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MedTech Outlook | Tuesday, January 26, 2021
AI utilized data and algorithms to derive computational models of activities that are often as good as humans.
FREMONT, CA: Artificial intelligence (AI) is the potential of a computer program to perform processes connected with human intelligence, like reasoning, learning, adaptation, sensory understanding, and interaction. As with scientific discipline, the AI scientific community leverages technical language and terminology that can be complex to understand for those outside the sector. This, in addition to the quick advancement in the field, can make it daunting for other disciplines to keep abreast of innovations in AI. Indeed, one of the vital concerns expressed regarding AI in medicine is that few interdisciplinary professionals work at AI and medicine interface and can translate between the two.
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The ongoing interest in AI in medical imaging stems from major innovations in deep learning‐based computer vision over the past few years. The area of computer vision concerns computers that analyze and understand the visual world. Within computer vision, object recognition is a vital task that can be posed as an image classification issue. Researchers in this field utilize challenge datasets to benchmark the progress inaccuracy of image classification. One of the driving forces behind AI in medical imaging is the vast amount of digital data created worldwide that may be useful in training algorithms.
Artificial intelligence (AI) refers to a machine performing activities that would ordinarily need human brainpower to accomplish, like making sense of spoken language, learning behaviors, or solving issues. This means that an AI program can study from real‐world data and experience and encompasses the potential to enhance its performance given more data. Explainable AI is an evolving subfield of AI that attempts to explain how AI systems' black box decisions are created. Explainable AI visions to understand the vital steps involved in making computational decisions. This should theoretically enable decisions taken by an algorithm to be realized by end‐users.
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