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This title is printed to order. This book may have been self-published. If so, we cannot guarantee the quality of the content. In the main most books will have gone through the editing process however some may not. We therefore suggest that you be aware of this before ordering this book. If in doubt check either the author or publisher’s details as we are unable to accept any returns unless they are faulty. Please contact us if you have any questions.
Artificial intelligence (AI) is a simulation of the process of human intelligence through computers.
AI has cemented its status as a powerful technology with the ability to propel a paradigm shift in healthcare and medicine of the 21st century and the future. The insights and values gained from AI and its subset, machine learning, are essential for predicting health outcomes and improving decision-making in healthcare and medicine.
AI can offer revolutionary insights into medicine, through data from genetics, proteomics and other life sciences that advance the process of drug discovery and development. Discovering drugs is a crucial first step in the biopharmaceutical value chain. Drug discovery is a long, expensive and often unsuccessful process. The biopharmaceutical industry makes efforts to employ AI to improve drug discovery, reduce research and development costs, reduce the time and cost of early drug discovery, and support predicting potential risks/side effects in late clinical trials that can be useful in avoiding traumatic events in clinical trials.
The rapid growth in life sciences and machine learning algorithms has led to enormous statistical access to the growth of AI-based start-ups focused on drug innovation in recent years. The growing need to curb drug discovery costs and reduce time involved in the drug development process, the rising adoption of cloud-based applications and services, and the impending patent expiry of blockbuster drugs are some of the key factors driving the growth of this market.
However, shortage of AI workforce and ambiguous regulatory guidelines for medical software and lack of data sets in this field are some of the factors expected to restrain the growth of this market in the coming years.
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This title is printed to order. This book may have been self-published. If so, we cannot guarantee the quality of the content. In the main most books will have gone through the editing process however some may not. We therefore suggest that you be aware of this before ordering this book. If in doubt check either the author or publisher’s details as we are unable to accept any returns unless they are faulty. Please contact us if you have any questions.
Artificial intelligence (AI) is a simulation of the process of human intelligence through computers.
AI has cemented its status as a powerful technology with the ability to propel a paradigm shift in healthcare and medicine of the 21st century and the future. The insights and values gained from AI and its subset, machine learning, are essential for predicting health outcomes and improving decision-making in healthcare and medicine.
AI can offer revolutionary insights into medicine, through data from genetics, proteomics and other life sciences that advance the process of drug discovery and development. Discovering drugs is a crucial first step in the biopharmaceutical value chain. Drug discovery is a long, expensive and often unsuccessful process. The biopharmaceutical industry makes efforts to employ AI to improve drug discovery, reduce research and development costs, reduce the time and cost of early drug discovery, and support predicting potential risks/side effects in late clinical trials that can be useful in avoiding traumatic events in clinical trials.
The rapid growth in life sciences and machine learning algorithms has led to enormous statistical access to the growth of AI-based start-ups focused on drug innovation in recent years. The growing need to curb drug discovery costs and reduce time involved in the drug development process, the rising adoption of cloud-based applications and services, and the impending patent expiry of blockbuster drugs are some of the key factors driving the growth of this market.
However, shortage of AI workforce and ambiguous regulatory guidelines for medical software and lack of data sets in this field are some of the factors expected to restrain the growth of this market in the coming years.