CityUHK Team Combines Microsystems and AI to Advance Cancer Treatment

What Happened

Professor Khoo Bee Luan and her research team at City University of Hong Kong (CityUHK) have developed an approach combining microsystems technology and artificial intelligence to advance personalised cancer treatment. The work, highlighted by the university on October 2, 2026, centres on tailoring cancer therapies to individual patients rather than applying standardised treatment protocols.

Who Is Involved

Professor Khoo Bee Luan leads the research from CityUHK's Department of Biomedical Engineering. Her work is part of the university's broader She Impacts initiative, which highlights research contributions by women faculty members across the institution. CityUHK is a publicly funded research university in Hong Kong, ranked among the leading institutions in Asia for engineering and applied sciences.

What the Research Involves

The research integrates microsystems, which are small-scale engineered devices capable of manipulating biological samples at the cellular level, with AI-driven analysis to process and interpret data generated from those systems. The combination is intended to give clinicians more precise information about how individual tumours behave and how specific patients may respond to particular therapies.

Microsystems technology in biomedical contexts typically involves devices such as microfluidic chips, which can isolate and analyse circulating tumour cells or other cancer biomarkers from small patient samples. When paired with machine learning models, these systems can identify patterns across large volumes of biological data that would be difficult to detect through conventional laboratory analysis alone.

The university's announcement did not specify which cancer types the research is currently targeting, nor did it disclose clinical trial status or regulatory submissions at this stage.

Background

Personalised or precision medicine has been an active area of oncology research for more than a decade. The central premise is that cancers with similar clinical presentations can behave differently at the molecular level, and that treatment outcomes improve when therapies are matched to the specific biological profile of a patient's disease rather than applied uniformly across a patient population.

AI has increasingly been applied to this challenge. Machine learning models have been used to analyse genomic data, medical imaging, and patient records to identify which patients are most likely to respond to specific drug regimens. Microsystems technology adds a further dimension by enabling the rapid, low-cost isolation and testing of live tumour cells outside the body, which can provide real-time information about drug sensitivity for a given patient.

Several research institutions and biotechnology companies globally are working in adjacent areas, including the use of organ-on-chip devices and AI-assisted pathology platforms. CityUHK's approach, combining both microsystems hardware and AI software within a single integrated research programme, reflects a broader convergence of engineering disciplines within biomedical research.

What It Means in Practice

If the approach advances through clinical validation, it could give oncologists data-driven guidance on which therapies to prioritise for individual patients before committing to a full treatment course. This would reduce the time patients spend on ineffective treatments and potentially limit unnecessary side effects associated with therapies that are not well matched to their specific cancer profile.

The research also has implications for drug development. Microsystem platforms that can test therapeutic responses on patient-derived cells could accelerate early-stage screening of new cancer drugs by providing more biologically relevant data than some existing laboratory models.

What Comes Next

CityUHK has not announced a timeline for clinical trials or external research partnerships related to the project, and further details on the programme's scope and funding are expected to be published through the university's research communications channels.