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“Nothing works without the doctor:” Physicians’ perception of clinical decision-making and artificial intelligence
INTRODUCTION: Artificial intelligence–driven decision support systems (AI–DSS) have the potential to help physicians analyze data and facilitate the search for a correct diagnosis or suitable intervention. The potential of such systems is often emphasized. However, implementation in clinical practic...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9807757/ https://www.ncbi.nlm.nih.gov/pubmed/36606050 http://dx.doi.org/10.3389/fmed.2022.1016366 |
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author | Samhammer, David Roller, Roland Hummel, Patrik Osmanodja, Bilgin Burchardt, Aljoscha Mayrdorfer, Manuel Duettmann, Wiebke Dabrock, Peter |
author_facet | Samhammer, David Roller, Roland Hummel, Patrik Osmanodja, Bilgin Burchardt, Aljoscha Mayrdorfer, Manuel Duettmann, Wiebke Dabrock, Peter |
author_sort | Samhammer, David |
collection | PubMed |
description | INTRODUCTION: Artificial intelligence–driven decision support systems (AI–DSS) have the potential to help physicians analyze data and facilitate the search for a correct diagnosis or suitable intervention. The potential of such systems is often emphasized. However, implementation in clinical practice deserves continuous attention. This article aims to shed light on the needs and challenges arising from the use of AI-DSS from physicians’ perspectives. METHODS: The basis for this study is a qualitative content analysis of expert interviews with experienced nephrologists after testing an AI-DSS in a straightforward usage scenario. RESULTS: The results provide insights on the basics of clinical decision-making, expected challenges when using AI-DSS as well as a reflection on the test run. DISCUSSION: While we can confirm the somewhat expectable demand for better explainability and control, other insights highlight the need to uphold classical strengths of the medical profession when using AI-DSS as well as the importance of broadening the view of AI-related challenges to the clinical environment, especially during treatment. Our results stress the necessity for adjusting AI-DSS to shared decision-making. We conclude that explainability must be context-specific while fostering meaningful interaction with the systems available. |
format | Online Article Text |
id | pubmed-9807757 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-98077572023-01-04 “Nothing works without the doctor:” Physicians’ perception of clinical decision-making and artificial intelligence Samhammer, David Roller, Roland Hummel, Patrik Osmanodja, Bilgin Burchardt, Aljoscha Mayrdorfer, Manuel Duettmann, Wiebke Dabrock, Peter Front Med (Lausanne) Medicine INTRODUCTION: Artificial intelligence–driven decision support systems (AI–DSS) have the potential to help physicians analyze data and facilitate the search for a correct diagnosis or suitable intervention. The potential of such systems is often emphasized. However, implementation in clinical practice deserves continuous attention. This article aims to shed light on the needs and challenges arising from the use of AI-DSS from physicians’ perspectives. METHODS: The basis for this study is a qualitative content analysis of expert interviews with experienced nephrologists after testing an AI-DSS in a straightforward usage scenario. RESULTS: The results provide insights on the basics of clinical decision-making, expected challenges when using AI-DSS as well as a reflection on the test run. DISCUSSION: While we can confirm the somewhat expectable demand for better explainability and control, other insights highlight the need to uphold classical strengths of the medical profession when using AI-DSS as well as the importance of broadening the view of AI-related challenges to the clinical environment, especially during treatment. Our results stress the necessity for adjusting AI-DSS to shared decision-making. We conclude that explainability must be context-specific while fostering meaningful interaction with the systems available. Frontiers Media S.A. 2022-12-20 /pmc/articles/PMC9807757/ /pubmed/36606050 http://dx.doi.org/10.3389/fmed.2022.1016366 Text en Copyright © 2022 Samhammer, Roller, Hummel, Osmanodja, Burchardt, Mayrdorfer, Duettmann and Dabrock. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Medicine Samhammer, David Roller, Roland Hummel, Patrik Osmanodja, Bilgin Burchardt, Aljoscha Mayrdorfer, Manuel Duettmann, Wiebke Dabrock, Peter “Nothing works without the doctor:” Physicians’ perception of clinical decision-making and artificial intelligence |
title | “Nothing works without the doctor:” Physicians’ perception of clinical decision-making and artificial intelligence |
title_full | “Nothing works without the doctor:” Physicians’ perception of clinical decision-making and artificial intelligence |
title_fullStr | “Nothing works without the doctor:” Physicians’ perception of clinical decision-making and artificial intelligence |
title_full_unstemmed | “Nothing works without the doctor:” Physicians’ perception of clinical decision-making and artificial intelligence |
title_short | “Nothing works without the doctor:” Physicians’ perception of clinical decision-making and artificial intelligence |
title_sort | “nothing works without the doctor:” physicians’ perception of clinical decision-making and artificial intelligence |
topic | Medicine |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9807757/ https://www.ncbi.nlm.nih.gov/pubmed/36606050 http://dx.doi.org/10.3389/fmed.2022.1016366 |
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