↓ Skip to main content

Should Artificial Intelligence Augment Medical Decision Making? The Case for an Autonomy Algorithm

Overview of attention for article published in AMA Journal of Ethics, September 2018
Altmetric Badge

Mentioned by

news
1 news outlet
twitter
21 tweeters

Citations

dimensions_citation
19 Dimensions

Readers on

mendeley
80 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Should Artificial Intelligence Augment Medical Decision Making? The Case for an Autonomy Algorithm
Published in
AMA Journal of Ethics, September 2018
DOI 10.1001/amajethics.2018.902
Pubmed ID
Abstract

A significant proportion of elderly and psychiatric patients do not have the capacity to make health care decisions. We suggest that machine learning technologies could be harnessed to integrate data mined from electronic health records (EHRs) and social media in order to estimate the confidence of the prediction that a patient would consent to a given treatment. We call this process, which takes data about patients as input and derives a confidence estimate for a particular patient's predicted health care-related decision as an output, the autonomy algorithm. We suggest that the proposed algorithm would result in more accurate predictions than existing methods, which are resource intensive and consider only small patient cohorts. This algorithm could become a valuable tool in medical decision-making processes, augmenting the capacity of all people to make health care decisions in difficult situations.

Twitter Demographics

The data shown below were collected from the profiles of 21 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 80 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 80 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 18%
Student > Bachelor 12 15%
Researcher 11 14%
Student > Master 9 11%
Student > Postgraduate 3 4%
Other 14 18%
Unknown 17 21%
Readers by discipline Count As %
Medicine and Dentistry 18 23%
Computer Science 12 15%
Engineering 5 6%
Philosophy 4 5%
Business, Management and Accounting 4 5%
Other 14 18%
Unknown 23 29%