![]() You can choose to be either a thermostat or a thermometer. In his presentation Unleash Your Super Brain to Learn Faster, Jim Kwik offers a great set of metaphors. You just want time to step back and focus on the things that matter to you the most. It's easy to believe that there’s not enough time in the day. Plus, remote work is available with the click of a button. There’s a constant cycle of news and entertainment. ![]() Radiol Artif Intell 2022 4(3):e210064.We live in a world of digital overload. External Validation of Deep Learning Algorithms for Radiologic Diagnosis: A Systematic Review. PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement. Collins GS, Reitsma JB, Altman DG, et al. ![]() Deep learning for chest radiograph diagnosis: a retrospective comparison of the CheXNeXt algorithm to practicing radiologists. Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy. COVID-19 Artificial Intelligence Diagnosis Using Only Cough Recordings. Artificial intelligence in radiology: 100 commercially available products and their scientific evidence. van Leeuwen KG, Schalekamp S, Rutten MJCM, van Ginneken B, de Rooij M. Stand-alone artificial intelligence for breast cancer detection in mammography: comparison with 101 radiologists. Rodriguez-Ruiz A, Lång K, Gubern-Merida A, et al. Identification of children at very low risk of clinically-important brain injuries after head trauma: a prospective cohort study. Kuppermann N, Holmes JF, Dayan PS, et al. A Short Instrument for Measuring Students’ Confidence with ‘Key Skills’ (SICKS): Development, Validation and Initial Results. Development and evaluation of a spiral model of assessing EBM competency using OSCEs in undergraduate medical education. A scoping review of transfer learning research on medical image analysis using ImageNet. Privacy-preserving distributed learning of radiomics to predict overall survival and HPV status in head and neck cancer. Understanding artificial intelligence based radiology studies: CNN architecture. Bloom’s taxonomy of cognitive learning objectives. Medical education reimagined: a call to action. Creating a Competency-Based Medical Education Curriculum for Canadian Diagnostic Radiology Residency (Queen’s Fundamental Innovations in Residency Education)-Part 1: Transition to Discipline and Foundation of Discipline Stages. Barriers to Resident Research in Radiology: A Canadian Perspective. Do no harm: a roadmap for responsible machine learning for health care. A “Bumper-Car” Curriculum for Teaching Deep Learning to Radiology Residents ☆. Effect of stress coping ability and working hours on burnout among residents. AI-RADS: An Artificial Intelligence Curriculum for Residents. Lindqwister AL, Hassanpour S, Lewis PJ, Sin JM. Preparing Radiologists to Lead in the Era of Artificial Intelligence: Designing and Implementing a Focused Data Science Pathway for Senior Radiology Residents. National Imaging Informatics Curriculum and Course. What do medical students actually need to know about artificial intelligence? NPJ Digit Med 2020 3(1):86. McCoy LG, Nagaraj S, Morgado F, Harish V, Das S, Celi LA. Medicine residents’ understanding of the biostatistics and results in the medical literature. The Role of Artificial Intelligence in Diagnostic Radiology: A Survey at a Single Radiology Residency Training Program. An international survey on AI in radiology in 1,041 radiologists and radiology residents part 1: fear of replacement, knowledge, and attitude. Huisman M, Ranschaert E, Parker W, et al. Educating Future Physicians in Artificial Intelligence (AI): An Integrative Review and Proposed Changes. The Need for a Machine Learning Curriculum for Radiologists. Wood MJ, Tenenholtz NA, Geis JR, Michalski MH, Andriole KP. Artificial Intelligence in Medicine: Where Are We Now? Acad Radiol 2020 27(1):62–70. Kulkarni S, Seneviratne N, Baig MS, Khan AHA.
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