연구동향 분석

의료분야에서 인공지능 현황 및 의학교육의 방향

정진섭 2020년

논문상세정보
인용/피인용
' 의료분야에서 인공지능 현황 및 의학교육의 방향' 의 주제별 논문영향력
논문영향력 선정 방법
논문영향력 요약
주제
  • Artificial intelligence
  • Medical education
  • deliveryofhealthcare
동일주제 총논문수 논문피인용 총횟수 주제별 논문영향력의 평균
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' 의료분야에서 인공지능 현황 및 의학교육의 방향' 의 참고문헌

  • Zebra Medical Vision’s solutions now available on Philips IntelliSpace AI workflow suite, enabling more AI capabilities for radiologists
  • Will artificial intelligence replace doctors?
  • Why deep-learning AIs are so easy to fool
    Heaven D [2019]
  • When it comes to gorillas, Google photos remains blind
  • What should medical students know about artificial intelligence in medicine?
    박성호 [2019]
  • What is data science?
    Kelleher JD [2018]
  • What AI means for doctors and doctoring
  • Watson for oncology and breast cancer treatment recommendations : agreement with an expert multidisciplinary tumor board
  • Vascular retinal biomarkers improves the detection of the likely cerebral amyloid status from hyperspectral retinal images
    Sharafi SM [2019]
  • Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs : a cross-sectional study
    Zech JR [2018]
  • Using artificial intelligence(Watson for oncology)for treatment recommendations amongst Chinese patients with lung cancer : feasibility study
    Liu C [2018]
  • Use of a novel artificial intelligence platform on mobile devices to assess dosing compliance in a phase 2 clinical trial in subjects with schizophrenia
    Bain EE [2017]
  • Unpacking the black box in artificial intelligence for medicine
  • U.S. Food and Drug Administration precertification pilot program for digital health software: weighing the benefits and risks
    Lee TT [2018]
  • Tomorrow’s doctors seek training in data science, but will that be enough?
  • The top 12 health chatbots
  • The rise of the data-driven physician
  • The relationship between physician burnout and quality of healthcare in terms of safety and acceptability : a systematic review
    Dewa CS [2017]
  • The potential for artificial intelligence in healthcare
    Davenport T [2019]
  • The neural network zoo
  • The future is now: medical education for the 21st century
  • The frequency of diagnostic errors in outpatient care : estimations from three large observational studies involving US adult populations
    Singh H [2014]
  • The first computational theory of mind and brain: a close look at Mcculloch and Pitts's “logical calculus of ideas immanent in nervous activity”
    Piccinini G [2004]
  • The fate of medicine in the time of AI
    Coiera E [2018]
  • The Topol Review
  • The Perceptron : A Probabilistic Model for Information Storage and Organization in The Brain
  • The Apple Watch can detect atrial fibrillation: so what now?
    Marcus GM [2020]
  • Teaching: MS650: machine learning for biomedical applications
  • Systematic review of machine learning for diagnosis and prognosis in dermatology
    Thomsen K [2019]
  • Surgical data science for next-generation interventions
  • Sumitomo Dainippon Pharma: discovering and designing drugs with artificial intelligence
  • Singapore healthcare cyberattack: officials will not name hackers who targeted Prime Minister Lee Hsien Loong
  • Should health care demand interpretable artificial intelligence or accept “black box” medicine?
    Wang F [2020]
  • Scalable and accurate deep learning with electronic health records
    Rajkomar A [2018]
  • SATR-DL : improving surgical skill assessment and task recognition in robot-assisted surgery with deep neural networks
    Wang Z [2018]
  • Reporting of artificial intelligence prediction models
    Collins GS [2019]
  • Relationship between burnout and professional conduct and attitudes among US medical students
    Dyrbye LN [2010]
  • Real-time use of artificial intelligence in identification of diminutive polyps during colonoscopy : a prospective study
    Mori Y [2018]
  • Real-time artificial intelligence for detection of upper gastrointestinal cancer by endoscopy : a multicentre, case-control, diagnostic study
    Luo H [2019]
  • Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning
    Poplin R [2018]
  • Precision practice with big data BIOMEDIN 205:precision medicine and big data
  • Potential liability for physicians using artificial intelligence
  • Post 2nd period medical education accreditation standards and regulations
  • Philips to offer Paige.AI’s prostate cancer detection tech
  • Personalized nutrition by prediction of glycemic responses
    Zeevi D [2015]
  • Pathways course info
  • Passive detection of atrial fibrillation using a commercially available smartwatch
    Tison GH [2018]
  • PEDIA : prioritization of exome data by image analysis
    Hsieh TC [2019]
  • Optimal intensive care outcome prediction over time using machine learning
    Meiring C [2018]
  • No safety switch: how lax oversight of electronic health records puts patients at risk
  • Modulating BET bromodomain inhibitor ZEN‐3694 and enzalutamide combination dosing in a metastatic prostate cancer patient using CURATE. AI, an artificial intelligence platform
    Pantuck AJ [2018]
  • Modeling and prediction of clinical symptom trajectories in Alzheimer’s disease using longitudinal data
    Bhagwat N [2018]
  • Medication management needs information and communications technology-based approaches, including telehealth and artificial intelligence
    Eggerth A [2019]
  • Medical education must move from the information age to the age of artificial intelligence
  • Meaning of artificial intelligence in English
    Lexico [2020]
  • Mastering the game of Go with deep neural networks and tree search
    Silver, D. [2016]
  • Machine learning in surgical robotics: 4 applications that matter
  • Machine learning in medicine
    Rajkomar A [2019]
  • Machine learning and the cancer-diagnosis problem : no gold standard
    Adamson AS [2019]
  • Machine learning and medical education
  • Learning representations by back-propagating errors
  • Learning analytics for training in the workplace
  • Large-scale assessment of a smartwatch to identify atrial fibrillation
    Perez MV [2019]
  • Korean doctor’s role
  • Key challenges for delivering clinical impact with artificial intelligence
    Kelly CJ [2019]
  • Introducing artificial intelligence training in medical education
    Paranjape K [2019]
  • Interpretable machine learning: definitions, methods, and applications
  • International evaluation of an AI system for breast cancer screening
    McKinney SM [2020]
  • Integrated prediction of lesion-specific ischaemia from quantitative coronary CT angiography using machine learning : a multicentre study
    Dey D [2018]
  • Inquiry immersion
  • Innovations & outcomes of the consortium
  • Influence of artificial intelligence on canadian medical students' preference for radiology specialty : a national survey study
    Gong B [2019]
  • Improving diagnosis in health care
    Balogh EP [2015]
  • Implementing machine learning in health care : addressing ethical challenges
    Char DS [2018]
  • Impact of a deep learning assistant on the histopathologic classification of liver cancer
    Kiani A [2020]
  • ImageNet Classification with Deep Convolutional Neural Networks
  • INDE 297: reflections, research & advances in patient care
  • How to read articles that use machine learning : users’ guides to the medical literature
    Liu Y [2019]
  • High-performance medicine : the convergence of human and artificial intelligence
    Topol EJ [2019]
  • Google and the University of Chicago are sued over data sharing
  • Genes to society: a curriculum for the Johns Hopkins University School of Medicine: translational science intersessions
  • Genes to society: a curriculum for the Johns Hopkins University School of Medicine: design and structure of the genes to society curriculum
  • Generative adversarial networks
  • Fostering the development of master adaptive learners : a conceptual model to guide skill acquisition in medical education
    Cutrer WB [2017]
  • FDA permits marketing of artificial intelligence-based device to detect certain diabetes-related eye problems
  • FDA clears GE Healthcare’s AI triage algorithm on X-ray device
  • FDA approvals for smart algorithms in medicine in one giant infographic
  • Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI
  • Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence
    Liang H [2019]
  • Evaluating machine learning articles
  • End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography
    Ardila D [2019]
  • Empathy and burnout in medicine-acknowledging risks and opportunities
    Samra R [2018]
  • Educational and Relational Stressors Associated with Burnout in Korean Medical Students
    배화옥 [2015]
  • Dissecting racial bias in an algorithm used to manage the health of populations
    Obermeyer Z [2019]
  • Digital fellowships
  • Digital Health Software Precertification (Pre-Cert) Program
  • Development and validation of a machine learning model to aid discharge processes for inpatient surgical care
    Safavi KC [2019]
  • Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs
    Gulshan V [2016]
  • Detection of anaemia from retinal fundus images via deep learning
    Mitani A [2020]
  • Dermatologist-level classification of skin cancer with deep neural networks
    Esteva A [2017]
  • Deep neural networks for acoustic modeling in speech recognition : The shared views of four research groups
    Hinton, G. [2012]
  • Deep medicine : how artificial intelligence can make healthcare human again
    Topol E [2019]
  • Deep learning versus human graders for classifying diabetic retinopathy severity in a nationwide screening program
  • Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy
  • Deep learning in ophthalmology : the technical and clinical considerations
    Ting DS [2019]
  • Deep learning for lung cancer prognostication : a retrospective multi-cohort radiomics study
    Hosny A [2018]
  • Deep learning for chest radiograph diagnosis in the emergency department
    Hwang EJ [2019]
  • Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer
    Kather JN [2019]
  • Deep explore
  • Current status of artificial intelligence in healthcare and awareness survey of medical students on artificial intelligence: results report for biomedical research course in College of Medicine
    Kwon YJ [2018]
  • Current applications of big data and machine learning in cardiology
    Cuocolo R [2019]
  • Current applications and future impact of machine learning in radiology
    Choy G [2018]
  • Core inquiry curriculum
  • Comparing artificial intelligence platforms for histopathologic cancer diagnosis
  • Clinically applicable deep learning for diagnosis and referral in retinal disease
    De Fauw J [2018]
  • Clinical applications of artificial intelligence in sepsis : a narrative review
    Schinkel M [2019]
  • Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning
    Coudray N [2018]
  • Chest radiograph interpretation with deep learning models : assessment with radiologist-adjudicated reference standards and population-adjusted evaluation
    Majkowska A [2020]
  • Cardiovascular disease risk prediction using automated machine learning : a prospective study of 423, 604 UK Biobank participants
    Alaa AM [2019]
  • Building high-level features using large scale unsupervised learning
  • Big data and artificial intelligence modeling for drug discovery
    Zhu H [2020]
  • Automated deep learning design for medical image classification by health-care professionals with no coding experience : a feasibility study
    Faes L [2019]
  • Association between surgical skin markings in dermoscopic images and diagnostic performance of a deep learning convolutional neural network for melanoma recognition
    Winkler JK [2019]
  • Artificial neural networks
    Le Cun Y [1992]
  • Artificial intelligence is rushing into patient care: and could raise risks
  • Artificial intelligence in surgery : promises and perils
  • Artificial intelligence in healthcare
  • Artificial intelligence in health care: the hope, the hype, the promise, the peril
  • Artificial intelligence in digital pathology : new tools for diagnosis and precision oncology
    Bera K [2019]
  • Artificial intelligence for mental health and mental illnesses : an overview
    Graham S [2019]
  • Artificial intelligence for drug toxicity and safety
    Basile AO [2019]
  • Artificial intelligence for clinical trial design
    Harrer S [2019]
  • Artificial intelligence and machine learning in healthcare mini-elective: Spring 2020
  • Application of artificial Intelligence to gastroenterology and hepatology
    Le Berre C [2020]
  • An augmented reality microscope with real-time artificial intelligence integration for cancer diagnosis
    Chen PC [2019]
  • An algorithm based on deep learning for predicting in-hospital cardiac arrest
    Kwon JM [2018]
  • American Board of Artificial Intelligence in Medicine (ABAIM) aims to educate and certify healthcare professionals in AI, and related technologies
  • Amazon scraps secret AI recruiting tool that showed bias against women
  • Amazon introduces machine learning medical transcription service
  • Adversarial attacks on medical machine learning
  • Accurate prediction of blood culture outcome in the intensive care unit using long short-term memory neural networks
  • Accuracy of computer-aided diagnosis of melanoma : a meta-analysis
    Dick V [2019]
  • Accreditation Standards of KIMEE 2019
  • AMA: take extra care when applying AI in medical education
  • AISC 610: computationally-enabled medicine
  • AI-driven dermatology could leave dark-skinned patients behind
  • AI is convicting criminals and determining jail time, but is it fair?
  • A proposal for the dartmouth summer research project on artificial intelligence, august 31, 1955
  • A mathematical-descriptor of tumor-mesoscopic-structure from computed-tomography images annotates prognostic-and molecularphenotypes of epithelial ovarian cancer
    Lu H [2019]
  • A fast learning algorithm for deep belief nets
  • A deep learning system for differential diagnosis of skin diseases
    Liu Y [2020]
  • A deep learning mammography-based model for improved breast cancer risk prediction
    Yala A [2019]
  • A deep learning approach to antibiotic discovery
    Stokes JM [2020]
  • A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging : a systematic review and meta-analysis
    Liu X [2019]
  • A clinically applicable approach to continuous prediction of future acute kidney injury
    Tomasev N [2019]
  • 4 Phases to making goal of lifelong physician learner a reality
  • 2015 New vision for education unlocking the potential of technology