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Big Data Analysis and Artificial Intelligence for Medical Sciences
Overview of the current state of the art on the use of artificial intelligence in medicine and biology
Big Data Analysis and Artificial Intelligence for Medical Sciences demonstrates the efforts made in the fields of Computational Biology and medical sciences to design and implement robust, accurate, and efficient computer algorithms for modeling the behavior of complex biological systems much faster than using traditional modeling approaches based solely on theory.
With chapters written by international experts in the field of medical and biological research, Big Data Analysis and Artificial Intelligence for Medical Sciences includes information on:
• Studies conducted by the authors which are the result of years of interdisciplinary collaborations with clinicians, computer scientists, mathematicians, and engineers
• Differences between traditional computational approaches to data processing (those of mathematical biology) versus the experiment-data-theory-model-validation cycle
• Existing approaches to the use of big data in the healthcare industry, such as through IBM's Watson Oncology, Microsoft's Hanover, and Google's DeepMind
• Difficulties in the field that have arisen as a result of technological changes, and potential future directions these changes may take
A timely and up-to-date resource on the integration of artificial intelligence in medicine and biology, Big Data Analysis and Artificial Intelligence for Medical Sciences is of great benefit not only to professional scholars, but also MSc or PhD program students eager to explore advancement in the field.
Overview of the current state of the art on the use of artificial intelligence in medicine and biology
Big Data Analysis and Artificial Intelligence for Medical Sciences demonstrates the efforts made in the fields of Computational Biology and medical sciences to design and implement robust, accurate, and efficient computer algorithms for modeling the behavior of complex biological systems much faster than using traditional modeling approaches based solely on theory.
With chapters written by international experts in the field of medical and biological research, Big Data Analysis and Artificial Intelligence for Medical Sciences includes information on:
• Studies conducted by the authors which are the result of years of interdisciplinary collaborations with clinicians, computer scientists, mathematicians, and engineers
• Differences between traditional computational approaches to data processing (those of mathematical biology) versus the experiment-data-theory-model-validation cycle
• Existing approaches to the use of big data in the healthcare industry, such as through IBM's Watson Oncology, Microsoft's Hanover, and Google's DeepMind
• Difficulties in the field that have arisen as a result of technological changes, and potential future directions these changes may take
A timely and up-to-date resource on the integration of artificial intelligence in medicine and biology, Big Data Analysis and Artificial Intelligence for Medical Sciences is of great benefit not only to professional scholars, but also MSc or PhD program students eager to explore advancement in the field.