Will Artificial Intelligence Replace Radiologists?
2019; Radiological Society of North America; Volume: 1; Issue: 3 Linguagem: Inglês
10.1148/ryai.2019190058
ISSN2638-6100
Autores Tópico(s)Radiomics and Machine Learning in Medical Imaging
ResumoHomeRadiology: Artificial IntelligenceVol. 1, No. 3 Next EditorialFree AccessWill Artificial Intelligence Replace Radiologists?Curtis P. Langlotz Curtis P. Langlotz Author AffiliationsFrom the Department of Radiology, Stanford University, 300 Pasteur Dr, Room H1330D, Stanford, CA 94305.Address correspondence to the author (e-mail: [email protected]).Curtis P. Langlotz Published Online:May 15 2019https://doi.org/10.1148/ryai.2019190058MoreSectionsPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In The question of whether Machines Can Think is about as relevant as the question of whether Submarines Can Swim.– Edsger Dijkstra, 1984IntroductionAs computer performance on complex vision tasks approaches that of clinical experts, radiologists look over their shoulders. Some of the ebullience for these new systems arises from the allure of creating beings in our own image (1). But the excitement is powered primarily by real innovation (2). Medical image analysis projects that once took years now can be completed in a matter of days or weeks. These huge leaps in computer vision have inspired dreams of health care transformation. But the extreme optimism often overshoots reality and dissipates during scientific “winters” of disregard (3). Artificial intelligence (AI) in radiology has, so far, followed this script.The hype peaked in the year 2016: An oncologist and key architect of the Affordable Care Act predicted in the New England Journal of Medicine that “machine learning will displace much of the work of radiologists and anatomical pathologists” (4). Two Oxford economists indicated in the Harvard Business Review that machines will replace doctors because “when professional work is broken down into component parts, many of the tasks involved turn out to be routine and process-based. They do not in fact call for judgment, creativity, or empathy” (5). A luminary Stanford computer scientist and founder of the Google Brain Deep Learning Project forecasted in The Economist that radiologists would be replaced by AI sooner than their executive assistants (6). An AI pioneer who recently won the Association for Computing Machinery Turing Award opined, “We should stop training radiologists now” (7). Scientists crowed; radiologists cowered; ventures capitalized.Many of these experts have since revised their thinking (8). Some now collaborate with radiologists to develop AI algorithms (9). But the eager pronouncements initially gave pause to medical students considering their specialty choices and spurred many radiologists to check their retirement accounts. The effect of computer vision on patient care is still mostly illusory, impeded by the scarcity of training data and the sluggish march to regulatory approval. No strangers to innovation, radiologists have confronted this supposed awful adversary, only to find what seems to be an amiable apprentice. As we ponder whether winter is coming for radiologists or for AI, the following parables may foretell the change of seasons.Computer-aided Detection for Mammography: A Cautionary TaleConcerns in the 1990s about the variable quality of mammography interpretation (10) led to two key steps forward: (a) the Breast Imaging Reporting and Data System (BI-RADS), arguably the most influential advance in the history of radiology communication (11), and (b) legislation to provide additional reimbursement for the use of AI to help radiologists detect breast cancer on mammograms. Radiologists flocked to purchase and deploy these computer-aided detection (CAD) systems (12). Recent persuasive evidence suggests that CAD systems have had no appreciable effect on the accuracy of radiologists (13). Perhaps the high rate of false-positive findings led to alert fatigue (14).The recent rush of novel AI algorithms should prompt introspection about past failures of AI to live up to its promise. Today’s AI tools have achieved regulatory clearance based on their performance at a small number of health care organizations. Perhaps the incremental accuracy of these new AI methods will reduce false-positive findings and blunt the “cry wolf” effect, but the generalizability of these algorithms to the diversity of radiology practices remains an open question.Radiologists Master New TechnologyAs early as 1896, William Morton, a neurologist who popularized the use of x-rays in the United States, partnered with Edwin Hammer, an engineer who had mastered the electrical generators needed to produce the current for x-rays (15). Similar partnerships between clinicians and engineers were forged over each cycle of radiology innovation, including the advent of US, CT, and MRI.When the first MRI devices were demonstrated, some speculated on the demise of radiologists. The high-contrast images made abnormalities obvious. As the theory went, patients would emerge from the imaging unit with clear results that could be managed by primary care physicians. Instead, we learned that these complex machines require extensive configuration to ensure the acquired images resolve the differential diagnosis. And the professionals interpreting the images must fathom how a device functions to distinguish artifact from reality (16). Training of radiologists began to incorporate MRI physics, now a mainstay of radiology residency. Radiologists can’t construct an MRI device any more than a pilot can build an airplane. But radiologists learn to protect patients from the machine’s weaknesses. As AI rises, organized radiology snaps into action once again. Radiologists are being trained to recognize AI’s shortcomings and capitalize on its strengths (17).Radiologists Know “The Long Tail”We often compare AI algorithms to radiology experts based on the ability to identify a single disease (18) or a small set of diseases (19). These assessments dramatically oversimplify what radiologists do. A comprehensive catalog of radiology diagnoses lists nearly 20 000 terms for disorders and imaging observations and over 50 000 causal relations (20). An AI algorithm that diagnoses common chest conditions at the level of a subspecialty thoracic radiologist is a major step forward, an incredible asset to underserved regions, and could serve as a valued assistant for a subspecialty radiologist. But human radiologists are also trained to detect uncommon diseases in the long tail of the distribution, including rheumatoid arthritis, sickle cell disease, and posttransplantation lymphoproliferative disorder. AI is impressive in identifying horses but is a long way from recognizing zebras.Even the simple act of measuring AI against radiologists, rather than measuring how AI might augment the performance of radiologists, perpetuates a misperception of AI’s likely clinical role. Since the advent of diagnostic clinical decision support systems, human-machine collaborations have performed better than either one alone (21). Studies of radiology AI systems are no different (9).The Mirage of Job DisplacementTo illustrate the overreaction to technology’s role in job displacement, venture capitalist Mary Meeker lists New York Times cry-wolf headlines from the past century (22): “March of the Machine Makes Idle Hands” (February 26, 1928); “Does Machine Displace Men in the Long Run?” (February 25, 1940); “200,000 Will Lose Jobs to Automation, U.S. Aides Say” (May 5, 1962); “A Robot is After Your Job” (September 3, 1980); “Will Robots Take Our Children’s Jobs” (December 11, 2017). And yet, the steady march of increased employment continues (6).Bank tellers are often cited as the canonical example of a job replaced by technology. But reliable studies of the industry show no such effect (23). In 1985, the United States had 60 000 automated teller machines (ATMs) and 485 000 bank tellers. In 2002, there were 352 000 ATMs and 527 000 bank tellers. The U.S. Bureau of Labor Statistics counted 600 500 bank tellers in 2008 and projects that this number grew to 638 000 in 2018 (24). Instead, bank tellers’ responsibilities advanced from the drudgery of withdrawals and deposits at the bank window to more interesting and sophisticated transactions.An Autopilot for RadiologistsPilots must assimilate a torrent of information from a plane’s sensors and from their own senses to make decisions on which human lives depend. Cockpits are designed to mitigate human failings and to complement the skills of pilots. Avionics digest complex information for easy human consumption. Displays and controls nudge pilots toward safety and warn against dangerous interventions. Pilots maintain equanimity because electronic monitors alert pilots to anomalous conditions. Tedious or repetitive tasks are handled by an autopilot. And yet, when a sensor malfunctions, a properly trained human pilot can look out the window and countermand the system.The vision for AI in radiology looks much like a cockpit (25). Detection algorithms will solve “needle in a haystack” search problems, finding breast calcifications and lung nodules. Registration and segmentation tools will relieve the tedium of measuring and plotting the time course of liver metastases. Anatomic measurement apps will plot organ volume against the normal range. Classification routines will assist in resolving diagnostic dilemmas. And so AI will elevate the cognitive universe of radiologists to the top of their license—exercising judgment, creativity, and empathy as they interpret images in partnership with AI algorithms and patients (26).Transformation of Radiology WorkAlthough the danger of AI to radiologists is overblown, the new medical computer vision industry will profoundly change how radiologists practice, most likely in a direction that pleases radiologists. And AI has the potential to democratize radiology by enabling nonradiologists in underserved areas to tap into subspecialty expertise, perhaps on their mobile devices. But the ethereal notion of an artificial general intelligence destined to replace us is just as fanciful today as attaching human qualities to submarines. As we are lifted by the latest AI bubble, “Will AI replace radiologists?” is the wrong question. The right answer is: Radiologists who use AI will replace radiologists who don’t.Disclosures of Conflicts of Interest: C.P.L. Activities related to the present article: received consulting fee for service on the advisory board of whiterabbit.ai; received stock options for consulting and service on the advisory board of whiterabbit.ai, Nines.ai, GalileoCDS, and Bunker Hill; research contracts for AI-related research from GE, Philips, and Siemens; editorial board member for Radiology: Artificial Intelligence. Activities not related to the present article: disclosed no relevant relationships. Other relationships: disclosed no relevant relationships.AcknowledgmentThe author would like to thank David Larson, MD, MBA, and Matthew Lungren, MD, MPH, for their comments on an earlier version of the manuscript.References1. Russell S, Dewey D, Tegmark M. Research priorities for robust and beneficial artificial intelligence. AI Mag 2015;36(4). Google Scholar2. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015;521(7553):436–444. Crossref, Medline, Google Scholar3. AI Winter. https://en.wikipedia.org/wiki/AI_winter. Accessed April 7, 2019. Google Scholar4. Obermeyer Z, Emanuel EJ. Predicting the future: big data, machine learning, and clinical medicine. N Engl J Med 2016;375(13):1216–1219. Crossref, Medline, Google Scholar5. Susskind R, Susskind D. Technology will replace many doctors, lawyers, and other professionals. Harvard Business Review. https://hbr.org/2016/10/robots-will-replace-doctors-lawyers-and-other-professionals. Published October 11, 2016. Accessed April 7, 2019. Google Scholar6. Automation and anxiety: Will smarter machines cause mass unemployment? The Economist. https://www.economist.com/special-report/2016/06/25/automation-and-anxiety. Published June 25, 2016. Accessed April 7, 2019. Google Scholar7. Hinton G. On radiology. https://www.youtube.com/watch?v=2HMPRXstSvQ. Accessed April 7, 2019. Google Scholar8. Hinton G. Deep learning: a technology with the potential to transform health care. JAMA 2018;320(11):1101–1102. Crossref, Medline, Google Scholar9. Bien N, Rajpurkar P, Ball RL, et al. Deep-learning-assisted diagnosis for knee magnetic resonance imaging: development and retrospective validation of MRNet. PLoS Med 2018;15(11):e1002699. Crossref, Medline, Google Scholar10. Beam CA, Layde PM, Sullivan DC. Variability in the interpretation of screening mammograms by US radiologists: findings from a national sample. Arch Intern Med 1996;156(2):209–213. Crossref, Medline, Google Scholar11. Langlotz CP. ACR BI-RADS for breast imaging communication: a roadmap for the rest of radiology. J Am Coll Radiol 2009;6(12):861–863. Crossref, Medline, Google Scholar12. Fenton JJ, Foote SB, Green P, Baldwin LM. Diffusion of computer-aided mammography after mandated Medicare coverage. Arch Intern Med 2010;170(11):987–989. Crossref, Medline, Google Scholar13. Lehman CD, Wellman RD, Buist DS, et al. Diagnostic accuracy of digital screening mammography with and without computer-aided detection. JAMA Intern Med 2015;175(11):1828–1837. Crossref, Medline, Google Scholar14. Nishikawa RM, Bae KT. Importance of better human-computer interaction in the era of deep learning: mammography computer-aided diagnosis as a use case. J Am Coll Radiol 2018;15(1 Pt A):49–52. Crossref, Medline, Google Scholar15. Langlotz CP. The radiology report: a guide to thoughtful communication for radiologists and other medical professionals. CreateSpace: Boston, Mass, 2015. Google Scholar16. Morelli JN, Runge VM, Ai F, et al. An image-based approach to understanding the physics of MR artifacts. RadioGraphics 2011;31(3):849–866. Link, Google Scholar17. Chokshi FH, Flanders AE, Prevedello LM, Langlotz CP. Fostering a healthy AI ecosystem for radiology: conclusions of the 2018 RSNA summit on AI in radiology. Radiol Artif Intell 2019;1:190021. Link, Google Scholar18. Lakhani P, Sundaram B. Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology 2017;284(2):574–582. Link, Google Scholar19. Rajpurkar P, Irvin J, Ball RL, et al. Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLoS Med 2018;15(11):e1002686. Crossref, Medline, Google Scholar20. Budovec JJ, Lam CA, Kahn CE Jr. Radiology gamuts ontology: differential diagnosis for the Semantic Web. RadioGraphics 2014;34(1):254–264. Link, Google Scholar21. Bankowitz RA, McNeil MA, Challinor SM, Parker RC, Kapoor WN, Miller RA. A computer-assisted medical diagnostic consultation service. Implementation and prospective evaluation of a prototype. Ann Intern Med 1989;110(10):824–832. Crossref, Medline, Google Scholar22. Meeker M. Internet Trends Report 2018. https://www.kleinerperkins.com/perspectives/internet-trends-report-2018/. Published 2018. Accessed April 7, 2019. Google Scholar23. Bessen J. Learning by doing: the real connection between innovation, wages, and wealth. New Haven, Conn: Yale University Press, 2015. Google Scholar24. Are ATMs stealing jobs? The Economist. Published June 15, 2011. Google Scholar25. Krupinski E, Bronkalla M, Folio L, et al. Advancing the diagnostic cockpit of the future: an opportunity to improve diagnostic accuracy and efficiency. Acad Radiol 2019;26(4):579–581. Crossref, Medline, Google Scholar26. Choy G, Khalilzadeh O, Michalski M, et al. Current applications and future impact of machine learning in radiology. Radiology 2018;288(2):318–328. Link, Google ScholarArticle HistoryReceived: Apr 9 2019Revision requested: Apr 16 2019Revision received: Apr 16 2019Accepted: Apr 17 2019Published online: May 15 2019 FiguresReferencesRelatedDetailsCited ByTuberculosis Detection from Chest Radiographs: Stop Training Radiologists NowBram van Ginneken, 6 September 2022 | Radiology, Vol. 306, No. 1Great debates in cardiac computed tomography: OPINION: “Artificial intelligence is key to the future of CCTA – The great hope”ManishMotwani, Michelle C.Williams, KoenNieman, Andrew D.Choi2023 | Journal of Cardiovascular Computed Tomography, Vol. 17, No. 1Clinical Implementation of a Combined Artificial Intelligence and Natural Language Processing Quality Assurance Program for Pulmonary Nodule Detection in the Emergency Department SettingJoseph J.Cavallo, Irenede Oliveira Santo, Jonathan L.Mezrich, Howard P.Forman2023 | Journal of the American College of RadiologyLarge language models (LLM) and ChatGPT: what will the impact on nuclear medicine be?Ian L.Alberts, LorenzoMercolli, ThomasPyka, GeorgePrenosil, KuangyuShi, AxelRominger, AliAfshar-Oromieh2023 | European Journal of Nuclear Medicine and Molecular ImagingArtificial Intelligence (AI)-Based Systems for Automatic Skeletal Maturity Assessment through Bone and Teeth Analysis: A Revolution in the Radiological Workflow?ElenaCaloro, MaurizioCè, DanieleGibelli, AndreaPalamenghi, CarloMartinenghi, GiancarloOliva, MichaelaCellina2023 | Applied Sciences, Vol. 13, No. 6The Cases for and against Artificial Intelligence in the Medical School CurriculumBrandon Ngo, Diep Nguyen, Eric vanSonnenberg, 17 August 2022 | Radiology: Artificial Intelligence, Vol. 4, No. 5Contemporary Medical ImagingMarlyvan Assen, EmanueleMuscogiuri, GiovanniTessarin, Carlo N.De Cecco2022Integrity of Scientific ResearchTugbaAkinci D’Antonoli2022Brain Informatics and HealthNathanLloyd, Arjab SinghKhuman2022Künstliche Intelligenz (KI) in der Radiologie?DavidBonekamp, H.-P.Schlemmer2022 | Der Urologe, Vol. 61, No. 4Autonomous artificial intelligence in pediatric radiology: the use and perception of BoneXpert for bone age assessmentHans HenrikThodberg, BenjaminThodberg, JoannaAhlkvist, Amaka C.Offiah2022 | Pediatric Radiology, Vol. 52, No. 7European Society of Paediatric Radiology Artificial Intelligence taskforce: a new taskforce for the digital ageLene BjerkeLaborie, JaishreeNaidoo, ErikaPace, PierluigiCiet, ChristineEade, Matthias W.Wagner, Thierry A. G. M.Huisman, Susan C.Shelmerdine2022 | Pediatric RadiologyDiagnostic captioning: a surveyJohnPavlopoulos, VasilikiKougia, IonAndroutsopoulos, DimitrisPapamichail2022 | Knowledge and Information Systems, Vol. 64, No. 7Diagnostic charting of panoramic radiography using deep-learning artificial intelligence systemMelikeBaşaran, ÖzerÇelik, Ibrahim SevkiBayrakdar, ElifBilgir, KaanOrhan, AlperOdabaş, Ahmet FarukAslan, RohanJagtap2022 | Oral Radiology, Vol. 38, No. 3Eudaimonia and the Future RadiologistMayankGoyal, RosalieMcDonough2022 | Academic Radiology, Vol. 29, No. 6Artificial Intelligence and Positron Emission Tomography Imaging WorkflowCherylBeegle, NavidHasani, RobertoMaass-Moreno, BabakSaboury, EliotSiegel2022 | PET Clinics, Vol. 17, No. 1Artificial intelligence for oral and maxillo-facial surgery: A narrative reviewSimonRasteau, DidierErnenwein, CharlesSavoldelli, PierreBouletreau2022 | Journal of Stomatology, Oral and Maxillofacial Surgery, Vol. 123, No. 3Dificultades en la implantación de la inteligencia artificial en la práctica radiológica: lo que el radiólogo necesita saberA.V.Nair, S.Ramanathan, P.Sathiadoss, A.Jajodia, D. BlairMacdonald2022 | Radiología, Vol. 64, No. 4Artificial intelligence and machine learning in cancer imagingDow-MuKoh, NickolasPapanikolaou, UlrichBick, RowlandIlling, Charles E.Kahn, JayshreeKalpathi-Cramer, CelsoMatos, LuisMartí-Bonmatí, AnneMiles, Seong KiMun, SandyNapel, AndreaRockall, EvisSala, NicolaStrickland, FredPrior2022 | Communications Medicine, Vol. 2, No. 12022 IEEE 7th International conference for Convergence in Technology (I2CT)N. U. A.Sasmitha, H. K. G. V.Wathasha, P. P. L.Guruge, W. J. T.Silva, LakmalRupasinghe, G. W. D. A.Gunarathne2022Employing deep convolutional neural networks for segmenting the medial retropharyngeal lymph nodes in CT studies of dogsDavidSchmid, Volkher B.Scholz, Patrick R.Kircher, Ines E.Lautenschlaeger2022 | Veterinary Radiology & Ultrasound, Vol. 63, No. 6The medical profession transformed by artificial intelligence: Qualitative studyLinaMosch, DanielFürstenau, JennyBrandt, JasperWagnitz, Sophie AIKlopfenstein, Akira-SebastianPoncette, FelixBalzer2022 | DIGITAL HEALTH, Vol. 8An evaluation of information online on artificial intelligence in medical imagingPhilipMulryan, NaomiNi Chleirigh, Alexander T.O’Mahony, ClaireCrowley, DavidRyan, PatrickMcLaughlin, MarkMcEntee, MichaelMaher, Owen J.O’Connor2022 | Insights into Imaging, Vol. 13, No. 1AI in breast screening mammography: breast screening readers' perspectivesClarisse Florencede Vries, Samantha J.Colosimo, MoraghBoyle, GeraldLip, Lesley A.Anderson, Roger T.Staff, D.Harrison, C.Black, A.Murray, K.Wilde, J. D.Blackwood, C.Butterly, J.Zurowski, J.Eilbeck, C.McSkimming2022 | Insights into Imaging, Vol. 13, No. 1Medical Students’ Knowledge and Attitude Towards Artificial Intelligence: An Online SurveyMouna M.Al Saad, AminShehadeh, SalemAlanazi, MonerahAlenezi, AhmadAbu alez, HanaEid, Mohammed SaifAlfaouri, SultanAldawsari, RawanAlenezi2022 | The Open Public Health Journal, Vol. 15, No. 1Growth trends for selected occupations considered at risk from automationMichaelHandel2022 | Monthly Labor ReviewArtificial Intelligence Applications in Health Care Practice: Scoping ReviewMalvikaSharma, CarlSavage, MonikaNair, IngridLarsson, PetraSvedberg, Jens MNygren2022 | Journal of Medical Internet Research, Vol. 24, No. 10Artificial intelligence in veterinary medicineRyan B.Appleby, Parminder S.Basran2022 | Journal of the American Veterinary Medical Association, Vol. 260, No. 8Clinician's guide to trustworthy and responsible artificial intelligence in cardiovascular imagingLilianaSzabo, ZahraRaisi-Estabragh, AhmedSalih, CelesteMcCracken, EsmeraldaRuiz Pujadas, PolyxeniGkontra, MateKiss, PalMaurovich-Horvath, HajnalkaVago, BelaMerkely, Aaron M.Lee, KarimLekadir, Steffen E.Petersen2022 | Frontiers in Cardiovascular Medicine, Vol. 9Emergency Teleradiology-Past, Present, and, Is There a Future?AnjaliAgrawal2022 | Frontiers in Radiology, Vol. 2Artificial Intelligence in Orthopedic Radiography Analysis: A Narrative ReviewKennethChen, ChristophStotter, ThomasKlestil, StefanNehrer2022 | Diagnostics, Vol. 12, No. 9Artificial Intelligence in Emergency Radiology: Where Are We Going?MichaelaCellina, MaurizioCè, GiovanniIrmici, VelioAscenti, ElenaCaloro, LorenzoBianchi, GiuseppePellegrino, NataschaD’Amico, SergioPapa, GianpaoloCarrafiello2022 | Diagnostics, Vol. 12, No. 12A Survey on the Use of Artificial Intelligence by Clinicians in Dentistry and Oral and Maxillofacial SurgeryTimEschert, FalkSchwendicke, JoachimKrois, LaurenBohner, ShankeethVinayahalingam, MarcelHanisch2022 | Medicina, Vol. 58, No. 8Artificial intelligence: Advances and new frontiers in medical imagingMarc RFromherz, Mina SMakary2022 | Artificial Intelligence in Medical Imaging, Vol. 3, No. 2AI: The Next Generation Radiology Extenders?AmineKorchi2022 | Applied RadiologyTrust in artificial intelligence: From a Foundational Trust Framework to emerging research opportunitiesRomanLukyanenko, WolfgangMaass, Veda C.Storey2022 | Electronic Markets, Vol. 32, No. 4RSNA-MICCAI Panel Discussion: 2. Leveraging the Full Potential of AI—Radiologists and Data Scientists Working TogetherMarius George Linguraru, Lena Maier-Hein, Ronald M. Summers, Charles E. Kahn, Jr, 27 October 2021 | Radiology: Artificial Intelligence, Vol. 3, No. 6Do We Expect More from Radiology AI than from Radiologists?Maciej A. Mazurowski, 17 March 2021 | Radiology: Artificial Intelligence, Vol. 3, No. 4Imaging Informatics for Healthcare ProfessionalsDanielPinto dos Santos2021Integrating artificial intelligence into radiology practice: undergraduate students’ perspectiveArosh S.Perera Molligoda Arachchige, AfanasySvet2021 | European Journal of Nuclear Medicine and Molecular Imaging, Vol. 48, No. 13Practical applications of deep learning: classifying the most common categories of plain radiographs in a PACS using a neural networkThomasDratsch, MichaelKorenkov, DavidZopfs, SebastianBrodehl, BettinaBaessler, DanielGiese, SebastianBrinkmann, DavidMaintz, DanielPinto dos Santos2021 | European Radiology, Vol. 31, No. 4An international survey on AI in radiology in 1,041 radiologists and radiology residents part 1: fear of replacement, knowledge, and attitudeMerelHuisman, ErikRanschaert, WilliamParker, DomenicoMastrodicasa, MartinKoci, DanielPinto de Santos, FrancescaCoppola, SergeyMorozov, MarcZins, CedricBohyn, UralKoç, JieWu, SatyamVeean, DominikFleischmann, TimLeiner, Martin JWillemink2021 | European Radiology, Vol. 31, No. 9Hiding beyond plain sight: Textural analysis of positron emission tomography to identify high-risk plaques in carotid atherosclerosisManishMotwani2021 | Journal of Nuclear Cardiology, Vol. 28, No. 5Artificial intelligence in radiation oncology: A review of its current status and potential application for the radiotherapy workforceC.Parkinson, C.Matthams, K.Foley, E.Spezi2021 | Radiography, Vol. 27Exploring the Role of Artificial Intelligence in an Emergency and Trauma Radiology DepartmentSabeenaJalal, WilliamParker, DuncanFerguson, SavvasNicolaou2021 | Canadian Association of Radiologists Journal, Vol. 72, No. 1Artificial intelligence for reduced dose 18F-FDG PET examinations: a real-world deployment through a standardized framework and business case assessmentKatiaKatsari, DanielePenna, VincenzoArena, GiuliaPolverari, AnnaritaIanniello, DomenicoItaliano, RolandoMilani, AlessandroRoncacci, Rowland O.Illing, EttorePelosi2021 | EJNMMI Physics, Vol. 8, No. 1Barriers and Enablers for Artificial Intelligence in Dental Diagnostics: A Qualitative StudyAnneMüller, Sarah MarieMertens, GerdGöstemeyer, JoachimKrois, FalkSchwendicke2021 | Journal of Clinical Medicine, Vol. 10, No. 8Artificial Intelligence in Low- and Middle-Income Countries: Innovating Global Health RadiologyDaniel J. Mollura, Melissa P. Culp, Erica Pollack, Gillian Battino, John R. Scheel, Victoria L. Mango, Ameena Elahi, Alan Schweitzer, Farouk Dako, 6 October 2020 | Radiology, Vol. 297, No. 3AI Hype and Radiology: A Plea for Realism and AccuracyJohn Banja, 1 July 2020 | Radiology: Artificial Intelligence, Vol. 2, No. 4Promises of artificial intelligence in neuroradiology: a systematic technographic reviewAllard W.Olthof, Peter M.A.van Ooijen, Mohammad H.Rezazade Mehrizi2020 | Neuroradiology, Vol. 62, No. 10Implementation and design of artificial intelligence in abdominal imagingHailey H.Choi, Silvia D.Chang, Marc D.Kohli2020 | Abdominal Radiology, Vol. 45, No. 12Artificial intelligence in radiology: the ecosystem essential to improving patient careJulieSogani, BibbAllen, KeithDreyer, GeraldineMcGinty2020 | Clinical Imaging, Vol. 59, No. 1Artificial Intelligence: A Private Practice PerspectiveNinaKottler2020 | Journal of the American College of Radiology, Vol. 17, No. 11Interventional radiology and artificial intelligence in radiology: Is it time to enhance the vision of our medical students?PierreAuloge, JulienGarnon, Joey MarieRobinson, SarahDbouk, JeanSibilia, MarcBraun, DominiqueVanpee, GuillaumeKoch, Roberto LuigiCazzato, AfshinGangi2020 | Insights into Imaging, Vol. 11, No. 1Artificial Intelligence in Radiology—Ethical ConsiderationsAdrian P.Brady, EmanueleNeri2020 | Diagnostics, Vol. 10, No. 4The Philosophy of Expertise in the Age of Medical Informatics: How Healthcare Technology is Transforming Our Understanding of Expertise and Expert Knowledge?MarcinRządeczka2020 | Studies in Logic, Grammar and Rhetoric, Vol. 63, No. 1A bird’s-eye view of deep learning in bioimage analysisErikMeijering2020 | Computational and Structural Biotechnology Journal, Vol. 18Artificial Intelligence and the Medical Radiation Profession: How Our Advocacy Must Inform Future PracticeAndrewMurphy, BrianLiszewski2019 | Journal of Medical Imaging and Radiation Sciences, Vol. 50, No. 4Impact of artificial intelligence on radiology: a EuroAIM survey among members of the European Society of Radiology2019 | Insights into Imaging, Vol. 10, No. 1Recommended Articles Evaluating AI Clinically—It’s Not Just ROC AUC!Radiology2020Volume: 298Issue: 1pp. 47-48Mandating Limits on Workload, Duty, and Speed in RadiologyRadiology2022Volume: 304Issue: 2pp. 274-282Electronic Health Record Closed-Loop Communication Program for Unexpected Nonemergent FindingsRadiology2021Volume: 301Issue: 1pp. 123-130Overview of Noninterpretive Artificial Intelligence Models for Safety, Quality, Workflow, and Education Applications in Radiology PracticeRadiology: Artificial Intelligence2022Volume: 4Issue: 2Shoulder Arthroplasty, from Indications to Complications: What the Radiologist Needs to KnowRadioGraphics2016Volume: 36Issue: 1pp. 192-208See More RSNA Education Exhibits Artificial Intelligence for the Average Intelligence: A Practical GuideDigital Posters2018Artificial Intelligence in Breast Imaging: Past, Present, and FutureDigital Posters2020Urban Mobile Mammography: Bringing Mammography to the UnderservedDigital Posters2020 RSNA Case Collection Post vaccination axillary adenopathyRSNA Case Collection2021Posterior dislocation of the shoulderRSNA Case Collection2020Poland SyndromeRSNA Case Collection2022 Vol. 1, No. 3 Metrics Downloaded 16,774 times Altmetric Score PDF download
Referência(s)