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Clinical Applications of Machine Learning Algorithms: Beyond the Black Box
| Content Provider | Scilit |
|---|---|
| Author | Watson, David Krutzinna, Jenny Bruce, Ian Griffiths, Christopher McInnes, Iain Barnes, Michael Floridi, Luciano |
| Copyright Year | 2019 |
| Description | Journal: Ssrn Electronic Journal Machine learning algorithms may radically improve our ability to diagnose and treat disease. For moral, legal, and scientific reasons, it is essential that doctors and patients be able to understand and explain the predictions of these models. Scalable, customisable, and ethical solutions can be achieved by working together with relevant stakeholders, including patients, data scientists, and policy makers. |
| Related Links | http://eprints.gla.ac.uk/182884/1/182884.pdf https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3352454 |
| ISSN | 10914358 |
| e-ISSN | 15565068 |
| DOI | 10.2139/ssrn.3352454 |
| Journal | Ssrn Electronic Journal |
| Language | English |
| Publisher | Elsevier BV |
| Publisher Date | 2019-03-12 |
| Access Restriction | Open |
| Subject Keyword | Journal: Ssrn Electronic Journal Black Box Algorithms |
| Content Type | Text |
| Resource Type | Article |
| Subject | Public Health, Environmental and Occupational Health Psychiatry and Mental Health |