Question

Ting, D. S., Liu, Y., Burlina, P., Xu, X., Bressler, N. M., & Wong, T. Y. (2018). AI for medical imaging goes deep. Nature medicine, 24(5), 539-540.

2. Pesapane, F., Codari, M., & Sardanelli, F. (2018). Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine. European radiology experimental, 2(1), 35.

3. Lewis, S. J., Gandomkar, Z., & Brennan, P. C. (2019). Artificial Intelligence in medical imaging practice: looking to the future. Journal of Medical Radiation Sciences, 66(4), 292-295.

4. Alexander, A., Jiang, A., Ferreira, C., & Zurkiya, D. (2020). An intelligent future for medical imaging: a market outlook on artificial intelligence for medical imaging. Journal of the American College of Radiology, 17(1), 165-170.

5. Ranschaert, E. R., Morozov, S., & Algra, P. R. (Eds.). (2019). Artificial Intelligence in Medical Imaging: Opportunities, Applications and Risks. Springer.

Please write short summary for each link around 15-20 lines (paragraph)

Answer 1

1.An artificial intelligence (AI) using a deep-learning approach can classify retinal images from optical coherence tomography for early diagnosis of retinal diseases and has the potential to be used in other image-based medical diagnoses.

2.This article provides basic definitions of terms such as "machine/deep learning" and analyses the integration of AI into radiology.Magnetic resonance imaging and computed tomography collectively account for more than 50% of current articles. Neuroradiology appears in about one-third of the papers, followed by musculoskeletal, cardiovascular, breast, urogenital, lung/thorax, and abdomen, each representing 6-9% of articles. With an irreversible increase in the amount of data and the possibility to use AI to identify findings either detectable or not by the human eye, radiology is now moving from a subjective perceptual skill to a more objective science.The higher efficiency provided by AI will allow radiologists to perform more value-added tasks, becoming more visible to patients and playing a vital role in multidisciplinary clinical teams.

3.For many of us, our perception of artificial intelligence (AI) is shaped by our exposure to film and television media. For the baby boomers, this may be the stilted, sarcastic robot from Lost in Space and for Generation Xs’, the galactic lifestyles of everyone.Millennials have been exposed to the mega‐mane, The Terminator and already live in a social media world influenced by algorithms. It seems for a long time we have known that the intelligence of artificial computer‐based technologies was coming our way. Together with the explosion of AI‐related professional discourse in medical imaging over the last 5 years, in 2019, Price Waterhouse Coopers have heralded unprecedented spending on AI technologies over the next 10 years.1 Machine learning (ML) has arrived into our working world, and medical radiation practitioners have the front seat on this bandwagon.

4.Radiologists today are under increasing work pressure. We surveyed radiologists in the United States across practice settings, and the overwhelming majority reported an increased workload. Artificial intelligence (AI), which includes machine learning, can help address these issues. It also has the potential to improve clinical outcomes and raise further the value of medical imaging in ways yet to be defined. In this article, we report on recent McKinsey & Company work to understand the growth of AI in medical imaging. We highlight progress in its clinical application, the investments that are backing it, and the barriers to broader adoption. We also offer a view on how the market will develop. AI is set to have a big impact on the medical imaging market and hence on how radiologists work, helping them to speed up scan time, make more accurate diagnoses, and ease their workload. As AI in medical imaging increasingly proves its worth, it is hard to imagine that AI will not ultimately transform radiology.

5. The overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging. After an introduction on game changers in radiology, such as deep learning technology, the technological evolution of AI in computing science and medical image computing is described, with explanation of basic principles and the types and subtypes of AI. Subsequent sections address the use of imaging biomarkers, the development and validation of AI applications, and various aspects and issues relating to the growing role of big data in radiology. Diverse real-life clinical applications of AI are then outlined for different body parts, demonstrating their ability to add value to daily radiology practices. The concluding section focuses on the impact of AI on radiology and the implications for radiologists, for example with respect to training. Written by radiologists and IT professionals, the book will be of high value for radiologists, medical/clinical physicists, IT specialists, and imaging informatics professionals.

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