Wolf Risa M, Abramoff Michael D, Channa Roomasa, Tava Chris, Clarida Warren, Lehmann Harold P
Department of Pediatric Endocrinology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Department of Ophthalmology, University of Iowa, Iowa City, IA, USA.
NPJ Digit Med. 2022 May 12;5(1):62. doi: 10.1038/s41746-022-00605-w.
Healthcare is a large contributor to greenhouse gas (GHG) emissions around the world, given current power generation mix. Telemedicine, with its reduced travel for providers and patients, has been proposed to reduce emissions. Artificial intelligence (AI), and especially autonomous AI, where the medical decision is made without human oversight, has the potential to further reduce healthcare GHG emissions, but concerns have also been expressed about GHG emissions from digital technology, and AI training and inference. In a real-world example, we compared the marginal GHG contribution of an encounter performed by an autonomous AI to that of an in-person specialist encounter. Results show that an 80% reduction may be achievable, and we conclude that autonomous AI has the potential to reduce healthcare GHG emissions.
鉴于当前的发电组合,医疗保健是全球温室气体(GHG)排放的一大贡献因素。远程医疗减少了医疗服务提供者和患者的出行,有人提议以此来减少排放。人工智能(AI),尤其是自主人工智能(即在无人监督的情况下做出医疗决策),有可能进一步减少医疗保健领域的温室气体排放,但也有人对数字技术以及人工智能训练和推理过程中的温室气体排放表示担忧。在一个实际案例中,我们比较了自主人工智能进行一次诊疗与面对面专家诊疗的边际温室气体贡献。结果表明,有可能实现80%的减排,我们得出结论,自主人工智能有潜力减少医疗保健领域的温室气体排放。