Literature DB >> 28126242

Artificial intelligence in medicine.

Pavel Hamet1, Johanne Tremblay2.   

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.
Copyright © 2017. Published by Elsevier Inc.

Entities:  

Keywords:  Artificial intelligence; Avatars; Future of medicine; Robots

Mesh:

Year:  2017        PMID: 28126242     DOI: 10.1016/j.metabol.2017.01.011

Source DB:  PubMed          Journal:  Metabolism        ISSN: 0026-0495            Impact factor:   8.694


  149 in total

1.  Why imaging data alone is not enough: AI-based integration of imaging, omics, and clinical data.

Authors:  Andreas Holzinger; Benjamin Haibe-Kains; Igor Jurisica
Journal:  Eur J Nucl Med Mol Imaging       Date:  2019-06-15       Impact factor: 9.236

Review 2.  Deep learning in breast radiology: current progress and future directions.

Authors:  William C Ou; Dogan Polat; Basak E Dogan
Journal:  Eur Radiol       Date:  2021-01-15       Impact factor: 5.315

Review 3.  Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling.

Authors:  Linlin Zhao; Heather L Ciallella; Lauren M Aleksunes; Hao Zhu
Journal:  Drug Discov Today       Date:  2020-07-11       Impact factor: 7.851

4.  Opening the black box of neural networks: methods for interpreting neural network models in clinical applications.

Authors:  Zhongheng Zhang; Marcus W Beck; David A Winkler; Bin Huang; Wilbert Sibanda; Hemant Goyal
Journal:  Ann Transl Med       Date:  2018-06

5.  Ethical principles for the application of artificial intelligence (AI) in nuclear medicine.

Authors:  Geoff Currie; K Elizabeth Hawk; Eric M Rohren
Journal:  Eur J Nucl Med Mol Imaging       Date:  2020-04       Impact factor: 9.236

6.  The use of 3D printing in cardiac surgery.

Authors:  Chin Siang Ong; Narutoshi Hibino
Journal:  J Thorac Dis       Date:  2017-08       Impact factor: 2.895

7.  Deconstructing the diagnostic reasoning of human versus artificial intelligence.

Authors:  Thierry Pelaccia; Germain Forestier; Cédric Wemmert
Journal:  CMAJ       Date:  2019-12-02       Impact factor: 8.262

8.  Artificial intelligence and radiomics in nuclear medicine: potentials and challenges.

Authors:  Cumali Aktolun
Journal:  Eur J Nucl Med Mol Imaging       Date:  2019-12       Impact factor: 9.236

Review 9.  Deep Learning for Drug Design: an Artificial Intelligence Paradigm for Drug Discovery in the Big Data Era.

Authors:  Yankang Jing; Yuemin Bian; Ziheng Hu; Lirong Wang; Xiang-Qun Xie
Journal:  AAPS J       Date:  2018-03-30       Impact factor: 4.009

Review 10.  Application of artificial intelligence in ophthalmology.

Authors:  Xue-Li Du; Wen-Bo Li; Bo-Jie Hu
Journal:  Int J Ophthalmol       Date:  2018-09-18       Impact factor: 1.779

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.