Perspectives on artificial intelligence and deep learning in pathology
Randy Van Ommeren, Kevin Faust, and Phedias Diamandis |
Artificial intelligence (AI), a collective term for a wide variety of machine learning systems, has progressed significantly in recent years with the development and widespread dissemination of deep learning techniques. Deep learning is a specific machine learning approach that uses neural node architectures reminiscent of those found in the human cortex. Neural networks can be trained on large quantities of data, allowing them to develop feature recognition capabilities that permit discrimination between various patterns in a data set. Deep learning approaches have been shown to function at a human – or even superhuman – level in various domains, recently beating a world-class player at the highly complex and intuitive game of GO (1).
The implementation of machine learning approaches for medical diagnostics has long been a topic of interest, but translation to real-world settings has remained limited (2). However, with recent developments in deep learning, the possibility of sophisticated decision support for clinicians has been aggressively rejuvenated. A flurry of publications in recent years have demonstrated the potential for deep learning applications in such varied fields as dermatology, ophthalmology, oncology, radiology, and pathology. Radiology and pathology, in particular, are considered highly amenable to deep learning-based technologies, given the particular strengths of these algorithms in image analysis (3).
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