U-M BME Faculty Pioneer Online Educational Modules on AI Foundations
The development of these online learning modules marks the first step in a broader vision to create a comprehensive educational program focused on AI in BME.
Through an innovative initiative that U-M Biomedical Engineering is developing, new online educational modules focusing on Artificial Intelligence (AI) and machine learning are set to transform the learning landscape for students and professionals. These modules aim to integrate evolving technology with practical biomedical applications, merging technology with education to prepare students for the future of a technologically changing world.
The development of these online learning modules marks the first step in a broader vision to create a comprehensive educational program focused on AI in BME. “This project began as a small, reusable step toward a potential AI-focused master’s program,” said Sriram Chandrasekaran, Associate Professor, Biomedical Engineering. “We wanted to develop online modules that offer flexibility across different courses and focus on core AI fundamentals essential for our students.”
The modules range from foundational lessons, such as linear algebra and statistics, to more complex AI model-building techniques. Crucially, they are designed not just as a theoretical series, but as practical, hands-on experience. “We aim to bridge the gap between concept and application, making these modules particularly relevant for BME data,” emphasized Anne Draelos, Assistant Professor, Biomedical Engineering and Computational Medicine & Bioinformatics. “This focus on immediate applicability sets our offering apart from typical online lectures.”
Zhongming Liu, Associate Professor, Biomedical Engineering and Associate Department Chair of Graduate Programs, echoed the importance of providing a problem-driven approach in the modules. “Teaching subjects like linear algebra in isolation can feel dry,” he said. “By starting with a real-world problem, students can see how the math and the algorithms we teach apply directly to the solutions.”
The modules are crafted to address a variety of BME-specific challenges, such as applications in neuroscience, imaging, genomics, and beyond. “For example, our introductory courses include diverse AI applications from genomics to health records, showing students the wide-ranging impact of AI in BME,” explained Dr. Chandrasekaran.
One significant aspect of this initiative is its adaptability across different student educational backgrounds. As Dr. Draelos noted, “We assume very little initial knowledge for the basic modules, making them accessible to a broad range of students.” This inclusivity ensures students from varied backgrounds, whether deeply familiar with biology, coding, or just venturing into AI, can benefit from knowledge-sharing as they build their expertise.
The future application of these modules holds even more promise. New modules can be developed and recorded as AI and machine learning technology evolves, allowing faculty to address emerging trends without skipping a beat. While currently available online for public use, there are potential plans for more formalized, course-focused offerings, possibly even as formal degree-guided coursework. As Dr. Chandrasekaran noted, “We’re exploring the potential for a certificate program where alumni and working professionals could update their skills at their own pace.”
This collaborative effort has already piqued interest beyond campus. “A BME External Advisory Board member expressed interest in sharing our modules with their employees,” Dr. Liu remarked, hinting at the broader applicability and recognition of the modules’ value in industry and professional settings.
The faculty acknowledges the challenges posed by rapidly evolving AI technologies, especially with tools such as ChatGPT becoming prevalent in educational contexts. “There’s a philosophical debate on how much students should use AI tools like ChatGPT to complete coursework,” said Dr. Chandrasekaran. “While these tools offer great potential, understanding the fundamentals is key to using them effectively.”
As this educational initiative evolves, the collaborative efforts of BME faculty are positioned not only to enhance BME’s academic framework, but also to foster a community of learners adept in AI and machine learning and their vast applications in biomedical engineering. As Dr. Draelos noted, “AI tools won’t replace BME professionals. Instead, they empower us to tackle problems with new perspectives,” she said. “The modules can offer the fundamentals, but each biomedical challenge is unique. It requires someone who deeply understands both the biomedical and AI information, and can draw upon their experience and knowledge to find a solution.”