Healthy AI: Advancing Sustainable Artificial Intelligence for Healthcare

The rapid advancement of artificial intelligence (AI)  has reached the healthcare sector, particularly radiology, where AI-driven diagnostic systems are increasingly being adopted to support radiologists. However, this surge in AI technology raises concerns about its environmental impact, prompting the need for more sustainable AI practices.

Associate Professor Daiju Ueda from Osaka Metropolitan University's Graduate School of Medicine, a member of the Japan Radiological Society, spearheaded a research review focusing on the environmental implications of AI. This review involved key figures from the Japan Radiological Society and medical researchers who examined the energy consumption of AI systems, the carbon footprint of data centers, and issues related to electronic waste.

The review proposed several solutions to address these environmental concerns, including the development of energy-efficient AI models, adoption of green computing practices, and the use of renewable energy sources. Additionally, it outlined strategies for the sustainable implementation of AI in healthcare settings.

These guidelines are crucial for medical institutions, policymakers, and AI developers to ensure environmentally responsible AI operations.


"AI has the potential to significantly enhance healthcare quality, but we must not overlook its environmental impact," emphasized Professor Ueda. "The best practices we recommend are initial steps towards achieving a balance between these benefits and environmental considerations. The future challenge will be to test and refine these practices in real-world medical settings. These efforts are also anticipated to aid in establishing standardized methods for evaluating AI's environmental impact and developing an international regulatory framework."

The review highlighted several key areas of concern:

Energy Consumption: AI systems, particularly those used in healthcare, require substantial computational power, leading to high energy consumption. This is especially true for deep learning models, which need extensive training and continuous updates.

Carbon Footprint: Data centers that support AI operations contribute significantly to carbon emissions. These centers require constant cooling and maintenance, which adds to their environmental impact.

Electronic Waste: The rapid evolution of AI technology leads to frequent hardware upgrades, resulting in increased electronic waste. This waste often contains hazardous materials that can harm the environment.


To mitigate these impacts, the review suggested:

Energy-Efficient AI Models: Developing AI models that require less computational power without compromising performance.

Green Computing Practices: Implementing practices that reduce energy consumption, such as optimizing algorithms and using energy-efficient hardware.

Renewable Energy Sources: Powering data centers with renewable energy to reduce their carbon footprint.

Sustainable Implementation: Ensuring that AI systems are deployed in a manner that minimizes environmental impact, including proper disposal and recycling of electronic waste.


Original source: https://www.sciencedaily.com/releases/2024/08/240802132709.htm

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