Literature DB >> 34862549

The Artificial Intelligence Doctor: Considerations for the Clinical Implementation of Ethical AI.

Julius M Kernbach1,2, Karlijn Hakvoort3,4, Jonas Ort3,4, Hans Clusmann4, Georg Neuloh4, Daniel Delev3,4.   

Abstract

The applications of artificial intelligence (AI) and machine learning (ML) in modern medicine are growing exponentially, and new developments are fast-paced. However, the lack of trust and appropriate legislation hinder its clinical implementation. Recently, there is a clear increase of directives and considerations on Ethical AI. However, most literature broadly deals with ethical tensions on a meta-level without offering hands-on advice in practice. In this article, we non-exhaustively cover basic practical guidelines regarding AI-specific ethical aspects, including transparency and explicability, equity and mitigation of biases, and lastly, liability.
© 2022. The Author(s), under exclusive license to Springer Nature Switzerland AG.

Entities:  

Keywords:  Artificial intelligence; Deep learning; Machine learning; Neurooncology; Neurosurgery

Mesh:

Year:  2022        PMID: 34862549     DOI: 10.1007/978-3-030-85292-4_29

Source DB:  PubMed          Journal:  Acta Neurochir Suppl        ISSN: 0065-1419


  10 in total

1.  Do physicians value decision support? A look at the effect of decision support systems on physician opinion.

Authors:  Stephan Dreiseitl; Michael Binder
Journal:  Artif Intell Med       Date:  2005-01       Impact factor: 5.326

2.  Learning hierarchical features for scene labeling.

Authors:  Clément Farabet; Camille Couprie; Laurent Najman; Yann Lecun
Journal:  IEEE Trans Pattern Anal Mach Intell       Date:  2013-08       Impact factor: 6.226

Review 3.  Machine learning: Trends, perspectives, and prospects.

Authors:  M I Jordan; T M Mitchell
Journal:  Science       Date:  2015-07-17       Impact factor: 47.728

4.  Can Deep Learning Improve Genomic Prediction of Complex Human Traits?

Authors:  Pau Bellot; Gustavo de Los Campos; Miguel Pérez-Enciso
Journal:  Genetics       Date:  2018-08-31       Impact factor: 4.562

5.  Explainable machine-learning predictions for the prevention of hypoxaemia during surgery.

Authors:  Scott M Lundberg; Bala Nair; Monica S Vavilala; Mayumi Horibe; Michael J Eisses; Trevor Adams; David E Liston; Daniel King-Wai Low; Shu-Fang Newman; Jerry Kim; Su-In Lee
Journal:  Nat Biomed Eng       Date:  2018-10-10       Impact factor: 25.671

Review 6.  Machine Learning in Medicine.

Authors:  Rahul C Deo
Journal:  Circulation       Date:  2015-11-17       Impact factor: 29.690

Review 7.  A guide to deep learning in healthcare.

Authors:  Andre Esteva; Alexandre Robicquet; Bharath Ramsundar; Volodymyr Kuleshov; Mark DePristo; Katherine Chou; Claire Cui; Greg Corrado; Sebastian Thrun; Jeff Dean
Journal:  Nat Med       Date:  2019-01-07       Impact factor: 53.440

8.  Precision histology: how deep learning is poised to revitalize histomorphology for personalized cancer care.

Authors:  Ugljesa Djuric; Gelareh Zadeh; Kenneth Aldape; Phedias Diamandis
Journal:  NPJ Precis Oncol       Date:  2017-06-19

9.  Mechanistic machine learning: how data assimilation leverages physiologic knowledge using Bayesian inference to forecast the future, infer the present, and phenotype.

Authors:  David J Albers; Matthew E Levine; Andrew Stuart; Lena Mamykina; Bruce Gluckman; George Hripcsak
Journal:  J Am Med Inform Assoc       Date:  2018-10-01       Impact factor: 4.497

10.  Prediction of premature all-cause mortality: A prospective general population cohort study comparing machine-learning and standard epidemiological approaches.

Authors:  Stephen F Weng; Luis Vaz; Nadeem Qureshi; Joe Kai
Journal:  PLoS One       Date:  2019-03-27       Impact factor: 3.240

  10 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.