In medical diagnosis, deep learning is expected to extend its roots into medical imaging, sensor-driven analysis, translational bioinformatics, public health policy development, and beyond. The deep learning technique emerged as a result of artificial neural networks. It serves as a powerful tool for machine learning, reshaping the future of artificial intelligence.
Deep learning systems are used where human interpretation is difficult. This can make diagnoses of diseases faster and accurate thus reducing the risk in the decision-making process.
Deep learning mainly depends on large amounts of training data. Such requirements make more critical the classical entry barriers of machine learning, i.e., data availability and privacy. It could save lives, and avoid medical complications.
Deep learning has gained a central position in recent years in machine learning and pattern recognition.
Deep learning shouldn’t be considered a tool for every single health challenge; it is still questionable whether a large amount of training data and computational resources needed to run deep learning at full performance is worthwhile.
Deep learning has provided a positive revival of Neural Networks.
Deep learning may slow down the development of machine learning algorithms with conscious use of computational devices.
Let human do what they do well and let machines do what they do well. In the end, we may maximise the potential of both.
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