As AI is increasingly used in radiology, researchers caution that it’s essential to consider the environmental impact of AI tools.
Health care and medical imaging significantly contribute to the greenhouse gas (GHG) emissions fueling global climate change. AI tools can improve both the practice of and sustainability in radiology through optimized imaging protocols resulting in shorter scan times, improved scheduling efficiency to reduce patient travel, and the integration of decision-support tools to reduce low-value imaging. But there is a downside to AI utilization.
Medical imaging generates a lot of greenhouse gas emissions, but we often don’t think about the environmental impact of associated data storage and AI tools. The development and deployment of AI models consume large amounts of energy, and the data storage needs in medical imaging and AI are growing exponentially.
AI offers the potential to improve workflows, accelerate image acquisition, reduce costs and improve the patient experience. However, the energy required to develop AI tools and store the associated data significantly contributes to GHG.
We need to do a balancing act, bridging to the positive effects while minimizing the negative impacts. Improving patient outcomes is our ultimate goal, but we want to do that while using less energy and generating less waste.
Summary of how artificial intelligence (AI) in radiology has a negative impact on the environment, with key opportunities and actions to improve sustainability using AI in radiology.
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