Summary: AI’s rapid growth in healthcare, particularly in radiology, has sparked environmental concerns, prompting the development of sustainable solutions like energy-efficient AI models, green computing, and renewable energy.
Key Takeaways
- The rapid adoption of AI-based diagnostic systems in healthcare, particularly radiology, has led to environmental concerns.
- A review by researchers highlights the energy consumption of AI systems, carbon emissions from data centers, and electronic waste issues in the medical field.
- Proposed solutions include developing energy-efficient AI models, implementing green computing, and using renewable energy to ensure sustainable deployment of AI in healthcare.
————————————————————————————————————————————————
Similar to other sectors, the rapid development of artificial intelligence (AI) has entered healthcare, particularly radiology. AI-based diagnostic systems are flourishing, with hospitals quickly adopting the technology to assist radiologists. However, concerns about the environmental impact of increasingly complex AI models and the need for sustainable AI solutions have arisen.
Associate Professor Daiju Ueda, PhD, of Japan’s Osaka Metropolitan University‘s Graduate School of Medicine, and a member of the Japan Radiological Society, led a research team to investigate AI’s environmental costs. This review included key members of the Japan Radiological Society and medical researchers, discussing AI systems’ energy consumption in the medical field, carbon emissions from data centers, and electronic waste issues.
Specific solutions to mitigate these environmental impacts include the development of energy-efficient AI models, green computing, and the use of renewable energy. The review also proposes measures for the sustainable deployment of AI in the medical field. These guidelines are crucial for medical institutions, policymakers, and AI developers to operate AI systems responsibly.
“AI has the potential to improve healthcare quality, but its environmental impact cannot be ignored. The best practices we recommended are the first steps toward balancing these factors,” says Ueda. “The challenge for the future will be to verify and further elaborate these recommendations in actual medical practice. They are also expected to contribute to the standardization of methods for assessing AI’s environmental impact and developing an international regulatory framework.”
