How applied AI research at Sheridan is helping improve health diagnostics and services

AI is quickly reshaping how healthcare is delivered. Within Sheridan’s Centre for Applied Artificial Intelligence (CAAI) and Faculty of Applied Science and Technology (FAST), researchers are helping develop AI-driven solutions that enhance the patient experience by improving clinical workflows that in turn make care more accessible and impactful.

From improving the accuracy of digital screening tools and chronic pain assessments to helping clinicians coordinate urgent hospital transfers for critically ill newborns, three recent Sheridan research projects highlight how applied AI research at Sheridan is helping advance healthcare services and diagnostics to be more accessible, accurate and responsive. By combining technical expertise with industry and community collaboration, the work is translating emerging AI capabilities into practical solutions that strengthen Canada’s digital health innovation ecosystem.

A new AI platform for faster hospital transfers for critically ill newborns

When newborns require specialized neonatal intensive care, every minute matters. Each year, approximately 15,000 critically ill newborns in Canada are admitted to Level 3 neonatal intensive care units (NICUs). Many of these infants must be transferred from another hospital because they were not initially delivered in a facility with the necessary level of care. Currently, physicians must manually contact hospitals to locate an available NICU bed — a process that can take up to 35 minutes during critical situations.

Led by CAAI Research Manager Dr. Haruna Isah and Nick Sajadi of Sheridan’s FAST, and in collaboration with technology integration company Korah Limited, the research project developed Find My Bed, an AI-driven decision support platform designed to help clinicians quickly identify the most appropriate hospital for urgent neonatal transfers. By enabling more accurate, real-time insight into NICU capacity and hospital capabilities, the platform supports faster decision-making and ensures critically ill newborns are directed to the right care as quickly as possible.

The “Find My Bed: An AI Navigator for Babies Requiring Immediate Hospital Transfers” project, funded by an Natural Sciences and Engineering Research Council of Canada (NSERC) Mobilize Seed Fund grant and a Mitacs BSI, built the platform by gathering insight from a multidisciplinary group of collaborators, including experts in neonatal health, nursing, healthcare process optimization and national clinical networks, alongside key stakeholders such as the Ministry of Health. This ensures the platform is designed to address both clinical and operational realities within Canada’s healthcare system. The Find My Bed platform was also developed to connect with Korah Limited’s existing ccRobot.ai technology, enabling streamlined healthcare logistics and communication between facilities.

This initiative is particularly unique as it combines predictive analytics, risk assessment and hospital capability matching into a single automated platform, meaning Find My Bed has the potential to transform how neonatal transfers are coordinated. By helping physicians make faster, more informed decisions, this project and new technology could significantly improve outcomes for critically ill newborns while supporting a more efficient and equitable distribution of patients across hospitals.

Improving the usability of AI-powered skin health apps

AI is increasingly being used in mobile health applications, including ones designed to help screen for potential skin conditions such as cancerous lesions. These tools are particularly important for individuals who live in remote or underserved regions who typically do not have easy access to care. However, the screening tools’ effectiveness is dependent on how well users can capture images and interpret results.

The project “Improving UX of AI-enabled Skin Care Apps for Enhancing the Usability and the Machine Learning Pipeline,” led by Dr. Ghassem Tofighi of Sheridan’s FAST in partnership with dermatology care provider Skinopathy Inc., focuses on improving the user experience of AI-enabled skin care apps. This includes the company’s GetSkinHelp application, which uses machine learning to analyze smartphone images of skin lesions and provide screening results that may help identify potential concerns like the early detection of skin cancers.

The research, funded by an NSERC Applied Research Technology Partnership (ARTP) grant, focuses on improving the human-AI interaction within the app, particularly in guiding users through image capture processes such as cropping lesion images or taking standardized facial photos for assessment. By helping users follow instructions more accurately, the project works to ensure that images closely resemble those used in training AI models, improving the reliability of screening results.

Using AI to more accurately assess and treat chronic pain

Chronic pain affects roughly one in five people in Canada and often has significant impacts on the healthcare system, as well as individuals’ quality of life — whether it's something simple like not being able to enjoy walking their dog or something more financially detrimental, like not being able to maintain steady employment based on physical limitations that impact their ability to complete assigned tasks. Yet assessing pain remains a challenge for clinicians because it relies heavily on patient self-reporting, which can be subjective, and medical imaging that does not always accurately correlate with pain levels.

This lack of objective measurement creates two major challenges. For many patients living with chronic pain, difficulty in accurately documenting their symptoms can lead to delays in receiving appropriate care and treatment. At the same time, malingering — the intentional exaggeration or fabrication of symptoms — can complicate clinical assessments and place additional strain on healthcare, insurance and legal systems.

To help address these challenges, the CAAI partnered with chronic pain management provider Karmy Pain Clinics on the project “Using AI for Chronic Pain Assessment and Malingering Detection,” exploring how artificial intelligence can support more accurate and objective reporting of pain symptoms, enabling clinicians to make better-informed decisions.

Led by Dr. Isah and supported through a NSERC Mobilize Seed Fund grant and a Mitacs BSI, the project developed AI models capable of analyzing multiple sources of clinical data. With Karmy Pain Clinics managing approximately 40 monthly cases, many of which are related to insurance claims and legal proceedings, the technology will help the company streamline clinical workflows, reduce assessment time and support fairer outcomes in complex medicolegal cases. By enabling more reliable pain evaluation, the platform will allow patients to receive more appropriate and timely care, while also strengthening the integrity of assessments used in insurance and legal decision-making.

Interested in learning more about how Generator at Sheridan is developing innovative solutions for today’s most pressing challenges? Visit sheridancollege.ca/generator