The unintended consequences of artificial intelligence in paediatric radiology

被引:0
|
作者
Ciet, Pierluigi [1 ,2 ]
Eade, Christine [3 ]
Ho, Mai-Lan [4 ]
Laborie, Lene Bjerke [5 ,6 ]
Mahomed, Nasreen [7 ]
Naidoo, Jaishree [8 ,9 ]
Pace, Erika [10 ]
Segal, Bradley [7 ]
Toso, Seema [11 ]
Tschauner, Sebastian [12 ]
Vamyanmane, Dhananjaya K. [13 ]
Wagner, Matthias W. [14 ,15 ,16 ]
Shelmerdine, Susan C. [17 ,18 ,19 ,20 ]
机构
[1] Erasmus MC, Sophias Childrens Hosp, Dept Radiol & Nucl Med, Rotterdam, Netherlands
[2] Univ Cagliari, Dept Med Sci, Cagliari, Italy
[3] Royal Cornwall Hosp Trust, Truro, Cornwall, England
[4] Univ Missouri, Columbia, MO USA
[5] Haukeland Hosp, Sect Paediat, Dept Radiol, Bergen, Norway
[6] Univ Bergen, Dept Clin Med, Bergen, Norway
[7] Univ Witwatersrand, Dept Radiol, Johannesburg, South Africa
[8] Dr J Naidoo Inc, Paediat Diagnost Imaging, Johannesburg, South Africa
[9] Envisionit Deep Ltd, Coveham House,Downside Bridge Rd, Cobham, England
[10] Royal Marsden NHS Fdn Trust, Dept Diagnost Radiol, London, England
[11] Univ Hosp Geneva, Childrens Hosp, Pediat Radiol, Geneva, Switzerland
[12] Med Univ Graz, Div Paediat Radiol, Dept Radiol, Graz, Austria
[13] Indira Gandhi Inst Child Hlth, Dept Pediat Radiol, Bangalore, India
[14] Hosp Sick Children, Div Neuroradiol, Dept Diagnost Imaging, Toronto, ON, Canada
[15] Univ Toronto, Dept Med Imaging, Toronto, ON, Canada
[16] Univ Hosp Augsburg, Dept Neuroradiol, Augsburg, Germany
[17] Great Ormond St Hosp Children NHS Fdn Trust, Dept Clin Radiol, Great Ormond St, London WC1H 3JH, England
[18] UCL Great Ormond St Inst Child Hlth, Great Ormond St Hosp Children, London, England
[19] NIHR Great Ormond St Hosp Biomed Res Ctr, 30 Guilford St, London, England
[20] St George Hosp, Dept Clin Radiol, London, England
关键词
Artificial intelligence; Child; Machine learning; Radiology; IMAGING DATA; ETHICS;
D O I
暂无
中图分类号
R72 [儿科学];
学科分类号
100202 ;
摘要
Over the past decade, there has been a dramatic rise in the interest relating to the application of artificial intelligence (AI) in radiology. Originally only 'narrow' AI tasks were possible; however, with increasing availability of data, teamed with ease of access to powerful computer processing capabilities, we are becoming more able to generate complex and nuanced prediction models and elaborate solutions for healthcare. Nevertheless, these AI models are not without their failings, and sometimes the intended use for these solutions may not lead to predictable impacts for patients, society or those working within the healthcare profession. In this article, we provide an overview of the latest opinions regarding AI ethics, bias, limitations, challenges and considerations that we should all contemplate in this exciting and expanding field, with a special attention to how this applies to the unique aspects of a paediatric population. By embracing AI technology and fostering a multidisciplinary approach, it is hoped that we can harness the power AI brings whilst minimising harm and ensuring a beneficial impact on radiology practice.
引用
收藏
页码:585 / 593
页数:9
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