The past, present and future role of artificial intelligence in imaging


      • AI can act as a competent second reader of images and reduce error rate.
      • AI is limited by a high false positive rate and an inability to display reasoning.
      • Future avenues lie in use of patient records to generate diagnoses.


      Artificial intelligence (AI) is already widely employed in various medical roles, and ongoing technological advances are encouraging more widespread use of AI in imaging. This is partly driven by the recognition of the significant frequency and clinical impact of human errors in radiology reporting, and the promise that AI can help improve the reliability as well the efficiency of imaging interpretation. AI in imaging was first envisioned in the 1960s, but initial attempts were limited by the technology of the day. It was the introduction of artificial neural networks and AI based computer aided detection (CAD) software in the 1980s that marked the advent of widespread integration of AI within radiology reporting. CAD is now routinely used in mammography, with consistent evidence of equivalent or improved lesion detection, with small increases in recall rates. Significant false positive rates remain a limitation for CAD, although these have markedly improved in the last decade. Other challenges include the difficulty clinicians encounter in trying to understand the reasoning of an AI system, which may limit their confidence in its advice, and a question mark hangs over who should be liable if CAD makes an error. The future integration of CAD with PACS promises the development of more comprehensively intelligent systems that can identify multiple, challenging diagnoses, and a move towards more individualised patient outcome predictions based upon AI analysis.


      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic and Personal
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'


      Subscribe to European Journal of Radiology
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect


        • Simmons A.B.
        • Chappell S.G.
        Artificial intelligence-definition and practice.
        IEEE J. Ocean. Eng. 1988; 13: 14-42
        • Minsky M.
        Steps toward artificial intelligence.
        Proc. IRE. 1961; 49: 8-30
        • Kahn C.E.
        Artificial intelligence in radiology: decision support systems.
        RadioGraphics. 1994; 14: 849-861
        • Miller R.A.
        Medical diagnostic decision support systems--past, present, and future: a threaded bibliography and brief commentary.
        J. Am. Med. Inform. Assoc. 1994; 1: 8-27
        • Siegel E.
        Artificial intelligence and diagnostic radiology: not quite ready to welcome our computer overlords.
        Appl. Radiol. 2012; 41: 8
        • Amato F.
        • López A.
        • Peña-Méndez E.M.
        • Vaňhara P.
        • Hampl A.
        • Havel J.
        Artificial neural networks in medical diagnosis.
        J. Appl. Biomed. 2013; 11: 47-58
        • Krupinski E.A.
        The future of image perception in radiology.
        Acad. Radiol. 2003; 10: 1-3
        • Garland L.H.
        On the scientific evaluation of diagnostic procedures.
        Radiology. 1949; 52: 309-328
        • Borgstede J.P.
        • Lewis R.S.
        • Bhargavan M.
        • Sunshine J.H.
        RADPEER quality assurance program: a multifacility study of interpretive disagreement rates.
        J. Am. Coll. Radiol. 2004; 1: 59-65
        • Kim Y.W.
        • Mansfield L.T.
        Fool me twice: delayed diagnoses in radiology with emphasis on perpetuated errors.
        Am. J. Roentgenol. 2014; 202: 465-470
        • Robinson P.J.
        Radiology’s Achilles’ heel: error and variation in the interpretation of the Röntgen image.
        Br. J. Radiol. 1997; 70: 1085-1098
        • Renfrew D.L.
        • Franken E.A.
        • Berbaum K.S.
        • Weigelt F.H.
        • Abu-Yousef M.M.
        Error in radiology: classification and lessons in 182 cases presented at a problem case conference.
        Radiology. 1992; 183: 145-150
        • Pinto A.
        Spectrum of diagnostic errors in radiology.
        World J. Radiol. 2010; 2: 377
        • Wakeley C.J.
        • Jones A.M.
        • Kabala J.E.
        • Prince D.
        • Goddard P.R.
        Audit of the value of double reading magnetic resonance imaging films.
        Br. J. Radiol. 1995; 68: 358-360
        • Quekel L.G.B.A.
        • Kessels A.G.H.
        • Goei R.
        • van Engelshoven J.M.A.
        Miss rate of lung cancer on the chest radiograph in clinical practice.
        Chest. 1999; 115: 720-724
        • Scott W.J.
        • Howington J.
        • Feigenberg S.
        • Movsas B.
        • Pisters K.
        Treatment of non-small cell lung cancer stage I and stage II.
        Chest. 2007; 132: 234S-242S
        • Lodwick G.S.
        • Haun C.L.
        • Smith W.E.
        • Keller R.F.
        • Robertson E.D.
        Computer diagnosis of primary bone tumors.
        Radiology. 1963; 80: 273-275
        • Winsberg F.
        • Elkin M.
        • Macy J.
        • Bordaz V.
        • Weymouth W.
        Detection of radiographic abnormalities in mammograms by means of optical scanning and computer analysis.
        Radiology. 1967; 89: 211-215
        • Kruger R.P.
        • Townes J.R.
        • Hall D.L.
        • Dwyer S.J.
        • Lodwick G.S.
        Automated radiographic diagnosis via feature extraction and classification of cardiac size and shape descriptors.
        IEEE Trans. Biomed. Eng. BME. 1972; 19: 174-186
        • Meyers P.H.
        • Nice C.M.
        • Becker H.C.
        • Nettleton W.J.
        • Sweeney J.W.
        • Meckstroth G.R.
        Automated computer analysis of radiographic images.
        Radiology. 1964; 83: 1029-1034
        • Ramesh A.N.
        • Kambhampati C.
        • Monson J.R.T.
        • Drew P.J.
        Artificial intelligence in medicine.
        Ann. Roy. Coll. Surg. Engl. 2004; 86: 334-338
        • Ding S.
        • Li H.
        • Su C.
        • Yu J.
        • Jin F.
        Evolutionary artificial neural networks: a review.
        Artif. Intell. Rev. 2013; 39: 251-260
      1. U.S.F.a.D. Administration, Guidance Documents (Medical Devices and Radiation-Emitting Products) - Guidance for Industry and FDA Staff - Clinical Performance Assessment: Considerations for Computer-Assisted Detection Devices Applied to Radiology Images and Radiology Device Data - Premarket Approval (PMA) and Premarket Notification [510(k)] Submissions, in: O.o.I.V.D.D.E.a. Safety (Ed.) 2012.

        • Doi K.
        Computer-aided diagnosis in medical imaging: historical review, current status and future potential.
        Comput. Med. Imaging Graph. 2007; 31: 198-211
        • Castellino R.A.
        Computer aided detection (CAD): an overview.
        Cancer Imaging. 2005; 5: 17-19
        • Morton M.J.
        • Whaley D.H.
        • Brandt K.R.
        • Amrami K.K.
        Screening mammograms: interpretation with computer-aided detection—prospective evaluation.
        Radiology. 2006; 239: 375-383
        • Dean J.C.
        • Ilvento C.C.
        Improved cancer detection using computer-aided detection with diagnostic and screening mammography: prospective study of 104 cancers.
        Am. J. Roentgenol. 2006; 187: 20-28
        • Ko J.M.
        • Nicholas M.J.
        • Mendel J.B.
        • Slanetz P.J.
        Prospective assessment of computer-aided detection in interpretation of screening mammography.
        Am. J. Roentgenol. 2006; 187: 1483-1491
        • Songyang Y.
        • Ling G.
        A CAD system for the automatic detection of clustered microcalcifications in digitized mammogram films.
        IEEE Trans. Med. Imaging. 2000; 19: 115-126
        • Nishikawa R.M.
        Current status and future directions of computer-aided diagnosis in mammography.
        Comput. Med. Imaging Graph. 2007; 31: 224-235
        • Georgian-Smith D.
        • Moore R.H.
        • Halpern E.
        • Yeh E.D.
        • Rafferty E.A.
        • D’Alessandro H.A.
        • Staffa M.
        • Hall D.A.
        • McCarthy K.A.
        • Kopans D.B.
        Blinded comparison of computer-aided detection with human second reading in screening mammography.
        Am. J. Roentgenol. 2007; 189: 1135-1141
        • Taylor P.
        • Potts H.W.W.
        Computer aids and human second reading as interventions in screening mammography: Two systematic reviews to compare effects on cancer detection and recall rate.
        Eur. J. Cancer. 2008; 44: 798-807
        • Gilbert F.J.
        • Astley S.M.
        • McGee M.A.
        • Gillan M.G.C.
        • Boggis C.R.M.
        • Griffiths P.M.
        • Duffy S.W.
        Single reading with computer-aided detection and double Reading of screening mammograms in the United Kingdom national breast screening program.
        Radiology. 2006; 241: 47-53
        • Gilbert F.J.
        • Astley S.M.
        • Gillan M.G.
        • Agbaje O.F.
        • Wallis M.G.
        • James J.
        • Boggis C.R.
        • Duffy S.W.
        CADET II: a prospective trial of computer-aided detection (CAD) in the UK breast screening programme.
        J. Clin. Oncol. 2008; 26 (508-508)
        • Goddard P.
        • Leslie A.
        • Jones A.
        • Wakeley C.
        • Kabala J.
        Error in radiology.
        Br. J. Radiol. 2001; 74: 949-951
        • Heuvers M.E.
        • Hegmans J.P.
        • Stricker B.H.
        • Aerts J.G.
        Improving lung cancer survival; Time to move on.
        BMC Pulm. Med. 2012; 12
        • Li F.
        • Sone S.
        • Abe H.
        • MacMahon H.
        • Armato S.G.
        • Doi K.
        Lung cancers missed at low-dose helical CT screening in a general population: comparison of clinical, histopathologic, and imaging findings.
        Radiology. 2002; 225: 673-683
        • Kligerman S.
        • Cai L.
        • White C.S.
        The effect of computer-aided detection on radiologist performance in the detection of lung cancers previously missed on a chest radiograph.
        J. Thorac. Imaging. 2013; 28: 244-252
        • Das M.
        • Muhlenbruch G.
        • Mahnken A.H.
        • Flohr T.G.
        • Gundel L.
        • Stanzel S.
        • Kraus T.
        • Gunther R.W.
        • Wildberger J.E.
        Small pulmonary nodules: effect of two computer-aided detection systems on radiologist performance.
        Radiology. 2006; 241: 564-571
        • Yuan R.
        • Vos P.M.
        • Cooperberg P.L.
        Computer-aided detection in screening CT for pulmonary nodules.
        Am. J. Roentgenol. 2006; 186: 1280-1287
        • Liang M.
        • Tang W.
        • Xu D.M.
        • Jirapatnakul A.C.
        • Reeves A.P.
        • Henschke C.I.
        • Yankelevitz D.
        Low-dose CT screening for lung cancer: computer-aided detection of missed lung cancers.
        Radiology. 2016; 281: 279-288
        • Sahiner B.
        • Chan H.-P.
        • Hadjiiski L.M.
        • Cascade P.N.
        • Kazerooni E.A.
        • Chughtai A.R.
        • Poopat C.
        • Song T.
        • Frank L.
        • Stojanovska J.
        • Attili A.
        Effect of CAD on radiologists’ detection of lung nodules on thoracic CT scans: analysis of an observer performance study by nodule size.
        Acad. Radiol. 2009; 16: 1518-1530
        • Shiraishi J.
        • Li Q.
        • Appelbaum D.
        • Doi K.
        Computer-aided diagnosis and artificial intelligence in clinical imaging.
        Semin. Nucl. Med. 2011; 41: 449-462
        • Shiraishi J.
        • Appelbaum D.
        • Pu Y.
        • Li Q.
        • Pesce L.
        • Doi K.
        Usefulness of temporal subtraction images for identification of interval changes in successive whole-body bone scans: JAFROC analysis of radiologists’ performance.
        Acad. Radiol. 2007; 14: 959-966
        • Yang S.K.
        • Moon W.K.
        • Cho N.
        • Park J.S.
        • Cha J.H.
        • Kim S.M.
        • Kim S.J.
        • Im J.-G.
        Screening mammography–detected cancers: sensitivity of a computer-aided detection system applied to full-field digital mammograms.
        Radiology. 2007; 244: 104-111
        • Friedemann B.
        • Uwe F.
        • Karim B.
        • Serge M.
        • Silivia O.
        • Eckhardt G.
        Computer Aided Detection (CAD) in Direct Digital Full Field Mammography, Digital Mammography.
        Springer nature, 2003: 253-256
        • Lee N.
        • Laine A.F.
        • Marquez G.
        • Levsky J.M.
        • Gohagan J.K.
        Potential of computer-aided diagnosis to improve CT lung cancer screening.
        IEEE Rev. Biomed. Eng. 2009; 2: 136-146
        • Armato S.G.
        • Giger M.L.
        • Moran C.J.
        • Blackburn J.T.
        • Doi K.
        • MacMahon H.
        Computerized detection of pulmonary nodules on CT scans.
        RadioGraphics. 1999; 19: 1303-1311
        • Yongbum L.
        • Hara T.
        • Fujita H.
        • Itoh S.
        • Ishigaki T.
        Automated detection of pulmonary nodules in helical CT images based on an improved template-matching technique.
        IEEE Trans. Med. Imaging. 2001; 20: 595-604
        • Suzuki K.
        • Armato S.G.
        • Li F.
        • Sone S.
        • Doi K.
        Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography.
        Med. Phys. 2003; 30: 1602-1617
        • Farag A.A.
        • El-Baz A.
        • Gimel’farb G.
        • El-Ghar M.A.
        • Eldiasty T.
        Springer Nature.
        Quantitative Nodule Detection in Low Dose Chest CT Scans: New Template Modeling and Evaluation for CAD System Design, Lecture Notes in Computer Science. 2005: 720-728
        • Messay T.
        • Hardie R.C.
        • Rogers S.K.
        A new computationally efficient CAD system for pulmonary nodule detection in CT imagery.
        Med. Image Anal. 2010; 14: 390-406
        • de Hoop B.
        • De Boo D.W.
        • Gietema H.A.
        • van Hoorn F.
        • Mearadji B.
        • Schijf L.
        • van Ginneken B.
        • Prokop M.
        • Schaefer-Prokop C.
        Computer-aided detection of lung cancer on chest radiographs: effect on observer performance.
        Radiology. 2010; 257: 532-540
        • Setio A.A.A.
        • Ciompi F.
        • Litjens G.
        • Gerke P.
        • Jacobs C.
        • van Riel S.J.
        • Wille M.M.W.
        • Naqibullah M.
        • Sanchez C.I.
        • van Ginneken B.
        Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks.
        IEEE Trans. Med. Imaging. 2016; 35: 1160-1169
        • Teach R.L.
        • Shortliffe E.H.
        An analysis of physician attitudes regarding computer-based clinical consultation systems.
        Comput. Biomed. Res. 1981; 14: 542-558
        • Pinto A.
        • Brunese L.
        • Pinto F.
        • Reali R.
        • Daniele S.
        • Romano L.
        The concept of error and malpractice in radiology.
        Semin. Ultrasound CT MRI. 2012; 33: 275-279
        • Guerriero C.
        • Gillan M.G.C.
        • Cairns J.
        • Wallis M.G.
        • Gilbert F.J.
        Is computer aided detection (CAD) cost effective in screening mammography? A model based on the CADET II study.
        BMC Health Serv. Res. 2011; 11
      2. IBM Makes a Quantum Processor Available for Use Online.
        Physics Today, 2016
        • Doi K.
        Current status and future potential of computer-aided diagnosis in medical imaging.
        Br. J. Radiol. 2005; 78: s3-s19
        • Wolf M.
        • Krause J.
        • Carney P.A.
        • Bogart A.
        • Kurvers R.H.J.M.
        collective intelligence meets medical decision-making: the collective outperforms the best radiologist.
        PLOS One. 2015; 10e0134269
        • Palmer D.W.
        • Piraino D.W.
        • Obuchowski N.A.
        • Bullen J.A.
        Springer Nature.
        Emergent Diagnoses from a Collective of Radiologists: Algorithmic Versus Social Consensus Strategies, Lecture Notes in Computer Science. 2014: 222-229
        • Dubey R.B.
        • Hanmandlu M.
        Integration of CAD into PACS Institute of Electrical and Electronics Engineers (IEEE), 2012.
        2nd International Conference on Power, Control and Embedded Systems. 2012;
        • Le A.H.T.
        • Liu B.
        • Huang H.K.
        Integration of computer-aided diagnosis/detection (CAD) results in a PACS environment using CAD–PACS toolkit and DICOM SR.
        Int. J. Comput. Assist. Radiol. Surg. 2009; 4: 317-329
        • Dayhoff J.E.
        • DeLeo J.M.
        Artificial neural networks: opening the black box.
        Cancer. 2001; 91: 1615-1635