Eleven research projects have been funded through the inaugural UBC AI and Health Network Fellows Program, which supports researchers harnessing artificial intelligence (AI) to transform health research and improve patient care across B.C.
Launched earlier this year with support from a transformative $22.5 million gift from the Gordon B. Shrum Charitable Fund to the UBC Faculty of Medicine, the UBC AI and Health Network unites UBC’s strengths in AI, health research and biomedical innovation to support health system transformation through AI-driven innovation.
The Network’s Fellows Program enables principal investigators (PIs) to recruit, mentor and support postdoctoral and clinical fellows conducting interdisciplinary research at the nexus of AI and health. Each award provides $100,000 towards the hiring of a postdoctoral or clinical fellow.
“The Fellows Program will empower researchers to responsibly integrate AI into health care,” says Dr. Raymond Ng, professor in the UBC Department of Computer Science and Network co-leader. “The funded projects demonstrate the creative potential of our research community to spark innovation that benefits patients and populations.”
The selected projects for 2025 align with the Network’s core focus areas, spanning clinical practice, to translational sciences, data sharing, end-user acceptance and training. Each project advances the Network’s mission to develop and deploy innovative AI tools that enhance timely access to equitable healthcare.
“By connecting researchers across disciplines, the Fellows Program will cultivate future leaders in AI and health,” says Dr. Anita Palepu, professor and head of the UBC Department of Medicine and Network co-leader. “These collaborations will strengthen B.C.’s capacity for AI innovation, paving the way for better health outcomes across the province.”
The Fellows Program is designed to support future leaders who are developing novel AI methods and tools to address real-world health challenges across B.C. and Canada. The program fosters collaboration across disciplines, from computer science and engineering to population health and clinical practice, and contributes to the responsible, ethical and equitable integration of AI in health care.
Funded through the UBC AI and Health Network, with generous support from Canada’s Immuno-Engineering and Biomanufacturing Hub (CIEBH) and the BC Cancer Foundation, the Fellows Program is part of a broader effort to expand B.C.’s leadership in AI-enabled health innovation.
“We’re proud to support the UBC AI and Health Network Fellows Program,” says Dr. Michelle Wong, executive director of CIEBH. “This innovative initiative aligns with our mission to harness the collective research and training excellence of our multidisciplinary, multisectoral partners, ultimately to translate scientific discoveries into treatments.”
“By investing in AI-driven research, we’re advancing real-world solutions that deliver smarter, more personalized cancer care at every stage – from diagnosis to treatment to survivorship – for patients across B.C.,” says Sarah Roth, President & CEO, BC Cancer Foundation.
2025 AI and Health Network Fellows Awardees
The following projects were selected through a competitive peer-review process for their scientific excellence, interdisciplinary approach and potential to advance responsible applications of AI in health research and care delivery.
General stream
Next-Generation Language Modeling for High-Precision and Ultra-Fast Protein Sequencing
Principal investigator: Muhammad Abdul-Mageed, Faculty of Arts
Co-investigator: Laks V.S. Lakshmanan, Faculty of Science
Description: Proteins are essential to cellular function, and understanding their structure is vital for detecting and treating disease. This project develops and applies AI methods to enhance how scientists interpret protein data from mass spectrometry, a powerful technology for studying complex protein mixtures. Traditional analysis methods often overlook rare or previously unknown proteins, slowing research and limiting discovery. By leveraging advanced AI language models, similar to those used in natural language processing, we aim to directly “decode” protein sequences from raw data. This innovative approach will accelerate data analysis, improve accuracy, and generate new biological insights. The resulting advancements could speed the discovery of disease biomarkers, enable more precise cancer diagnostics and treatments, and deepen understanding of human–microbe interactions. Developing this capability in British Columbia will position the province as a leader in AI-driven health research and innovation, strengthening both scientific capacity and healthcare outcomes.
Co-creating an AI-based Decision-Support Tool for Triaging and Diagnosing Musculoskeletal Conditions: DigiMSK
Principal investigator: Clare Ardern, Faculty of Medicine
Co-investigators: Kendall Ho, Faculty of Medicine, Karim Khan, Faculty of Medicine
Description: Many Canadians living with ongoing muscle, bone or joint pain face long waits for diagnosis and care. Because this pain often does not appear on scans or tests, it is frequently misunderstood or dismissed, especially for women, gender-diverse, and racialized people. Limited access to family doctors leaves many patients navigating the system alone.
Current online tools such as HealthLink 811 and Ada are not designed for musculoskeletal issues and perform poorly for these conditions. Our team is exploring how AI could improve communication between patients and clinicians and speed up diagnosis.
Our team is developing DigiMSK, an AI-powered chatbot co-designed with patients and clinicians to help people describe symptoms and connect to appropriate care. The project involves designing the interface, training and testing accuracy, and evaluating usability. Our goal is a trustworthy, accessible digital tool that helps individuals with musculoskeletal pain to obtain timely, equitable and effective care.
AI Integrated Diagnostic Platform for Mild Traumatic Brain Injury
Principal investigator: Naznin Virji-Babul, Faculty of Medicine
Description: Concussions, or mild traumatic brain injuries (mTBI), are common yet difficult to diagnose accurately. Current assessments depend on patients’ self-reported symptoms and clinician judgment, which can be inconsistent and delay care.
This project will develop an AI-powered diagnostic tool that integrates electroencephalography (EEG), a non-invasive measure of brain activity, with clinical data such as symptoms, injury details, and medical history. Using advanced agentic AI and Transformer-based models, the system will learn to detect and monitor concussions more accurately and objectively than current approaches. Interpretable AI models and clinician-friendly tools will be designed to integrate seamlessly into healthcare settings while maintaining strong privacy protections.
Ultimately, this research aims to transform concussion diagnosis and management by providing faster, more consistent, and evidence-based assessments, improving outcomes for patients, and supporting more effective, data-driven care.
Advancing Orthopaedic Diagnostics & Treatment: AI for Dialogue-Driven Documentation and Decision Making
Principal investigator: Anthony Cooper, Faculty of Medicine
Co-investigator: John Jacob, Faculty of Medicine
Description: Diagnostic and treatment decisions are often hindered by missing information and limited access to evidence at the time of care; however, AI is transforming orthopaedic practice through automated documentation, advanced diagnostics, and innovative decision support. The primary objective is to develop a hybrid AI pipeline including generative AI and semi-supervised machine learning methods to extract essential information from patient-doctor dialogues, clinical and radiology reports, thereby supporting physicians by providing accurate diagnostic and treatment decisions. The system will also identify missing information during consultations, prompting relevant follow-up questions, and generate clinical reports using extracted information from dialogue, structured according to the physician’s preferred template.
Advancing Patient-Centered AI: Validating NLP Models Using Clinical and Communication Data in BC
Principal investigator: Richard Todd Lester, Faculty of Medicine
Description: This project harnesses AI and natural language processing (NLP) to advance equitable healthcare by analyzing real-world patient communication within B.C. and internationally. Utilizing WelTel, a secure two-way texting platform founded by Dr. Lester, the study focuses on developing and validating NLP models that capture authentic patient-provider interactions from diverse populations, including Indigenous and resource-limited communities. These unique datasets span clinical areas such as pediatric diabetes (Interior Health), cardiology (BC Children’s Hospital), and primary and cancer care in Haida Gwaii, offering rich insights into patient needs, care barriers and engagement patterns.
An Explainable AI Framework for Collaborative Assessment of Motor Disorders using De-Identified 3D Biomarkers
Principal investigator: Timothy Murphy, Faculty of Medicine
Description: Parkinson’s disease (PD) affects movement and is currently assessed using clinical rating scales that rely on human observation, which can be subjective and inconsistent. Although AI has revolutionized many areas of medicine, its use in assessing movement disorders like PD is still limited, mainly due to patient privacy concerns and the lack of transparent, explainable AI models. This project will develop an AI tool that can analyze patient movement from video in a secure and understandable way. The system uses advanced computer vision and 3D modeling to represent a person’s motion through an anonymized digital avatar, protecting privacy while revealing how the AI reaches its conclusions.
Clinicians can review and interact with these visualizations, helping the AI improve through feedback. The result will be a privacy-preserving, explainable AI platform that supports more objective and collaborative diagnosis of Parkinson’s disease.
Decoding Alzheimer’s Disease via Spatial Redoxomics and Interpretable Multimodal AI
Principal investigator: Xin Tang, Faculty of Science
Co investigators: Freda Miller, Faculties of Medicine and Science, Margo Seltzer, Faculty of Science
Description: Alzheimer’s disease (AD) arises from a complex mix of biological changes in the brain – including oxidative stress, which disrupts the cell’s chemical balance, and the accumulation of amyloid-β (Aβ) plaques that damage neurons. However, scientists still do not fully understand how these changes interact with the brain’s many genes and cell types.
This project will use AI to uncover these relationships by analyzing data from lab-grown human “mini-brains” known as organoids. Using cutting-edge imaging and spatial genomics tools, researchers can measure oxidative stress, amyloid plaque formation, and gene activity in the same cells. By combining these rich data sources, the team will develop an interpretable AI model that links oxidative damage and Aβ accumulation to specific genes and cells.
This work will generate powerful new tools for studying AD and identify potential biomarkers and therapeutic targets, helping pave the way toward earlier diagnosis and more effective treatments
Cancer stream (fully funded by BC Cancer Foundation)
Adapting and Evaluating a Human-in-the-loop AI System to Enhance Exercise Support for People with Colon Cancer
Principal investigator: Lauren Capozzi, BC Cancer
Co-investigators: John-Jose Nunez, BC Cancer and Faculty of Medicine, Kristin Campbell, BC Cancer and Faculty of Medicine
Description: A major international study, CHALLENGE, published in 2025 in the New England Journal of Medicine, showed that a structured exercise program can help people with colon cancer live longer and feel better. The program worked because it included regular coaching from qualified exercise professionals to help people stay active safely. However, many cancer programs do not have enough staff to offer this support to everyone who needs it.
This project will explore how a new type of AI system, called “human-in-the-loop” AI, can help extend this proven exercise program to more patients. This system combines automated coaching with oversight from real clinicians to make sure the advice is safe and personalized. Together with patients and care teams, we will adapt and test this AI-supported approach. Our goal is to make exercise support more accessible and improve recovery and quality of life after colon cancer.
DiagTrace: Making Cancer Diagnosis Traceable with Knowledge Graphs and Chain-of-Reasoning
Principal investigator: Xiaoxiao Li, Faculty of Applied Science
Co-investigators: Zu-Hua Gao, Faculty of Medicine, Gang Wang, Faculty of Medicine
Description: Cancer care depends on information from medical reports and patient updates, but much of it is trapped in free text that requires time-consuming manual review. DiagTrace uses explainable AI to make this information clear, traceable, and actionable. It builds a structured “knowledge graph” linking key details such as tumor site, stage, and biomarkers from BC Cancer reports, with each fact cited to its source.
Using this foundation, DiagTrace creates concise, auditable summaries that show how conclusions are reached (e.g., lesion → biomarker → therapy eligibility) and flag uncertainties or missing data. The system combines advanced language models with strong privacy and fairness safeguards, integrating patient-reported symptoms and preferences. Co-designed with clinicians, DiagTrace acts as an assistant, helping doctors by triaging reports and providing clear, evidence-based summaries to improve the speed and clarity of cancer care. The project will deliver a validated prototype and a toolkit for scaling AI-enabled diagnostics across cancer centers.
AI Cancer Care Navigation Assistant
Principal investigator: John-Jose Nunez, Faculty of Medicine; BC Cancer
Co-investigators: Lauren Capozzi, BC Cancer; Srinivas Raman, Faculty of Medicine
Description: Many people with cancer face challenges accessing support such as counselling, reliable information, or transportation assistance when they need it most. Services are available through BC Cancer and community organizations. However, patients often struggle to find the right resources due to the complexity of the system, as resources can vary by cancer type, location, age, and other factors. These challenges can lead to delays in care, unmet needs, poorer treatment outcomes, and added strain on the healthcare system.
Our project will bring on a postdoctoral fellow to help develop and test a personalized Cancer Care navigation assistant powered by AI. The assistant will recommend resources tailored to their situation, including those they may not know exist. It will deliver information in formats such as text, automated phone calls, or printable PDFs, empowering patients with varied technological preferences.
Development of Acceptance Testing and Routine Quality Assurance of AI Systems in Breast and Lung Screening
Principal investigator: Rasika Rajapakshe, BC Cancer and Faculty of Medicine
Description: The integration of AI into breast and lung cancer screening workflows – particularly in mammography and low-dose computed tomography (LDCT) – has shown promise in enhancing detection accuracy, improving workflow efficiency and addressing radiologist shortages. However, the clinical safety and performance of AI systems are contingent upon rigorous acceptance testing prior to deployment and continuous quality assurance (QA) during operation. Unlike traditional imaging equipment, AI systems introduce new layers of variability due to their dependence on training data, model updates, and algorithmic behavior under varying clinical conditions.
Furthermore, AI systems can detect ethnicity from medical images. There is currently a lack of standardized, evidence-based methodologies for testing, monitoring, and maintaining the performance of these AI tools in real-world screening settings. This project aims to address this gap.
Please note: A number of the teams are actively recruiting postdoctoral or clinical fellows. If you’re interested in exploring an opportunity within the Network, please connect with us at ai.healthnetwork@ubc.ca
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