Neural Networks and Deep Learning in Modern Healthcare-<\/strong><\/h2>\nAlso Read:<\/strong> The Internet Of Things Is the New Future Of The Healthcare Industry<\/a><\/p>\nNeural networks\u00a0are a computational concept\u00a0based on a\u00a0large collection of neural units, loosely modelling just the way our brain analyses. Our brain\u00a0solves problems with large clusters of biological neurons connected by axons. Each neural unit is connected with many other units which are\u00a0self-trained\u00a0rather than explicitly programmed.\u00a0In the case of AI, the neural network has the ability to learn from its previous cases. Artificial neural networks can diagnose diseases fast & accurately like eye problems, malignant melanoma etc.<\/p>\n
ANNs (Artificial Neural Netowrks) are\u00a0a powerful tool to help physicians perform diagnosis and other operations. They\u00a0helps in processing the large amount of data in less time, reduction of diagnosis time, etc.\u00a0ANNs have proven suitable to detect heart and cancer problems, to analyse blood samples, to track glucose level in diabetics, tumour detection using image analysis, development of drugs, etc.\u00a0It is making the analysis more credible and increasing the patient satisfaction.<\/p>\n
![\"\"](\"https:\/\/yaabot.com\/wp-content\/uploads\/2017\/05\/yaabot_neural03.jpg\")
<\/a><\/p>\n\u00a0<\/strong><\/p>\nIn 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\u00a0of artificial neural networks. It\u00a0serves as a\u00a0powerful 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.\u00a0It\u00a0could save lives, and avoid medical complications.<\/p>\n
![\"\"](\"https:\/\/yaabot.com\/wp-content\/uploads\/2017\/05\/yaabot_neural04.jpg\")
<\/a><\/p>\nDeep 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.<\/p>\n
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.<\/strong><\/p>\nAvailable AI Interfaces<\/strong><\/h2>\nGoogle DeepMind<\/strong><\/p>\n![\"\"](\"https:\/\/yaabot.com\/wp-content\/uploads\/2017\/05\/yaabot_neural05-1024x564.png\")
<\/a><\/p>\nDeepMind\u00a0was founded in 2010 by\u00a0Demis Hassabis<\/a>,\u00a0Shane Legg<\/a>\u00a0and\u00a0Mustafa Suleyman<\/a>. The team\u00a0combined machine learning and system neuroscience to build learning algorithms. Google acquired the startup in\u00a02014. DeepMind algorithms have shown considerable potential. They\u00a0remember previously solved\u00a0problems and use the data to solve new ones if required.<\/p>\n\n- In July 2016, DeepMind collaborated with Moorfields Eye Hospital<\/a> to apply DeepMind technology in the analysis of eye scans, searching for early signs of diseases leading to\u00a0blindness<\/a>.<\/li>\n
- A month later, with the researchers of University College London Hospital, DeepMind announced\u00a0an algorithm that can accurately differentiate between healthy and cancerous tissues in head and neck areas.<\/li>\n
- Also in 2016, DeepMind collaborated with Royal Free London NHS Foundation Trust to assist in the management of acute kidney injury.<\/li>\n<\/ul>\n
Neural networks\u00a0are a computational concept\u00a0based on a\u00a0large collection of neural units, loosely modelling just the way our brain analyses. Our brain\u00a0solves problems with large clusters of biological neurons connected by axons. Each neural unit is connected with many other units which are\u00a0self-trained\u00a0rather than explicitly programmed.\u00a0In the case of AI, the neural network has the ability to learn from its previous cases. Artificial neural networks can diagnose diseases fast & accurately like eye problems, malignant melanoma etc.<\/p>\n
ANNs (Artificial Neural Netowrks) are\u00a0a powerful tool to help physicians perform diagnosis and other operations. They\u00a0helps in processing the large amount of data in less time, reduction of diagnosis time, etc.\u00a0ANNs have proven suitable to detect heart and cancer problems, to analyse blood samples, to track glucose level in diabetics, tumour detection using image analysis, development of drugs, etc.\u00a0It is making the analysis more credible and increasing the patient satisfaction.<\/p>\n
\u00a0<\/strong><\/p>\n 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\u00a0of artificial neural networks. It\u00a0serves as a\u00a0powerful 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.\u00a0It\u00a0could save lives, and avoid medical complications.<\/p>\n 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.<\/p>\n 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.<\/strong><\/p>\n Google DeepMind<\/strong><\/p>\n DeepMind\u00a0was founded in 2010 by\u00a0Demis Hassabis<\/a>,\u00a0Shane Legg<\/a>\u00a0and\u00a0Mustafa Suleyman<\/a>. The team\u00a0combined machine learning and system neuroscience to build learning algorithms. Google acquired the startup in\u00a02014. DeepMind algorithms have shown considerable potential. They\u00a0remember previously solved\u00a0problems and use the data to solve new ones if required.<\/p>\n<\/a><\/p>\n<\/a><\/p>\nAvailable AI Interfaces<\/strong><\/h2>\n
<\/a><\/p>\n\n
![\"\"](\"https:\/\/entropymag.co\/wp-content\/plugins\/trx_addons\/components\/lazy-load\/images\/placeholder.png\")
<\/a><\/p>\n