IEEE pulse
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Artificial intelligence (AI) and machine learning (ML) have influenced medicine in myriad ways, and medical imaging is at the forefront of technological transformation. Recent advances in AI/ML fields have made an impact on imaging and image analysis across the board, from microscopy to radiology. ⋯ Recent enhancements for computational hardware is enabling researchers to revisit old AI algorithms and experiment with new mathematical ideas. Researchers are applying these methods to a broad array of medical technologies, ranging from microscopic image analysis to tomographic image reconstruction and diagnostic planning.
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"It is a capital mistake to theorize before one has data," said Sherlock Holmes creator and author Arthur Conan Doyle in 1887. In this era of big data, and especially the crush of medical information becoming available through new technologies and bulging databases, Doyle's quote could be updated to: "It is a capital mistake to theorize before one has data and understands what they mean."
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Is there a Cheshire Cat in science? One might believe so, given the many published scientific discoveries that cannot be independently reproduced. The ?replication crisis? in science has become a widely discussed issue among scientists and the lay media and even has its own entry in Wikipedia.