Featured Publications

ProbMED: A Probabilistic Framework for Medical Multimodal Binding

We present ProbMED, a novel probabilistic framework that enables robust multimodal learning in healthcare by binding diverse medical data types including images, text, and physiological signals. Our approach addresses uncertainty quantification and enables more reliable clinical decision support through principled probabilistic modeling of multimodal medical embeddings.

Measuring the Generalizability of AI Models in Healthcare

Unstructured datasets, including medical imaging, electrocardiograms, and natural language data, are gaining attention with advancements in deep convolutional neural networks and large language models. However, estimating the generalizability of these models to new healthcare settings without extensive validation on external data remains challenging. We propose an open source, bias-corrected external accuracy estimate that better estimates external accuracy to within 4% on average by measuring and calibrating for bias induced shortcut learning.

AI-guided Prospective Cancer Radiotherapy

We describe an AI model that generates radiation treatment plans for prostate cancer patients. AI was successfully deployed in a standard-of-care clinical setting, providing gains in efficiency, improved treatment quality, and an understanding of physicians’ decision-making and perceptions of AI use when patient care is at stake.

First Voxel-Based AI for Radiation Therapy Planning

We present a proof-of-concept for a method to automatically infer the radiation dose directly from the patient's treatment planning image based on a database of previous patients with corresponding clinical treatment plans. Our method uses regression forests augmented with density estimation over the most informative features to learn an automatic atlas-selection metric that is tailored to dose prediction.

Director

Chris McIntosh

  • Chair in Medical Imaging and Artificial Intelligence
  • Scientist: Toronto General Hospital Research Institute, Peter Munk Cardiac Centre, Joint Department of Medical Imaging, University Health Network
  • Assistant Professor: Department of Medical Biophysics, Department of Computer Science, University of Toronto
  • Faculty Affiliate, Vector Institute
  • Contact information

      • Dr. Chris McIntosh is a Computer Scientist and Chair in Medical Imaging and Artificial Intelligence at the University of Toronto and University Health Network. He has received prestigious academic awards from NSERC, CIHR, and the Michael Smith Foundation for Health Research. His PhD dissertation in computer vision, machine learning and healthcare, developed AI-driven methods for quantitative medical image analysis through manifold learning, received an honourable mention for the top dissertation in computer vision and medical image analysis by the Canadian Image Processing and Pattern Recognition Society.

      His research centers on developing and deploying machine learning and AI technologies that directly improve patient care. Working at the intersection of computer science and medicine, his work encompasses multimodal learning, meta learning, and explainable AI across diverse healthcare applications. From revolutionizing radiation therapy treatment planning to creating innovative wearable device biomarkers in partnership with Apple for heart failure monitoring, his research bridges the gap between cutting-edge AI theory and real-world clinical impact.

      Dr. McIntosh's groundbreaking work on AI-driven radiation therapy planning has achieved remarkable clinical success. Featured on the cover of Nature Medicine, this technology has received regulatory approvals and is now used directly in patient care worldwide through a successful licensing deal with RaySearch Laboratories. The system demonstrates superior results to standard of care for cancer patients while reducing treatment planning time from hours to minutes—a breakthrough that exemplifies his commitment to translating AI research into tangible patient benefits.

      His recent contributions include developing novel methods to estimate AI model generalizability without external data (published in npj Digital Medicine) and advancing multimodal generative models that seamlessly integrate diverse medical data sources. His publications span leading technical and clinical venues including Nature Medicine, ICCV, IEEE Transactions on Medical Imaging, npj Digital Medicine, MICCAI, and CVPR, reflecting the interdisciplinary nature and broad impact of his research.

      The real-world significance of his work has garnered attention from prominent media outlets including Forbes, ScienceDaily, MedPage Today, and University of Toronto News, highlighting his role in advancing AI applications in healthcare.

      Outside of his research pursuits, Chris enjoys spending time with his family, playing golf, and snowboarding.

    Students and Staff

    Siham Amara-Belgadi (PhD Student)

    Sangwook Kim (PhD Student)

    Balagopal Unnikrishnan (PhD Student)

    William Gao (PhD Student)

    Sejin Kim (PhD Student)

    Bhavish Verma (PhD Student)

    Cathy Ongly (PhD Student)

    Max You (PhD Student)

    Lab Alumni

    Aly Khalifa (Physics Resident, Princess Margaret Cancer Centre)

    Overview

    Working together

    Innovation doesn't happen in a vacuum. Jointly embedded in Canada's largest hospital network and the University of Toronto, our research brings together experts and trainees in computer science, medicine, biology, and physics to solve impactful problems at the intersection of artificial intelligence and medicine.

    Medical domains: Our work includes a variety of data modalities (biological, wearable, structured reporting, and imaging) and critical diseases such as heart disease and cancer. We work closely with clinicians to understand and ultimately augment patient care with AI.

    AI technologies: Solving clinical problems means pushing and extending the boundaries of AI and machine learning technology including semi-supervised learning, domain adaptation/model generalization, meta-learning, and multi-modal data fusion.

    Data: We work on datasets of all shapes and sizes, from rare diseases with rich structured data but few patients, to diverse imaging datasets in the tens of thousands.

    Keep reading below for example projects.

    Wearable device biomarkers

    What if we understood the patient journey instead of the Coles Notes?

    Wearable technologies offer a unique glimpse into patient function and biology outside of episodes of care. If AI is the present, wearbles with AI are the next frontier. Working with leading cardiologists Heather Ross and Yas Moyaedi, the Ted Rogers Centre for Heart Research, and a key partnership with Apple we are developing AI technology to asses the role of wearbles in heart failure.

    Key Publications:

    In the News:

    • June 2025: Canadian Healthcare Technology - Exploring how smartwatch technology and AI are revolutionizing remote heart failure monitoring, offering new possibilities for early intervention and personalized patient care.
    • Fall 2020: University of Toronto - Groundbreaking collaboration between University of Toronto researchers and Apple to develop innovative smartwatch capabilities for heart failure prevention and early detection through advanced wearable AI.

    Multimodal Generative Models and Multitask Learning

    What if a model could simultaneously understand multiple sources of medical data and multiple medical tasks?

    Multimodal large language models in healthcare represent a cutting-edge fusion of advanced linguistic capabilities and diverse data sources. By seamlessly integrating text, images, electrocardiograms, and more these models harbor great potential to enhance clinical care through more robust and generalizable machine learning.

    Key Publications:

    Automation in Radiation Therapy

    The new standard of care

    An experienced clinical team can more than double a cancer patient's odds of survival. In particular, radiation therapy (RT) is a key part of the treatment for 40% of all cancer patients with the potential to be highly curative and effective given the right treatment plan. However, a major barrier in that effectiveness is the expertise and experience of the patient's clinical team in his or her particular cancer. We believe that AI can help bring the expertise of the world's best clinical teams to every patient in every hospital, saving lives and costly resources.

    Working with clinical partners (Tom Purdie, Ale Berlin, and Leigh Conroy) we have developed the world's first computer vision treatment system that we piloted to treat prostate cancer patients at Princess Margaret Cancer Centre, with superior results to our standard of care for the majority of patients. Cancer treatments are generated by the AI in minutes, instead of the hours-to-days taken under the typical manual process, increasing both efficiency and quality of care. Follow-up studies are expanding the models to breast cancer, brain tumours in young adults, and more. Through a successful licensing deal of this Canadian technology to RaySearch Laboratories we are also supporting deployment across Canada and around the world.

    Key Publications:

    In the News:

    • October 2022: UroToday - ASTRO 2022 conference coverage highlighting clinical integration of machine learning for curative-intent radiation treatment of patients with prostate cancer, showcasing real-world implementation of AI-driven treatment planning.
    • 2021: MedPage Today - Comprehensive coverage on therapeutic radiology and machine learning advancements, exploring the intersection of AI technology and clinical radiation therapy practice.
    • 2021: Hospital Healthcare - Examining physician perspectives and challenges in adopting machine learning algorithms for treatment planning in prostate cancer, addressing clinical adoption barriers and solutions.
    • June 2021: ScienceDaily - Research breakthrough showcasing fast AI-driven radiotherapy planning capabilities, demonstrating significant improvements in treatment efficiency and quality.
    • August 2018: Forbes - Early coverage of groundbreaking AI technology capable of designing comprehensive radiation therapy treatment plans for cancer patients in just twenty minutes, revolutionizing traditional planning workflows.

    AI Model Generalizability in Healthcare

    How do we know if our AI will work in the real world?

    The promise of AI in healthcare hinges on models that can generalize across different hospitals, patient populations, and clinical settings. However, unstructured medical data—from imaging scans to ECGs to clinical notes—presents unique challenges for model validation and deployment.

    Our research addresses a critical gap: how to estimate whether an AI model trained at one institution will perform reliably at another, without requiring extensive external validation studies. We've developed novel bias-corrected methods that can predict external accuracy within 4% on average by identifying and calibrating for problematic "shortcut learning" that models often exploit.

    This work enables more confident deployment of AI systems across healthcare networks and helps ensure that the benefits of machine learning reach patients regardless of where they receive care.

    Key Publications:

    In the News:

    • November 2024: EMJ Podcast on AI - Discussing the advancement of AI from "bench-to-bedside," while exploring concerns around bias and fairness when working with diverse patient populations and the critical importance of ensuring equitable healthcare AI deployment.
    • September 2024: UHN Research - Predicting the performance of AI tools and exploring potential biases, highlighting breakthrough research in estimating AI model reliability across different healthcare settings without extensive external validation.

    Image Segmentation

    Coming soon to a clinic near you

    Image segmentation is the gold standard for computational morphology and a key step in the radiation therapy process. A long sought after technology, automated segmentation methods are recently achieving near clinical performance, paving the way for clinical studies and real-world deployment. Our research focuses on developing robust deep learning approaches for multi-organ segmentation that can handle the variability encountered across different imaging protocols and patient populations.

    Building on our success in automated treatment planning, we are now conducting clinical studies to evaluate the integration of AI-driven segmentation tools into routine clinical workflows. These studies are examining both the accuracy and efficiency gains of automated contouring in radiation therapy, with early results showing potential savings for clinicians while maintaining high-quality treatment planning standards. However, many challenges remain, including handling edge cases, ensuring consistent performance across diverse patient anatomies, and seamlessly integrating these tools into existing clinical systems.

    Jobs Help Wanted"Jobs Help Wanted" by Innov8social is licensed under CC BY 2.0

    Do you want to change the shape of AI and healthcare?

    • Postdoctoral Fellowships

      We are seeking exceptional postdoctoral fellows to join our interdisciplinary team developing AI solutions that directly impact patient care. Ideal candidates have strong expertise in machine learning and deep learning, with demonstrated experience in healthcare applications involving medical signals, imaging, or wearable data.

      Our postdocs work on cutting-edge projects from multimodal AI models to clinical deployment of radiation therapy planning systems. We offer competitive funding, mentorship opportunities, and the chance to publish in top-tier venues while making real-world clinical impact.

      Interested candidates should submit a CV, cover letter describing research interests and career goals, your most relevant publication, and contact information for three references to Chris McIntosh with the subject line "Postdoctoral Fellowship Application".

    • Graduate Students (MSc/PhD)

      Join our dynamic research lab where computer science meets clinical medicine. We welcome motivated students passionate about developing AI technologies that save lives and improve patient outcomes. Our graduates publish in venues like Nature Medicine, ICCV, and MICCAI while contributing to technologies deployed in hospitals worldwide.

      We seek students with strong programming skills and background in machine learning, computer vision, or related fields. Prior experience in healthcare or medical imaging is valuable but not required—we provide comprehensive mentorship to help you develop expertise at the intersection of AI and medicine.

      Interested students must apply to either the Department of Medical Biophysics (preferred) or Computer Science based on your background and interests. Feel free to reach out before applying to discuss research opportunities.

    • Summer Research Opportunities

      Experience the excitement of healthcare AI research through our summer internship program. Work alongside graduate students and postdocs on projects ranging from wearable device biomarkers to automated medical imaging analysis. Our summer students often contribute to publications and gain valuable experience for graduate school applications.

      We welcome undergraduate students with programming experience and coursework in machine learning, computer science, or related fields. Interest in healthcare applications and strong problem-solving skills are essential.

      Apply through the MBP summer student program or contact us directly to discuss project opportunities and eligibility for other summer research programs.

    Affiliations