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Translating the Machine: Skills that Human Clinicians Must Develop in the Era of Artificial Intelligence
In coming decades, artificial intelligence (AI) platforms are expected to build on the profound achievements demonstrated in research papers towards implementation in real-world medicine. The implementation of AI systems is likely to be as an adjunct to clinicians rather than a replacement, but it s...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Healthcare
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8770770/ https://www.ncbi.nlm.nih.gov/pubmed/34807411 http://dx.doi.org/10.1007/s40123-021-00430-6 |
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author | Aslam, Tariq M. Hoyle, David C. |
author_facet | Aslam, Tariq M. Hoyle, David C. |
author_sort | Aslam, Tariq M. |
collection | PubMed |
description | In coming decades, artificial intelligence (AI) platforms are expected to build on the profound achievements demonstrated in research papers towards implementation in real-world medicine. The implementation of AI systems is likely to be as an adjunct to clinicians rather than a replacement, but it still has the potential for a revolutionary impact on ophthalmology specifically and medicine in general in terms of addressing crucial scientific, socioeconomic and capacity challenges facing populations worldwide. In this paper we discuss the broad range of skills that clinicians should develop or refine to be able to fully embrace the opportunities that this technology will bring. We highlight the need for an awareness to identify AI systems that might already be in place and the need to be able to properly assess the utility of their outputs to correctly incorporate the AI system into clinical workflows. In a second section we discuss the need for clinicians to cultivate those human skills that are beyond the capabilities of the AI platforms and which should be just as important as ever. We describe the need for such an awareness by providing clinical examples of situations that might in the future arise in human interactions with machine algorithms. We also envisage a harmonious future in which an educated human and machine interaction can be optimised for the best possible patient experience and care. |
format | Online Article Text |
id | pubmed-8770770 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Healthcare |
record_format | MEDLINE/PubMed |
spelling | pubmed-87707702022-02-02 Translating the Machine: Skills that Human Clinicians Must Develop in the Era of Artificial Intelligence Aslam, Tariq M. Hoyle, David C. Ophthalmol Ther Review In coming decades, artificial intelligence (AI) platforms are expected to build on the profound achievements demonstrated in research papers towards implementation in real-world medicine. The implementation of AI systems is likely to be as an adjunct to clinicians rather than a replacement, but it still has the potential for a revolutionary impact on ophthalmology specifically and medicine in general in terms of addressing crucial scientific, socioeconomic and capacity challenges facing populations worldwide. In this paper we discuss the broad range of skills that clinicians should develop or refine to be able to fully embrace the opportunities that this technology will bring. We highlight the need for an awareness to identify AI systems that might already be in place and the need to be able to properly assess the utility of their outputs to correctly incorporate the AI system into clinical workflows. In a second section we discuss the need for clinicians to cultivate those human skills that are beyond the capabilities of the AI platforms and which should be just as important as ever. We describe the need for such an awareness by providing clinical examples of situations that might in the future arise in human interactions with machine algorithms. We also envisage a harmonious future in which an educated human and machine interaction can be optimised for the best possible patient experience and care. Springer Healthcare 2021-11-22 2022-02 /pmc/articles/PMC8770770/ /pubmed/34807411 http://dx.doi.org/10.1007/s40123-021-00430-6 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by-nc/4.0/Open Access This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) . |
spellingShingle | Review Aslam, Tariq M. Hoyle, David C. Translating the Machine: Skills that Human Clinicians Must Develop in the Era of Artificial Intelligence |
title | Translating the Machine: Skills that Human Clinicians Must Develop in the Era of Artificial Intelligence |
title_full | Translating the Machine: Skills that Human Clinicians Must Develop in the Era of Artificial Intelligence |
title_fullStr | Translating the Machine: Skills that Human Clinicians Must Develop in the Era of Artificial Intelligence |
title_full_unstemmed | Translating the Machine: Skills that Human Clinicians Must Develop in the Era of Artificial Intelligence |
title_short | Translating the Machine: Skills that Human Clinicians Must Develop in the Era of Artificial Intelligence |
title_sort | translating the machine: skills that human clinicians must develop in the era of artificial intelligence |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8770770/ https://www.ncbi.nlm.nih.gov/pubmed/34807411 http://dx.doi.org/10.1007/s40123-021-00430-6 |
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