A survey on artificial intelligence for reducing the climate footprint in healthcare

Das, Kaushik; Chandra, J

doi:10.1016/j.nexus.2022.100167

Cited by 24 publications

(7 citation statements)

References 47 publications

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“…The next wave of health informatics and digital health solutions being embraced by the healthcare industry is artificial intelligence (AI), which is being offered by several IT suppliers. The current research studies face several restrictions such as a lack of specific artificial intelligence (AI) operations that could not be accessed [36,37]. Figure 3.1 represents the patients can benefit greatly from AI in numerous ways related to healthcare.…”

Section: Methodsmentioning

confidence: 99%

Revolutionizing Healthcare with Smarter AI: In-depth Exploration of Advancements, Challenges, and Future Directions

Bangash,

Khan,

Husnain

et al. 2024

VFAST trans. softw. eng.

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Artificial intelligence (AI) is the main branch of computer science that permits advanced machines to interpret and analyze complex healthcare data elaborating the recent challenges in the medical field of study. The current state of AI applications in healthcare is examined in this systematic literature review, with an emphasis on the technology's accomplishments, difficulties, and potential. The wide breadth of AI technologies used in healthcare settings, such as robots, computer vision, machine learning, and natural language processing, is highlighted in this review through an extensive analysis of peer-reviewed publications. It talks about how customized medicine, predictive analytics, illness detection, and treatment planning are just a few of the areas of healthcare delivery that AI-driven technologies are transforming. According to research by investment bank Goldman Sachs, 300 million full-time employees could be replaced by artificial intelligence (AI). In the US and Europe, it might replace 25% of labor duties, but it might also lead to an increase in productivity and the creation of new jobs. Additionally, it might eventually result in a 7% rise in the global annual value of products and services produced. Additionally, the paper projects that approximately 25% of all employment might be performed totally by AI and that two-thirds of jobs in the U.S. and Europe "are exposed to some degree of AI automation. "The most likely groups to be impacted by workforce automation are educated white-collar workers making up to $80,000 annually, according to research from OpenAI and the University of Pennsylvania. According to a McKinsey Global Institute study, developments in digitalization, robots, and artificial intelligence may require at least 14% of workers worldwide to change jobs by 2030.

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Section: Methodsmentioning

confidence: 99%

Revolutionizing Healthcare with Smarter AI: In-depth Exploration of Advancements, Challenges, and Future Directions

Bangash,

Khan,

Husnain

et al. 2024

VFAST trans. softw. eng.

View full text Add to dashboard Cite

show abstract

“…At the same time, researchers believe the healthcare sector's AI-based mode can lower the growing carbon footprint (Wolf et al, 2022;Das and Chandra, 2023). Chatbots can diminish the carbon footprint of healthcare facilities by decreasing the necessity for patients to commute to hospitals for minor concerns and uncertainties.…”

Section: Chatbot In Medicinementioning

confidence: 99%

Overview of Chatbots with special emphasis on artificial intelligence-enabled ChatGPT in medical science

Chakraborty,

Pal,

Bhattacharya

et al. 2023

Front. Artif. Intell.

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The release of ChatGPT has initiated new thinking about AI-based Chatbot and its application and has drawn huge public attention worldwide. Researchers and doctors have started thinking about the promise and application of AI-related large language models in medicine during the past few months. Here, the comprehensive review highlighted the overview of Chatbot and ChatGPT and their current role in medicine. Firstly, the general idea of Chatbots, their evolution, architecture, and medical use are discussed. Secondly, ChatGPT is discussed with special emphasis of its application in medicine, architecture and training methods, medical diagnosis and treatment, research ethical issues, and a comparison of ChatGPT with other NLP models are illustrated. The article also discussed the limitations and prospects of ChatGPT. In the future, these large language models and ChatGPT will have immense promise in healthcare. However, more research is needed in this direction.

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“…Reducing carbon footprint is crucial. TinyML as green AI is an important tool for realizing climate and environmental policies [29], e.g., overcoming limitations of traditional sensors and monitoring systems [20], superior efficiency and energy saving advantages over traditional machine learning algorithms when running on small devices [64], low power consumption, high efficiency and energy saving capabilities, and low storage costs in environmental radiation-monitoring systems [65,66]. It also offers adaptive unsupervised anomaly detection for extreme environments [67], thus facilitating provision of accurate data for weather forecasting and disaster warnings [68].…”

Section: In Environmental and Climate Governancementioning

confidence: 99%

Synergy of Patent and Open-Source-Driven Sustainable Climate Governance under Green AI: A Case Study of TinyML

Li,

Luo,

Liang

et al. 2023

Sustainability

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Green AI (Artificial Intelligence) and digitalization facilitate the “Dual-Carbon” goal of low-carbon, high-quality economic development. Green AI is moving from “cloud” to “edge” devices like TinyML, which supports devices from cameras to wearables, offering low-power IoT computing. This study attempts to provide a conceptual update of climate and environmental policy in open synergy with proprietary and open-source TinyML technology, and to provide an industry collaborative and policy perspective on the issue, through using differential game models. The results show that patent and open source, as two types of TinyML innovation, can benefit a wide range of low-carbon industries and climate policy coordination. From the case of TinyML, we find that collaboration and sharing can lead to the implementation of green AI, reducing energy consumption and carbon emissions, and helping to fight climate change and protect the environment.

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A survey on artificial intelligence for reducing the climate footprint in healthcare

Cited by 24 publications

References 47 publications

Revolutionizing Healthcare with Smarter AI: In-depth Exploration of Advancements, Challenges, and Future Directions

Revolutionizing Healthcare with Smarter AI: In-depth Exploration of Advancements, Challenges, and Future Directions

Overview of Chatbots with special emphasis on artificial intelligence-enabled ChatGPT in medical science

Synergy of Patent and Open-Source-Driven Sustainable Climate Governance under Green AI: A Case Study of TinyML

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