Yuxuan Wu

Artificial Intelligence and the Future of Healthcare Workforce – A Study on Machine Learning Applications in Medical Imaging Practice and the Implications on Imaging Professions and Professionals Yuxuan Wu

Recent progress in Artificial Intelligence (AI) research challenges the understanding that the process of automation only impacts upon low-skilled jobs. Being trained with big datasets, AI models show greater potential in automating tasks related to professional work, leading to a boom of applications aiming to empower professionals. To understand its implications for work and employment, my PhD  project focuses on the UK healthcare sector, especially Medical Imaging, a domain where most market authorised clinical AI applications belong. 

Two broad questions are being addressed:

(1) How and why are AI applications being developed, implemented, and used?

(2) How is Imaging workforce being reconfigured and with what implications for related professions and practitioners? A multi-method, case-based qualitative study is presently being undertaken, which considers the motivations and actions of different stakeholders. Specifically, interviews and documentary analysis are being conducted regarding key stakeholders, including application developers, professional associations, management members at healthcare organisations, and imaging clinicians with AI usage experience.

Supervisors: Dr Paul Lewis Dr Andy Hodder

Email: YXW300@student.bham.ac.uk

Biography

Yuxuan has a general interest on issues related to New Technologies and Work and Employment. Her research is normally phenomenon-driven and adopts a pragmatism in theory choice. Her PhD research focuses on the application of Machine Learning in Health work and its implications for the practitioners’ professions. 

Yuxuan also maintains a keen curiosity about the impact of Artificial Intelligence on diverse aspects of society. For instance, the recent advancements in generative AI models have opened up new possibilities for optimising numerous elements, including musical performances. Being reconstructed by AI models, pre-recorded soundtracks could be enhanced to perfection in terms of both instrumental and vocal performance. However, it's rare to find discussions suggesting that live performances will become obsolete. Even with the availability of tools like Auto-Tune, skilled instrumentalists and vocalists retain their competitive edge. This raises intriguing questions such as where the boundary of AI-enabled optimisation is, especially when the subjective human preference is taken into consideration.

Qualifications

Bachelor of Science in Sociology – Sun Yat-sen University – Jun 2019 

Master of Science in Organisational Behaviour with Distinction – London School of Economics – Dec 2020

Scholarship

Birmingham Business School Doctoral Scholarship

Research Interests

Work and Employment; New Technologies

Publications

"From Computational Models to Clinical Practice: The Reality of Artificial Intelligence in Healthcare", Paper presentation for “Disrupting technology: Contextualising continuity and change in technology, work and employment”, International Conference, Monash University Prato Centre, Italy, 11-13 Jun 2023 

“Jurisdictional Evolution and Power Transition: Assessing the implications of Machine Learning Software usage in Medical Imaging Practice”, Paper presentation for Digital Transformations of Work and Employment in the Professions, A BSA Early Career Forum Regional Event, Leeds University Business School, Leeds, 28 Sep, 2023

Membership of Professional Bodies

The British Universities Industrial Relations Association (BUIRA) - Apr 2022 

British Sociological Association (BSA) - May 2023

Teaching Responsibilities

Seminar Tutor / Organisational Behaviour 28306 / Postgraduate / 2022-23 

Seminar Tutor / Research Methods in International Business 19500 / Postgraduate / 2022-23