Artificial intelligence, or AI, is rapidly reshaping biomedical engineering, from early disease detection to real-time health monitoring and clinical decision support.
At Arizona State University, the School of Biological and Health Systems Engineering, part of the Ira A. Fulton Schools of Engineering, is expanding its role in this space through new faculty hires and a graduate degree focused on intelligent biomedical systems.
Engineering leaders are strengthening both research and curriculum across engineering, computing and the health sciences to prepare the next generation of engineers to build trustworthy, human-centered AI for health care. This effort is part of a broader strategic focus across the Fulton Schools to drive AI and data-driven engineering.
This year, the school welcomed assistant professors Asif Salekin and Ziyi Huang, whose research sits at the intersection of AI and health. Their arrival aligns with the launch of the new Master of Science in artificial intelligence engineering with a concentration in intelligent biomedical systems engineering and reflects a coordinated effort to grow both research and education in AI-enabled health care.
Building trustworthy, human-centered AI
Huang’s research focuses on developing trustworthy AI models and theoretical frameworks that enable reliable, explainable and responsible predictions. Her work spans risk-aware decision-making, agentic AI and machine learning theory, with an emphasis on building robust multimodal models capable of handling complex and dynamic data.
She develops frameworks that translate AI outputs into decisions clinicians and operators can use, addressing a persistent challenge in health systems where data is often noisy or incomplete.
“My goal is to build AI systems that people can genuinely trust,” Huang says. “In health care, especially, it’s not enough for a model to be accurate. It must be transparent, reliable across populations and capable of supporting human decision-makers in meaningful ways.”
Her long-term vision is to create AI systems that complement human expertise through reason and decision-making, ultimately improving outcomes across diverse communities.
Salekin directs the Laboratory for Ubiquitous and Intelligent Sensing, or UIS, Lab, where his research integrates human-centered AI, machine learning and ubiquitous sensing technologies to address pressing health and behavioral challenges. His work combines multimodal data streams, including wearables, speech, physiological signals and medical imaging, to understand complex human states outside traditional clinical settings.
A central focus of Salekin’s research is developing ethical and algorithmically fair, formally verified, robust, scalable, interpretable and privacy-aware digital health systems that are deployable in real-world environments.
Applications include mental health monitoring, opioid craving detection, speech and sensorimotor disorders, chronic and neurological diseases, and cancer risk stratification and outcome prediction.
“We are at a point where AI can move from being a research tool to becoming a deployable, real-world health solution,” Salekin says. “My work focuses on building systems that are not only technically strong, but ethical, interpretable and clinically actionable — systems that can truly support patients and providers.”
His research has been supported by the National Science Foundation, National Institutes of Health and industry, including OpenAI. His work has appeared in leading computing venues such as IMWUT/UbiComp, AAAI Applied Intelligence and INTERSPEECH, as well as high-impact peer-reviewed journals, including Nature Molecular Psychiatry and PNAS.
“Artificial intelligence is transforming biomedical engineering at every level — from molecular discovery to population health,” says Heather Clark, a professor and the director of the School of Biological and Health Systems Engineering. “These strategic hires reflect our commitment to advancing trustworthy, human-centered AI while leveraging ASU’s strengths in engineering, health and data science. With strong institutional support and cross-campus collaboration, we are building a research ecosystem that will drive meaningful impact.”
The addition of Huang and Salekin strengthens the school’s efforts to embed AI across research thrusts and foster interdisciplinary collaboration with clinicians, data scientists and industry partners.
Preparing students for intelligent biomedical systems
The new master’s degree program prepares students to design and apply AI in biomedical contexts, combining systems that are technically rigorous, ethically grounded and clinically relevant.
The curriculum integrates machine learning, biomedical data science, sensing systems, signal processing and translational engineering. Students gain both theoretical depth and hands-on experience applying AI to real-world health challenges.
“AI is no longer an elective skillset in biomedical engineering — it is foundational,” says David Brafman, associate director of academic excellence in the School of Biological and Health Systems Engineering. “We are intentionally embedding AI throughout our curriculum while also launching a dedicated graduate program that prepares students to build intelligent biomedical systems from the ground up. This degree reflects where the field is heading and where our students need to be.”
Together, these faculty hires and the new graduate degree underscore the school’s investment in shaping the future of biomedical engineering through AI.
By advancing trustworthy AI research and training the next generation of engineers, the School of Biological and Health Systems Engineering is positioning ASU at the forefront of intelligent, equitable and human-centered health care innovation.
Emmanuelle Compton contributed to this story.
