Community and Values
In the Biomedical Communications Graduate Program, we have a commitment to academic excellence and innovation in health communication and visualization. As a graduate program that spans two campuses we try to foster a sense of community among students whether they find themselves on the St. George campus or the Mississauga campus.
“We believe that by challenging our student community, we can achieve the greatest impact.”
The goal of a university education is to help you develop as an individual intellectually, emotionally, and socially. To do so it’s important to take advantage of the many opportunities available to you. Students have the opportunity to become involved in the Institute of Medical Science Students’ Association (IMSSA) and also to participate in the Annual IMS Scientific Day. BMC students work closely with IMS graduate students in the design and publication of a student initiated quarterly magazine.
Our students collaborate with University of Toronto medical students in the annual publication of Toronto Notes, a comprehensive medical reference for the Medical Council of Canada Qualifying Exam. Students may also become involved with the graduate community at UTM (UTMAGS). Our program offers work opportunities for students who are interested in participating in our work-study program, or gaining valuable teaching experience as a teaching assistant for one of our undergraduate courses.
The Biomedical Communications graduate program aspires to provide education and scholarship of the highest quality — to advance the frontiers of knowledge and to prepare students for leadership roles in our profession.
Emerging Technologies
The MScBMC program has a long history of exploring and integrating new technologies into its curriculum and guiding professional practice in new directions. In the 1990s, we were at the forefront of the digital revolution, rapidly integrating digital image making tools, like Photoshop and Illustrator, and promoting the use of 3d modelling, animation and rendering software in our curriculum. Thirty years later, we are on the cusp of what will likely create another great shift in the way we make visual communication media: Artificial Intelligence (AI). MScBMC’s faculty is taking a thoughtful, proactive approach to understanding AI technologies, promoting debate and discussion about their use, and discovering approaches to ethically integrate these tools into our workflows and curriculum. Our hope is that these tools can be harnessed by medical illustration professionals, making their practices more efficient, and ultimately permitting to produce more and higher quality communication media that serves ever growing and diverse audiences.
While the medical illustration profession will be affected by AI tools, we believe that medical illustration as a field remains relatively insulated because of its emphasis on scientific accuracy, accountability, and specialized expertise that current AI tools are not yet able to replicate.
In the sections that follow, we outline the factors that contribute to this resilience and consider the field’s future outlook.
Why medical illustration is relatively insulated from AI
Unlike many other forms of visual communication, medical illustration demands scientific accuracy. This demand for rigour, accountability, and specialized expertise places it beyond the current capabilities of AI systems. In addition, there are a number of conceptual and practical impediments to the wholesale adoption of generative AI output in our space.
CORE COMPETENCIES
Accuracy and Scientific Rigour - Medical illustration is defined by its uncompromising need for scientific accuracy. While modern AI image generators can create visually appealing images, they routinely fail at depicting even basic anatomy, let alone complex biological systems, physiological mechanisms, and surgical processes that comprise much of the medical illustration field. Inaccuracies in these areas can undermine both credibility and safety. For clients and practitioners, this diminishes both the trustworthiness and the value of AI-generated outputs compared to works crafted by trained medical illustrators.
Interpretation of Complex, Incomplete, or Novel Information - Much of a medical illustrator’s work goes far beyond drawing what has already been depicted. Medical illustrators are often asked to visualize novel phenomena: new surgical approaches; emerging diseases; or hypothesized or recently discovered processes that occur at microscopic or molecular scales. In other words, medical illustrators are often called upon to visualize subjects or processes for which no reference image exists. Generative AI tools, which rely on patterns in existing training data, struggle with these tasks: they cannot reliably depict something that has never been documented or that requires integrating multiple incomplete datasets.
Visual Storytelling - Medical illustration is more than making realistic pictures; it is about telling a clear visual story that supports communication and learning. A skilled illustrator decides what to highlight, simplify, or leave out so that the viewer can quickly grasp the key concept. They control composition, colour, labels, and sequence to guide the eye, manage cognitive load, and build understanding step by step. Generative AI can produce realistic images, but it usually lacks the intentional narrative structure that makes a medical image educationally effective.
Empathy and Audience-Centred Design - Many medical illustration projects involve communicating with specialized or vulnerable audiences, such as patients or their caregivers. Effective communication requires empathy, cultural sensitivity, audience research, and tailored design strategies, qualities and processes that remain uniquely human strengths. AI tools have yet to demonstrate effectiveness in these areas.
LEGAL AND PROFESSIONAL RESPONSIBILITIES
Copyright Limitations - Most copyright authorities, including those in Canada and the U.S., currently hold that AI-generated images lack copyright protection because they are not the product of human authorship. This creates a practical barrier for clients: they may be unwilling to use visuals that fall into the public domain and could be reused or modified by competitors.
Accountability and Liability - Pharmaceutical, biotech, and healthcare clients require expert accountability for the materials they distribute. If an AI-generated visualization is found to be misleading, the legal liability could be significant, potentially outweighing any potential cost savings of using AI-generated content.
BROADER CONCERNS AROUND AI ADOPTION
In addition to field-specific issues, there are broader concerns that influence whether and how AI should be used:
Ethical concerns: Many AI systems have been trained using copyrighted materials (artwork, text, and images) scraped from the internet without creators’ consent or compensation. Although some newer models are trained on ethically sourced or licensed datasets, these remain the exception.
Job displacement: AI tools, when used for cost-cutting, may displace creative professionals from compensated work.
Environmental impact: Large-scale AI models often consume significant energy resources, raising concerns about sustainability and climate impact