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Case Study - Methods

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     Using AI to tackle disease, from monitoring, to detection, to lifestyle management. Google’s strategy involves an end-to-end approach to healthcare, including: Data generation — This includes digitising and ingesting data produced by wearables, imaging, and MRIs among other methods. This data stream is critical to AI-driven anomaly detection. Disease detection — Using AI to detect anomalies in a given dataset that might signal the presence of some disease. Disease/lifestyle management — These tools help people who have been diagnosed with a disease or are at risk of developing one go about their day-to-day lives and/or make positive lifestyle modifications DeepMind is involved in several parts of disease detection and Google itself holds several patents under the parent company.      Google's DeepMind artificial intelligence research company is developing a clinical decision support (CDS) tool for identifying eye diseases. Researchers from DeepMind...
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Case Study - Introduction

        Google, in full Google LLC formerly Google Inc. (1998–2017), American search engine company, founded in 1998 by Sergey Brin and Larry Page, that is a subsidiary of the holding company Alphabet Inc. More than 70 percent of worldwide online search requests are handled by Google, placing it at the heart of most Internet users’ experience.  Google has always seen itself as more than a search engine and advertising company. Now it’s turning its focus to healthcare, betting that its AI prowess can create a powerful new paradigm for the detection, diagnosis, and treatment of disease. In short, Google seems to be going after the healthcare space from every possible angle. Google is betting that the future of healthcare is going to be structured data and AI. The company is applying AI to disease detection, new data infrastructure, and potentially insurance. "Tomorrow, if AI can shape healthcare, it has to work through the regulations of healthcare… In fact, ...
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Research Diagram 2 - Machine Learning Process

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Machine Learning Process,  is the first step in ML process to take the data from multiple sources and followed by a fine-tuned process of data, this data would be the feed for ML algorithms based on the problem statement, like predictive, classification and other models which are available in the space of ML world. Let us see the process steps. ( Author's work ) Machine Learning – Stages: We can split ML process stages into 5 as below, mentioned in the flow diagram. Collection of Data Data Cleaning Model Building Model Evaluation Model Deployment
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