Literature DB >> 31227547

Should we be afraid of medical AI?

Ezio Di Nucci1.   

Abstract

I analyse an argument according to which medical artificial intelligence (AI) represents a threat to patient autonomy-recently put forward by Rosalind McDougall in the Journal of Medical Ethics The argument takes the case of IBM Watson for Oncology to argue that such technologies risk disregarding the individual values and wishes of patients. I find three problems with this argument: (1) it confuses AI with machine learning; (2) it misses machine learning's potential for personalised medicine through big data; (3) it fails to distinguish between evidence-based advice and decision-making within healthcare. I conclude that how much and which tasks we should delegate to machine learning and other technologies within healthcare and beyond is indeed a crucial question of our time, but in order to answer it, we must be careful in analysing and properly distinguish between the different systems and different delegated tasks. © Author(s) (or their employer(s)) 2019. No commercial re-use. See rights and permissions. Published by BMJ.

Entities:  

Keywords:  ethics

Mesh:

Year:  2019        PMID: 31227547     DOI: 10.1136/medethics-2018-105281

Source DB:  PubMed          Journal:  J Med Ethics        ISSN: 0306-6800            Impact factor:   2.903


  2 in total

Review 1.  Applications of machine learning to diagnosis and treatment of neurodegenerative diseases.

Authors:  Monika A Myszczynska; Poojitha N Ojamies; Alix M B Lacoste; Daniel Neil; Amir Saffari; Richard Mead; Guillaume M Hautbergue; Joanna D Holbrook; Laura Ferraiuolo
Journal:  Nat Rev Neurol       Date:  2020-07-15       Impact factor: 42.937

2.  Shared decision-making and maternity care in the deep learning age: Acknowledging and overcoming inherited defeaters.

Authors:  Keith Begley; Cecily Begley; Valerie Smith
Journal:  J Eval Clin Pract       Date:  2020-11-13       Impact factor: 2.336

  2 in total

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