Programme(s) to which this project applies:
|☑ MPhil/PhD||☒ MRes[Med]||☒ URIS|
Current clinical treatment for different diseases has several common limitations, such as low efficiency, reliance on subjective judgment, inconsistency, etc. Artificial intelligence (AI) technology may have great significance for the revolution of the clinical treatment process by providing fast and accurate assistance. However, considering that clinical treatment planning is a highly complex process, which is difficult for AI to fully take over, the human specialist and AI should work closely together for better treatment outcomes.
Thus, we aim to propose an interactive medical system for treatment planning, which utilizes advanced AI technology and interactive design to improve the efficiency, consistency and accuracy of clinical treatment planning.
Our objective includes:
1. Establish a multi-modality big dataset for system development
2. Develop a set of high-performance AI models for different clinical tasks to improve the efficiency of clinicians.
3. Design a user-friendly UI interface to make clinicians and AI work closely together.
4. Conduct technical and clinical validation of our development system.
Dr T Zhang, Department of Orthopaedics and Traumatology
Dr Teng Grace Zhang is a biomedical engineer with a medical background. Most of Grace’s research combines both disciplines by focusing on the modelling of biological systems with direct clinical applications including telemedicine, auto-diagnosis, surgical planning and tracking to facilitate real-time feedback with minimal radiations.
Currently, Grace is an Assistant Professor, at the Digital Health Laboratory of the Orthopaedics and Traumatology Department, Clinical Medicine School, Faculty of Medicine, The University of Hong Kong (HKU). Previously, Grace has worked for nearly seven years as a Scientific Officer at the St George Clinical School of the University of New South Wales (UNSW, Sydney, Australia). All Grace’s tertiary education including her PhD (Australian Postgraduate Award) was completed at UNSW. Grace also has experience in running multiple instruments and drug trials funded by Medtronic and Sigma. She’s experienced in managing a new system developed as she worked in Kunovus Australia for two years as a system engineer prior to coming to the University of Hong Kong. Members of IEEE, EMBS, ABEC etc. The current ongoing projects include HMRF08192266 on light-based AI-driven malalignment quantification, HMRF19200911 on AIS digital health, PRP/078/21FX for surgical planning, AOSpine for automated spine malalignment screening, MRP/038/20X for non-radiation AI diagnosis and ITS/329/19 for anti-migration bone screws.
For more information or to express interest for this project, please email the supervisor or the specified contact point in the project description. Interested candidates are advised to enclose with your email:
Information on the research programme, funding support and admission documentations could be referenced online at the Research Postgraduate Admissions website. General admission enquiries should be directed to email@example.com.
HKUMed MBBS students interested in the Master of Research in Medicine (MRes[Med]) programme may visit the programme website for more information.
HKUMed UG students interested in the Undergraduate Research Internship Scheme (URIS) may visit the scheme’s website for more information.