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Intelligence-Based Medicine: Artificial Intelligence and Human Cognition in Clinical Medicine and Healthcare
Intelligence-Based Data Science, Artificial Intelligence, and Human Cognition in Clinical Medicine and Healthcare provides a multidisciplinary and comprehensive survey of artificial intelligence concepts and methodologies with real life applications in healthcare and medicine. Authored by a senior physician-data scientist, the book presents an intellectual and academic interface between the medical and the data science domains that is symmetric and balanced.The content consists of basic concepts of artificial intelligence and its real-life applications in a myriad of medical areas as well as medical and surgical subspecialties. It brings section summaries to emphasize key concepts delineated in each section; mini-topics authored by world-renowned experts in the respective key areas for their personal perspective; and a compendium of practical resources, such as glossary, references, best articles, and top companies.The goal of the book is to inspire clinicians to embrace the artificial intelligence methodologies as well as to educate data scientists about the medical ecosystem, in order to create a transformational paradigm for healthcare and medicine by using this emerging new technology.
534 pages, Hardcover
Published July 16, 2020
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Displaying 1 of 1 review
January 31, 2022
“If AI is the rocket to launch us into orbit and onto moonshot projects, data are the fuel we need" (p.407).
The book is logically divided into four sections: intro to AI, AI in the current era (general), current AI in medicine, and the future of AI in medicine. There is no coding, math, or medical jargon in this book, and is presented as a primer for the busy clinician or data-scientist. If you are interested in the programming or mathematics itself, I recommend Hands-On Machine Learning by Geron (or an online data-science course on Udemy/Coursera).
Most of the core topics in AI are touched upon, including the different categories of machine learning, evaluating performance, the capabilities of deep learning along with its limitations... I learned that one of the major issues in AI is the relative lack of explainability in how trained models come to decisions, the “black box” conundrum (p.129). There are summary tables showing different AI application categories with examples, including medical imaging, altered reality, decision support, biomedical diagnostics, precision medicine, drug discovery, digital health, wearable technology, robotic technology, virtual assistants (p.263). But particularly unique is the chapter on potential application of AI for a huge list of subspecialties (p.267-387).
Around 100 commentaries by selected authors are sprinkled throughout, which I found to be increasingly distracting as the book progressed. Many of these articles start the same way explaining how much data is generated every year, the underutilized potential of AI, how we have to avoid the hype, etc. and mention many of the same examples, such as AlphaGo, IBM Watson, and the Stanford derm study. I suspect it can be trimmed 50-100 pages without losing much content. Here is an example from one of the textboxes:
The author’s enthusiasm for AI is palpable, but can be too much at times. There is a lot of hype surrounding AI which naturally has me weary. However, a lot of thought and effort went into this book, and it fills an important niche in this growing field. For example, the last section contains a curated list of key references (books and journals), top 100+ landmark papers, and 100 AI-in-medicine companies to know.
I predict that this book and its future editions will be a standard reference in the years to come. Read this book as a starting point if you are at all interested in the application of AI in healthcare.
The book is logically divided into four sections: intro to AI, AI in the current era (general), current AI in medicine, and the future of AI in medicine. There is no coding, math, or medical jargon in this book, and is presented as a primer for the busy clinician or data-scientist. If you are interested in the programming or mathematics itself, I recommend Hands-On Machine Learning by Geron (or an online data-science course on Udemy/Coursera).
Most of the core topics in AI are touched upon, including the different categories of machine learning, evaluating performance, the capabilities of deep learning along with its limitations... I learned that one of the major issues in AI is the relative lack of explainability in how trained models come to decisions, the “black box” conundrum (p.129). There are summary tables showing different AI application categories with examples, including medical imaging, altered reality, decision support, biomedical diagnostics, precision medicine, drug discovery, digital health, wearable technology, robotic technology, virtual assistants (p.263). But particularly unique is the chapter on potential application of AI for a huge list of subspecialties (p.267-387).
Around 100 commentaries by selected authors are sprinkled throughout, which I found to be increasingly distracting as the book progressed. Many of these articles start the same way explaining how much data is generated every year, the underutilized potential of AI, how we have to avoid the hype, etc. and mention many of the same examples, such as AlphaGo, IBM Watson, and the Stanford derm study. I suspect it can be trimmed 50-100 pages without losing much content. Here is an example from one of the textboxes:
“Rapid developments in the field of AI over the past decade have sparked unprecedented levels of excitement within the healthcare sector: the expectation is that AI can be leveraged to tackle some of healthcare’s greatest challenges. Beyond the hopes and the hype, we need to define and determine which issues constitute the primary challenges to better care…” (p.244).(now imagine 100 of these intros).
The author’s enthusiasm for AI is palpable, but can be too much at times. There is a lot of hype surrounding AI which naturally has me weary. However, a lot of thought and effort went into this book, and it fills an important niche in this growing field. For example, the last section contains a curated list of key references (books and journals), top 100+ landmark papers, and 100 AI-in-medicine companies to know.
I predict that this book and its future editions will be a standard reference in the years to come. Read this book as a starting point if you are at all interested in the application of AI in healthcare.
Displaying 1 of 1 review