Rayner, L., Glicksberg, B.S, Liu, M., Gale, W., McConnell M.V., Percha, B., Snyder,T.M., Dudley, J.T (2019) Deep learning predicts hip fracture using confounding patient and healthcare variables. NPJ Digital Medicine. Available at: https://www.nature.com/articles/s41746-019-0105-1.pdf [Accessed 17th October 2022].

Cheng, C.T., Chou, C., Liao, C.H., Chung, I.F., Ho, T., Lee, T., Chang, C. and Chen, C. (2019) Application of a deep learning algorithm for detection and visualization of hip fractures on plain pelvic radiographs. European Radiology, 29(10), 5469-5477.

Chung, S.W., Han, S.S., Lee, J.W., Oh, K., Kim, N.R., Yoon, J.P., Kim, J.Y., Moon, S.H., Kwon, J., Lee, H., Noh, Y. and Kim, Y. (2018) Automated detection and classification of the proximal humerus fracture by using deep learning algorithm. Acta Orthopaedica, 89(4), 468-473.

Fazal, M.I., Patel, M.E., Tye, J. and Gupta, Y., (2018). The past, present and future role of artificial intelligence in imaging. European journal of radiology, 105, pp.246-250.

Goddard K, Roudsari A, Wyatt JC. Automation bias: a systematic review of frequency, effect mediators, and mitigators. (2012) Journal of the American Medical Informatics Association : JAMIA. 19 (1): 121-7.

Kahneman, D., Sibony, O., Sunstein, C.R. (2021) Noise: A flaw in human judgement. HarperCollins. London

Kumar, D., Wong, A., Taylor, G.W. (2018).  Explaining the unexplained: a Class-Enhances Attentive Response (CLEAR) approach to understanding deep neural networks.  Available at: https://ieeexplore.ieee.org/Xplore/home.jsp [Accessed: 17th October 2022]

Lodwick, G., Keats, T. E., , Dorst, J. P. (1963). The coding of roentgen images for computer analysis as applied to lung cancer.  Radiology, 81 (2) [online] Available: https://pubs.rsna.org/doi/10.1148/81.2.185 [Accessed 17th October 2022]

Pesapane, F., Codari, M. and Sardanelli, F., 2018. Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine. European radiology experimental, 2(1), pp.1-10.

Rainey, C., O’Regan, T., Matthew, J., Skelton, E., Woznitza, N., Chu, K-Y, Goodman, S., McConnell, J., Hughes, C., Bond, R., McFadden, S., Malamateniou, C. (2021) Beauty is in the AI of the beholder: are we ready for the clinical integration of artificial intelligence in Radiography? An exploratory analysis of perceived AI knowledge, skills, confidence and education perspectives of UK radiographers. Frontiers in Digital Health https://doi.org/10.3389/fdgth.2021.739327

Rajpurkar, P., Irvin, J., Bagul, A., Ding, D., Duan, T., Mehta, H., Yang, B., Zhu, K., Laird, D., Ball, RL., Langlotz, C.,Shpanskaya, K., Lungren, M., Ng, A.Y. (2018) MURA: large dataset for abnormality detection in musculoskeletal radiographs.  Available at: https://arxiv.org/abs/1712.06957 [accessed: 15th October 2022]

Qin, C., Yao, D., Shi, Y., Song, Z., (2018). Computer-aided detection in chest radiography based on artificial intelligence: a survey. BioMedical Engineering OnLine 17, 113

The Doctoral College at Ulster University

Key dates

Submission deadline
Monday 6 February 2023
04:00PM

Interview Date
20th - 22nd March 2023

Preferred student start date
18th September 2023

Applying

Apply Online  

Contact supervisor

Dr Sonyia McFadden

Other supervisors

  • Second supervisor joint is was not on drop down. Can this please be added.