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Artificial intelligence in medicine

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TLDR
AI in medicine, which is the focus of this review, has two main branches: virtual and physical, and the virtual branch includes informatics approaches from deep learning information management to control of health management systems, and active guidance of physicians in their treatment decisions.
Abstract
Artificial Intelligence (AI) is a general term that implies the use of a computer to model intelligent behavior with minimal human intervention. AI is generally accepted as having started with the invention of robots. The term derives from the Czech word robota, meaning biosynthetic machines used as forced labor. In this field, Leonardo Da Vinci's lasting heritage is today's burgeoning use of robotic-assisted surgery, named after him, for complex urologic and gynecologic procedures. Da Vinci's sketchbooks of robots helped set the stage for this innovation. AI, described as the science and engineering of making intelligent machines, was officially born in 1956. The term is applicable to a broad range of items in medicine such as robotics, medical diagnosis, medical statistics, and human biology-up to and including today's "omics". AI in medicine, which is the focus of this review, has two main branches: virtual and physical. The virtual branch includes informatics approaches from deep learning information management to control of health management systems, including electronic health records, and active guidance of physicians in their treatment decisions. The physical branch is best represented by robots used to assist the elderly patient or the attending surgeon. Also embodied in this branch are targeted nanorobots, a unique new drug delivery system. The societal and ethical complexities of these applications require further reflection, proof of their medical utility, economic value, and development of interdisciplinary strategies for their wider application.

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Journal ArticleDOI

Artificial intelligence in drug development: present status and future prospects

TL;DR: The major causes of attrition rates in new drug approvals are discussed, the possible ways that AI can improve the efficiency of the drug development process and collaboration of pharmaceutical industry giants with AI-powered drug discovery firms are discussed.
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Mapping the Challenges of Artificial Intelligence in the Public Sector: Evidence from Public Healthcare

TL;DR: A case of adoption of the AI system IBM Watson in public healthcare in China is analysed, to map how three groups of stakeholders perceive the challenges of AI adoption in the public sector.
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Pleiotropy and Specificity: Insights from the Interleukin 6 Family of Cytokines

TL;DR: The IL-6 family of cytokines in autoimmune diseases is reviewed from the viewpoints of their structure, signaling, and biological features and possible mechanisms of their functional pleiotropy are discussed.
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Deep Learning for Drug Design: an Artificial Intelligence Paradigm for Drug Discovery in the Big Data Era

TL;DR: Several most powerful and mainstream architectures, including the convolutional neural network, recurrent neural network (RNN), and deep auto-encoder networks (DAENs), for supervised learning and nonsupervised learning are discussed.
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Restructured society and environment: A review on potential technological strategies to control the COVID-19 pandemic.

TL;DR: This study enlightens the various implemented technologies that assists the healthcare systems, government and public in diverse aspects for fighting against COVID-19 and deals with untapped potential technologies that have prospective applications in controlling the pandemic circumstances.
References
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Book

Cybernetics: Or Control and Communication in the Animal and the Machine

TL;DR: In this paper, a study of human control functions and Mechanico-Electrical Systems designed to replace them is presented, with a focus on the human body's ability to control itself.
Journal ArticleDOI

Cybernetics, or Control and Communication in the Animal and the Machine.

TL;DR: This is an extremely thought-provoking book, by the professor of mathematics of the Masachusetts Institute of Technology, whose subject matter ranges from mathematical calculators to the nerves and brain of the human body.
Journal Article

Bukimi no tani [the uncanny valley]

Masaki Mori
- 01 Jan 1970 - 
Journal ArticleDOI

Science and civilisation in China

Joseph Needham, +1 more
- 01 Apr 1963 - 
Journal ArticleDOI

A trial studying approach to predict college achievement.

TL;DR: It is argued that using trial studying is a reliable and valid way to select students for higher education and has predictive validity for study success, has high acceptance by stakeholders, and measures self-regulation in a high-stakes testing context that cannot be measured through self-report questionnaires.
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