Literature DB >> 29126825

Artificial Intelligence in Medical Practice: The Question to the Answer?

D Douglas Miller1, Eric W Brown2.   

Abstract

Computer science advances and ultra-fast computing speeds find artificial intelligence (AI) broadly benefitting modern society-forecasting weather, recognizing faces, detecting fraud, and deciphering genomics. AI's future role in medical practice remains an unanswered question. Machines (computers) learn to detect patterns not decipherable using biostatistics by processing massive datasets (big data) through layered mathematical models (algorithms). Correcting algorithm mistakes (training) adds to AI predictive model confidence. AI is being successfully applied for image analysis in radiology, pathology, and dermatology, with diagnostic speed exceeding, and accuracy paralleling, medical experts. While diagnostic confidence never reaches 100%, combining machines plus physicians reliably enhances system performance. Cognitive programs are impacting medical practice by applying natural language processing to read the rapidly expanding scientific literature and collate years of diverse electronic medical records. In this and other ways, AI may optimize the care trajectory of chronic disease patients, suggest precision therapies for complex illnesses, reduce medical errors, and improve subject enrollment into clinical trials.
Copyright © 2018 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Analytics; Artificial intelligence; Big data; Chronic disease; Deep learning; Electronic medical record; Machine learning; Medical imaging; Natural language processing; Neural networks; Precision medicine

Mesh:

Year:  2017        PMID: 29126825     DOI: 10.1016/j.amjmed.2017.10.035

Source DB:  PubMed          Journal:  Am J Med        ISSN: 0002-9343            Impact factor:   4.965


  85 in total

1.  Using electronic health records for clinical trials: Where do we stand and where can we go?

Authors:  Kimberly A Mc Cord; Lars G Hemkens
Journal:  CMAJ       Date:  2019-02-04       Impact factor: 8.262

Review 2.  Artificial Intelligence Transforms the Future of Health Care.

Authors:  Nariman Noorbakhsh-Sabet; Ramin Zand; Yanfei Zhang; Vida Abedi
Journal:  Am J Med       Date:  2019-01-31       Impact factor: 4.965

Review 3.  Artificial Intelligence for Mental Health and Mental Illnesses: an Overview.

Authors:  Sarah Graham; Colin Depp; Ellen E Lee; Camille Nebeker; Xin Tu; Ho-Cheol Kim; Dilip V Jeste
Journal:  Curr Psychiatry Rep       Date:  2019-11-07       Impact factor: 5.285

4.  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

Review 5.  [Enhanced imaging in urological endoscopy].

Authors:  M C Kriegmair; S Hein; D S Schoeb; H Zappe; R Suárez-Ibarrola; F Waldbillig; B Gruene; P-F Pohlmann; F Praus; K Wilhelm; C Gratzke; A Miernik; C Bolenz
Journal:  Urologe A       Date:  2020-12-10       Impact factor: 0.639

Review 6.  Clinical Decision Support Systems.

Authors:  Andreas Teufel; Harald Binder
Journal:  Visc Med       Date:  2021-09-28

Review 7.  Rethinking drug design in the artificial intelligence era.

Authors:  Petra Schneider; W Patrick Walters; Alleyn T Plowright; Norman Sieroka; Jennifer Listgarten; Robert A Goodnow; Jasmin Fisher; Johanna M Jansen; José S Duca; Thomas S Rush; Matthias Zentgraf; John Edward Hill; Elizabeth Krutoholow; Matthias Kohler; Jeff Blaney; Kimito Funatsu; Chris Luebkemann; Gisbert Schneider
Journal:  Nat Rev Drug Discov       Date:  2019-12-04       Impact factor: 84.694

Review 8.  Current applications of artificial intelligence combined with urine detection in disease diagnosis and treatment.

Authors:  Jun Tan; Feng Qin; Jiuhong Yuan
Journal:  Transl Androl Urol       Date:  2021-04

Review 9.  Requirements and reliability of AI in the medical context.

Authors:  Yoganand Balagurunathan; Ross Mitchell; Issam El Naqa
Journal:  Phys Med       Date:  2021-03-13       Impact factor: 2.685

10.  A deep database of medical abbreviations and acronyms for natural language processing.

Authors:  Lisa Grossman Liu; Raymond H Grossman; Elliot G Mitchell; Chunhua Weng; Karthik Natarajan; George Hripcsak; David K Vawdrey
Journal:  Sci Data       Date:  2021-06-02       Impact factor: 6.444

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