酿造
通才与专种
人工智能
数据科学
机器学习
计算机科学
化学
食品科学
生物
发酵
生态学
栖息地
出处
期刊:Nature
[Springer Nature]
日期:2023-10-24
卷期号:622 (7984): 686-688
被引量:9
标识
DOI:10.1038/d41586-023-03302-0
摘要
his radiology residency at the University of Alabama at Birmingham near the peak of what he calls the field's "AI scare".It was 2018, just two years after computer scientist Geoffrey Hinton had proclaimed that people should stop training to be radiologists because machine-learning tools would soon displace them.Hinton, sometimes referred to as the godfather of artificial intelligence (AI), predicted that these systems would soon be able to read and interpret medical scans and X-rays better than people could.A substantial drop in applications for radiology programmes followed."People were worried that they were going to finish residency and just wouldn't have a job," Perchik says.Hinton had a point.AI-based tools are increasingly part of medical care; more than 500 have been authorized by the US Food and Drug Administration (FDA) for use in medicine.Most are related to medical imaging -used for enhancing images, measuring abnormalities or flagging test results for follow-up.But even seven years after Hinton's prediction, radiologists are still very much in demand.And clinicians, for the most part, seem underwhelmed by the performance of these technologies.Surveys show that although many AN AI REVOLUTION IS BREWING IN MEDICINE. WHAT WILL IT LOOK LIKE?Emerging generalist models could overcome some limitations of firstgeneration machine-learning tools for clinical use.By Mariana LenharoResearchers are feeding machine-learning tools millions of medical scans to give them general diagnostic capabilities.
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