Literature DB >> 32376538

[Artificial intelligence empowers laboratory medicine in Industry 4.0].

Quan Zhou1, Suwen Qi2, Bin Xiao1, Qiaoliang Li2, Zhaohui Sun1, Linhai Li1.   

Abstract

Since 2017, China, the United States, and the European Union have successively issued national-level artificial intelligence (AI) strategic development plans, and the human history is about to witness the 4th industrial revolution with the theme of "intelligence". In the field of medical testing, the explosive growth of AI theories and technologies also provide a new direction for the development of medical testing theory, methods and applications. We review the evolution of AI and the recent progress in three major elements of AI, namely algorithms, data and computing power, and elaborate on the combined innovation of "AI + testing" in light of the key application dimensions of medical testing. The major applications include specimen collection robots, sample dilution robots and sample transfer robots involved in the processing of test specimens; test item mining such as tumor markers and pharmacogenomics; cytomorphology, laboratory medicine data processing, auxiliary diagnostic models, and internet-based medical tests. With the advent of the era of Industry 4.0, AI technology will promote the development of medical testing from automation to a highly intelligent stage.

Entities:  

Keywords:  artificial intelligence; big data; intellectualization; laboratory medicine

Mesh:

Year:  2020        PMID: 32376538      PMCID: PMC7086124          DOI: 10.12122/j.issn.1673-4254.2020.02.23

Source DB:  PubMed          Journal:  Nan Fang Yi Ke Da Xue Xue Bao        ISSN: 1673-4254


  19 in total

1.  Computer-based consultations in clinical therapeutics: explanation and rule acquisition capabilities of the MYCIN system.

Authors:  E H Shortliffe; R Davis; S G Axline; B G Buchanan; C C Green; S N Cohen
Journal:  Comput Biomed Res       Date:  1975-08

2.  ¹H NMR-based serum metabolic profiling in compensated and decompensated cirrhosis.

Authors:  Su-Wen Qi; Zhi-Guang Tu; Wu-Jian Peng; Lin-Xian Wang; Xin Ou-Yang; An-Ji Cai; Yong Dai
Journal:  World J Gastroenterol       Date:  2012-01-21       Impact factor: 5.742

3.  A modular framework for the automatic classification of chromosomes in Q-band images.

Authors:  Enea Poletti; Enrico Grisan; Alfredo Ruggeri
Journal:  Comput Methods Programs Biomed       Date:  2011-10-02       Impact factor: 5.428

4.  A novel approach using MALDI-TOF/TOF mass spectrometry and prestructured sample supports (AnchorChip Technology) for proteomic profiling and protein identification.

Authors:  Sau-Mei Leung; Rebecca L Pitts
Journal:  Methods Mol Biol       Date:  2008

5.  Implementation of a patient safety program at a tertiary health system: A longitudinal analysis of interventions and serious safety events.

Authors:  Douglas P Cropper; Nidal H Harb; Patricia A Said; Jon H Lemke; Nicolas W Shammas
Journal:  J Healthc Risk Manag       Date:  2018-03-31

6.  Liver tissue metabolic profiling and pathways of non-alcoholic steatohepatitis in rats.

Authors:  Suwen Qi; Si Huang; Xin Chen; Qin Huo; Ni Xie; Jun Xia
Journal:  Hepatol Res       Date:  2017-04-05       Impact factor: 4.288

7.  [In silico data mining of the human programmed cell death 5 (PDCD5) sequences].

Authors:  Ying Zheng; Dalong Ma
Journal:  Beijing Da Xue Xue Bao Yi Xue Ban       Date:  2003-08

8.  An efficient method for automatic morphological abnormality detection from human sperm images.

Authors:  Fatemeh Ghasemian; Seyed Abolghasem Mirroshandel; Sara Monji-Azad; Mahnaz Azarnia; Ziba Zahiri
Journal:  Comput Methods Programs Biomed       Date:  2015-09-04       Impact factor: 5.428

9.  Erratum to: Modeling precision treatment of breast cancer.

Authors:  Anneleen Daemen; Obi L Griffith; Laura M Heiser; Nicholas J Wang; Oana M Enache; Zachary Sanborn; Francois Pepin; Steffen Durinck; James E Korkola; Malachi Griffith; Joe S Hur; Nam Huh; Jongsuk Chung; Leslie Cope; Mary Jo Fackler; Christopher Umbricht; Saraswati Sukumar; Pankaj Seth; Vikas P Sukhatme; Lakshmi R Jakkula; Yiling Lu; Gordon B Mills; Raymond J Cho; Eric A Collisson; Laura J Van't Veer; Paul T Spellman; Joe W Gray
Journal:  Genome Biol       Date:  2015-05-12       Impact factor: 13.583

10.  Anticancer drug sensitivity prediction in cell lines from baseline gene expression through recursive feature selection.

Authors:  Zuoli Dong; Naiqian Zhang; Chun Li; Haiyun Wang; Yun Fang; Jun Wang; Xiaoqi Zheng
Journal:  BMC Cancer       Date:  2015-06-30       Impact factor: 4.430

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  1 in total

Review 1.  Clinlabomics: leveraging clinical laboratory data by data mining strategies.

Authors:  Xiaoxia Wen; Ping Leng; Jiasi Wang; Guishu Yang; Ruiling Zu; Xiaojiong Jia; Kaijiong Zhang; Birga Anteneh Mengesha; Jian Huang; Dongsheng Wang; Huaichao Luo
Journal:  BMC Bioinformatics       Date:  2022-09-24       Impact factor: 3.307

  1 in total

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