Collaboration Hub

Researcher: Yalini Senathirajah
Institution: University of Toronto
Project description: Interested in collaborations with those with a mature AI recommendation algorithm that could be integrated into the EHR (our research is on user-controlled platforms for EHR redesign)
Project aims: Composable approaches to EHR design may have benefits for human-computer interaction, cognitive support, efficiency, workflow, and other benefits. It also may make it easier to incorporate AI-based recommendations at the point-of-care. We are interested in possibly piloting this approach with those with appropriate tested algorithms seeking to incorporate them into the EHR.
Desired skills in collaborators: Those who have developed AI-based algorithms aimed at clinical care, at an appropriate stage of development/testing.

Contact: Adam Kepecs (Tel: 647-812-6678)
Organization: IntelAGENT
Project aims: This project aims to use Machine Learning to parse a clinician's unstructured free-text patient encounter note in their Electronic Medical Record (EMR) system to identify billable OHIP services. This will alleviate administrative overhead and help physicians spend more time in clinical practice.
Desired skills: Expertise in Natural Language Processing models for unstructured clinical notes.
Other collaborators in this project: Prof. Frank Rudzicz

Researcher: Laurent Bozec
Lab: https://bozec-lab.ca
Institution: University of Toronto
Project description: We are developing convolutional neural networks for the purposes of classifying collagen degradation
markers. This will be used to assist in the study and diagnosis of connective tissue disorders such as Ehlers-Danlos Syndrome.
Desired skills in collaborators: Proficiency in MATLAB and/or Python is required, as well as familiarity with machine learning techniques, specifically CNN models or image recognition.

Researcher: William Chow
Institution: McGill University
Project description: We are looking for a student with Al coding ability who can build a large language model for image analysis.
Desired skills in collaborators: Coding and AI LLM building skills

Contact: Rashi Ramchandani

Institution: University of Ottawa/Hospital for Sick Children

Project Title: Development and Validation of an Artificial Intelligence Application for Real-Time Detection of Pediatric ENT Health Trends on Social Media 

Project description: This study aims to build and validate a real-time artificial intelligence (AI) system that continuously scans social media platforms (TikTok, YouTube, Instagram, X, and Reddit) to detect emerging health-related trends involving the ear, nose, and throat among children and adolescents. The system will classify and rank viral behaviours by engagement metrics and notify pediatric otolaryngologists of potential trends posing health risks or misinformation. The project will evaluate model performance, accuracy, and clinical usefulness of automated social media surveillance in pediatric otolaryngology.

Desired skills in collaborators: We’re seeking collaborators with experience in full-stack development, AI integration, and API use for social media data extraction. Ideal candidates can design real-time dashboards, deploy LLM or NLP models for trend analysis, and translate technical outputs into clinically meaningful visualizations for pediatric otolaryngologists.

CONTACT:  Abhishek Chopra

RESEARCH PROJECT TITLE: Understanding Physician EMR Workflow Needs and Validating AI-Driven Summarization Tool

PROJECT DESCRIPTION: This project tests a new AI tool that creates quick summaries of electronic medical records, helping doctors save time and spend more time with patients. We compare AI summaries to those written by doctors to see how well they work and what information is most important. By talking to doctors and testing these tools, we aim to build smarter, safer technology that fits into real healthcare settings and improves patient care. Our research will help shape the next generation of clinical AI tools that are easy to use, protect patient privacy, and support better health outcomes.

SKILLS REQUIRED IN POTENTIAL COLLABORATORS: We seek physicians with experience with EMR systems, privacy-preserving technologies, and research ethics. Strong communication skills and a commitment to equitable, clinically relevant innovation are essential.

SPECIFIC DATA SETS : We are seeking access to de-identified electronic medical record (EMR) datasets for research purposes, focusing on patient records that include clinical notes, diagnoses, medications, and lab results. These datasets will be used solely to evaluate and validate AI-generated summaries, with strict adherence to privacy and ethical guidelines.

ADDITIONAL NOTES: 
• The project investigators are Camellia Zakaria (Dalla Lana School of Public Health, University of Toronto), Karim Keshavjee (Institute of Health Policy, Management and Evaluation, University of Toronto)
• We welcome collaborators with clinical, technical, or policy expertise in AI and healthcare. Our focus is on equitable, privacy-preserving innovation that directly supports physicians and improves patient outcomes

Connect: Dr. Mina Nouredanesh (Assistant Professor and Tier 2 Canada Research Chair in Al for Complex Health Data).

Faculty page: https://umanitoba.ca/community-global-health/faculty-staff/mina-nouredanesh

We are seeking highly motivated candidates for master's, PhD, and postdoctoral positions to join our research group. Our interdisciplinary research focuses on developing and applying machine learning approaches to multimodal health data (e.g., medical imaging and wearable sensor data) to advance the understanding, diagnosis, and treatment of challenging health conditions such as Parkinson's disease, dementia, and autoimmune diseases. 

Contact: Keith Thompson

Overview:
We have a visting Associate Professor from Ankara University's Dept. Of Fam Med. She is working with a pediatrician there who has created a model trained on 10,000 samples of children urinalysis ages 1mo to 12-years-old. The model predicts UTI successfuly without waiting for culture reports. Our visiting professor would like to test it on a sample of about 100 Canadian children of the same age ranges We would need urinalyisis and subsequent culture results. We prompt the model with the urinalysis reports and see if it correctly determines which samples were indeed positive culture.
Problem is we only have until June 2026.

What skills are you looking for?
Basically, to get the data pull we need- not sure how complex that will be

Which specific data sets are you seeking to obtain? (Complete if applicable)
Children urinalysis age 1mth to 12 yr with culture reports for same urine sample