Enhancing medical-imaging artificial intelligence through holistic use of time-tested key imaging and clinical parameters:Future insights  

作　　者：Prakash Nadkarni Suleman Adam Merchant 

机构地区：[1]College of Nursing,University of Iowa,Iowa City,IA 52242,United States [2]Department of Radiology,LTM Medical College<M General Hospital,Mumbai 400022,Maharashtra,India

出　　处：《Artificial Intelligence in Medical Imaging》2022年第3期55-69,共15页医学影像中的人工智能（英文）

摘　　要：Much of the published literature in Radiology-related Artificial Intelligence(AI)focuses on single tasks,such as identifying the presence or absence or severity of specific lesions.Progress comparable to that achieved for general-purpose computer vision has been hampered by the unavailability of large and diverse radiology datasets containing different types of lesions with possibly multiple kinds of abnormalities in the same image.Also,since a diagnosis is rarely achieved through an image alone,radiology AI must be able to employ diverse strategies that consider all available evidence,not just imaging information.Using key imaging and clinical signs will help improve their accuracy and utility tremendously.Employing strategies that consider all available evidence will be a formidable task;we believe that the combination of human and computer intelligence will be superior to either one alone.Further,unless an AI application is explainable,radiologists will not trust it to be either reliable or bias-free;we discuss some approaches aimed at providing better explanations,as well as regulatory concerns regarding explainability(“transparency”).Finally,we look at federated learning,which allows pooling data from multiple locales while maintaining data privacy to create more generalizable and reliable models,and quantum computing,still prototypical but potentially revolutionary in its computing impact.

关 键 词：Medical imaging Artificial intelligence Federated learning holistic approach Quantum computing Future insights 

分 类 号：P20[天文地球—测绘科学与技术]