Towards International Standards for the Evaluation of Artificial Intelligence for Health
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[1] Erik Schultes,et al. The FAIR Guiding Principles for scientific data management and stewardship , 2016, Scientific Data.
[2] Donald J. Trump,et al. Executive Order 13859: Maintaining American Leadership in Artificial Intelligence , 2019 .
[3] Avrim Blum,et al. The Ladder: A Reliable Leaderboard for Machine Learning Competitions , 2015, ICML.
[4] S. Friend,et al. Crowdsourcing biomedical research: leveraging communities as innovation engines , 2016, Nature Reviews Genetics.
[5] Anne E Carpenter,et al. Opportunities and obstacles for deep learning in biology and medicine , 2017, bioRxiv.
[6] G. Collins,et al. Uniformity in measuring adherence to reporting guidelines: the example of TRIPOD for assessing completeness of reporting of prediction model studies , 2019, BMJ Open.
[7] Sebastian Thrun,et al. Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.
[8] Bram van Ginneken,et al. A survey on deep learning in medical image analysis , 2017, Medical Image Anal..
[9] Andre Esteva,et al. A guide to deep learning in healthcare , 2019, Nature Medicine.
[10] Masaru Ishii,et al. Towards computational fluorescence microscopy: Machine learning-based integrated prediction of morphological and molecular tumor profiles , 2018, ArXiv.
[11] Paul Voosen,et al. The AI detectives. , 2017, Science.
[12] Jie Xu,et al. The practical implementation of artificial intelligence technologies in medicine , 2019, Nature Medicine.
[13] Yibo Zhang,et al. Deep learning enhanced mobile-phone microscopy , 2017, ACS Photonics.
[14] C. Gidengil,et al. Evaluation of symptom checkers for self diagnosis and triage: audit study , 2015, BMJ : British Medical Journal.
[15] K-R Müller,et al. Scoring of tumor-infiltrating lymphocytes: From visual estimation to machine learning. , 2018, Seminars in cancer biology.
[16] Alexander S. Ecker,et al. Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming , 2019, ArXiv.
[17] Lu Lu,et al. How to Host An Effective Data Competition: Statistical Advice for Competition Design and Analysis , 2019, Stat. Anal. Data Min..
[18] Christoph Meinel,et al. Deep Learning for Medical Image Analysis , 2018, Journal of Pathology Informatics.
[19] Klaus-Robert Müller,et al. Introduction to machine learning for brain imaging , 2011, NeuroImage.
[20] 尚弘 島影. National Institute of Standards and Technologyにおける超伝導研究及び生活 , 2001 .
[21] Shamim Nemati,et al. An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU , 2017, Critical care medicine.
[22] Jennifer Couzin-Frankel,et al. Medicine contends with how to use artificial intelligence. , 2019, Science.
[23] Atul J. Butte,et al. Assessment of a Deep Learning Model Based on Electronic Health Record Data to Forecast Clinical Outcomes in Patients With Rheumatoid Arthritis , 2019, JAMA network open.
[24] Andrew L. Beam,et al. Adversarial attacks on medical machine learning , 2019, Science.
[25] J. Chi,et al. Automated Detection of P. falciparum Using Machine Learning Algorithms with Quantitative Phase Images of Unstained Cells , 2016, PloS one.
[26] Kamran Sartipi,et al. HL7 FHIR: An Agile and RESTful approach to healthcare information exchange , 2013, Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems.
[27] Alexander Binder,et al. Unmasking Clever Hans predictors and assessing what machines really learn , 2019, Nature Communications.
[28] P. Mildenberger,et al. Introduction to the DICOM standard , 2002, European Radiology.
[29] Ari Ercole,et al. Optimal intensive care outcome prediction over time using machine learning , 2018, PloS one.
[30] Miles Brundage,et al. The Role of Cooperation in Responsible AI Development , 2019, ArXiv.
[31] Aaron Carass,et al. Why rankings of biomedical image analysis competitions should be interpreted with care , 2018, Nature Communications.
[32] Masoumeh Haghpanahi,et al. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network , 2019, Nature Medicine.
[33] Ziad Obermeyer,et al. Regulation of predictive analytics in medicine , 2019, Science.
[34] Nils Strodthoff,et al. Detecting and interpreting myocardial infarction using fully convolutional neural networks , 2018, Physiological measurement.
[35] J. Samet,et al. From the Food and Drug Administration. , 2002, JAMA.
[36] Aidan N. Gomez,et al. Benchmarking Bayesian Deep Learning with Diabetic Retinopathy Diagnosis , 2019 .
[37] Organización Mundial de la Salud. Guidelines for the treatment of malaria , 2010 .
[38] Lei Ying,et al. Nanophotonic media for artificial neural inference , 2018, Photonics Research.
[39] Thomas Wiegand,et al. WHO and ITU establish benchmarking process for artificial intelligence in health , 2019, The Lancet.
[40] Gary S Collins,et al. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and Elaboration , 2015, Annals of Internal Medicine.
[41] Bram van Ginneken,et al. Google’s lung cancer AI: a promising tool that needs further validation , 2019, Nature Reviews Clinical Oncology.
[42] Hao Chen,et al. Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge , 2016, Medical Image Anal..
[43] Gary S. Collins,et al. Reporting of artificial intelligence prediction models , 2019, The Lancet.