AI and sustainability in radiology

Jul 23, 2025

Dr. Kate Hanneman, a cardiac radiologist at the University of Toronto, discusses the intersection of artificial intelligence and sustainability in healthcare. She argues that AI can optimize radiology processes, cut costs, and enhance patient outcomes while minimizing environmental impact. The conversation reveals how eliminating data redundancy and establishing centralized data management is vital for sustainable AI practices. Together with experts, they explore responsible AI use to balance technological advancement and global resource preservation in healthcare.

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INSIGHT

Broad View of Sustainability

Environmental sustainability in healthcare includes preserving cost, energy, human capital, and reducing waste and pollution.
AI can improve clinical decision making to reduce unnecessary procedures, lowering waste and resource usage.

ADVICE

Avoid Data Redundancy

Avoid redundant data storage and AI training efforts by collaborating across groups internationally.
Shared frameworks expedite AI development and reduce resource waste from duplicated work.

ADVICE

Speed AI Training with Foundation Models

Use foundation models to speed up AI training, reducing energy consumption by 60 to 80%.
Efficient training processes are crucial even if renewable energy powers data centers.

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Is it possible for artificial intelligence to make healthcare more environmentally sustainable?

In the field of radiology, AI may prove to be transformative. It has the potential to streamline processes, cut costs, and increase accuracy in imaging, diagnostics, and prevention.

When we talk about sustainability, we often think of carbon emissions or climate change. But environmental sustainability means something much broader in healthcare: it’s about how we can preserve resources, whether cost, energy, human capital, and reduce waste and pollution, all while improving health outcomes for everyone, everywhere.

Today, host Vibhas Deshpande, Head of Sustainability Innovation and Research, Americas at Siemens Healthineers, welcomes Dr. Kate Hanneman, cardiac radiologist at the University of Toronto; Dr. Charles Goh, Chief Medical Informatics Officer at Singapore General Hospital; and Tobias Heimann, head of the Department of Artificial Intelligence Germany at Siemens Healthineers, to explore how the rise of artificial intelligence is reshaping the conversation around sustainability in healthcare.

Together, they’re helping define what “responsible AI” really means—and what steps healthcare leaders can take now to ensure that AI benefits patients, clinicians and health systems and preserves global resources.

What you’ll learn in this episode:

Why eliminating data redundancy and using foundational models are keys to keeping AI development sustainable
AI is used to shorten imaging exams while still maintaining a high image quality, which contributes to energy savings
AI can help to arrive at correct diagnosis by improving clinical decision making and alignment to best practice to minimize waste in health systems
Sustainability is more than a metric of emissions or kilowatts—it’s also about how we invest intellectual and clinical capital, such as freeing clinicians to do what humans can do best: connect, interpret, and care.

Connect with Vibhas Deshpande

Connect with Kate Hanneman

Connect with Charles Goh

Connect with Tobias Heimann

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