One of the biggest--ad most lucrative-applications of artificial intelligence（AI）is in health care.And the capacity of ai to diagnose or predict disease risk is developing rapidly.In recent weeks researchers have unveiled AI models that scan retinal images to predict eye-and cardiovascular-disease risk,and that analyse mammograms to detect breast cancer.Some ai tools have already found their way into clinical practiceaI diagnostics have the potential to improve the delivery and effectiveness of health care.Many are a triumph for science,representing years of improvements in computing power and the neural networks that underlie deep learning.In this form of Al,computers process hundreds of thousands of labelled disease images,until they can classify the images unaided.In reports,researchers conclude that an algorithm is successful if it can identify a particular condition from such images as effectively as can pathologists and radiologists.But that alone does not mean the ai diagnostic is ready for the clinic.Many reports are best viewed as analogous to studies showing that a drug kills a pathogen in a Petri dish.Such studies are exciting but scientific process demands that the methods and materials be described in detail,and that the study is replicated and the drug tested in a progression of studies culminating in large clinical trials.This does not seem to be happening enough in ai diagnostics.Many in the field complain that too many developers are not taking the studies far enough.They are not applying the evidence-based approaches that are established in mature fields,such as drug development These details matter.For instance,one investigation published last year found that an model detected breast cancer in whole slide images better than did 11 pathologists who were allowed assessment times of about one minute per image.However,a pathologist given unlimited time performed as well as al,and found difficult-to-detect cases more often than the computers Some issues might not appear until the tool is applied.For example,a diagnostic algorithm might incorrectly associate images produced using a particular device with a disease--but only because,during the training process,the clinic using that device saw more people with the disease than did another clinic uSing a different device These problems can be overcome.One way is for doctors who deploy aI diagnostic tools in the clinic to track results and report them so that retrospective studies expose any deficiencies.better yet such tools should be developed rigorously-trained on extensive data and validated in controlled studies that undergo peer review.This is slow and difficult,in part because privacy concerns can make it hard for researchers to access the massive amounts of medical data needed.A News story in Nature discusses one possible answer:researchers are building blockchain-based systems to encourage patients to securey share information.At present,human oversight will probably prevent weaknesses in ai diagnosis from being a matter of life or death.That is why regulatory bodies,such as the US Food and Drug Administration,allow doctors to pilot technologies classified as low risk But lack of rigour does carry immediate risks the hype-fail cycle could discourage others from investing in similar techniques that might be better.Sometimes,in a competitive field such as al,a well-publicized set of results can be enough to stop rivals from entering the same field Slow and careful research is a better approach.Backed by reliable data and robust methods,it may take longer,and will not churn out as many crowd-pleasing announcements.But it could prevent deaths and change lives
例如,去年发表的一项调査发现,在整个幻灯片图像中,人工智能模型检测出乳腺癌的效果比11位病理学家好。这11位病理学家有大约1分钟的时间对每张图像进行评估。解析本句为复合句,本句主干为one invesgath,found that,.ublished last year为后置定语,修饰i investigation;an AI model detected breast cancer in whole slide images better than did 11 pathologists为found的宾语从句;who were allowed assessment times of about one minute per image为定语从句,修饰先行词pathologists than后面引导的为倒装句。为了保持句子的平衡,从句还可以用全部或部分倒装,即:than+助动词+主语。例如:In addition,far more Japanese workers expressed dissatisfaction with their Jobs than did their counterparts in the 10 other countries surveyed译文:另外,据调查,不满意自己工作的日本员工比另外10个国家的员工人数要多得多。
