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Using AI to Detect Pain through Facial Expressions: A Review
Pain assessment is a complex task largely dependent on the patient’s self-report. Artificial intelligence (AI) has emerged as a promising tool for automating and objectifying pain assessment through the identification of pain-related facial expressions. However, the capabilities and potential of AI...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10215219/ https://www.ncbi.nlm.nih.gov/pubmed/37237618 http://dx.doi.org/10.3390/bioengineering10050548 |
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author | De Sario, Gioacchino D. Haider, Clifton R. Maita, Karla C. Torres-Guzman, Ricardo A. Emam, Omar S. Avila, Francisco R. Garcia, John P. Borna, Sahar McLeod, Christopher J. Bruce, Charles J. Carter, Rickey E. Forte, Antonio J. |
author_facet | De Sario, Gioacchino D. Haider, Clifton R. Maita, Karla C. Torres-Guzman, Ricardo A. Emam, Omar S. Avila, Francisco R. Garcia, John P. Borna, Sahar McLeod, Christopher J. Bruce, Charles J. Carter, Rickey E. Forte, Antonio J. |
author_sort | De Sario, Gioacchino D. |
collection | PubMed |
description | Pain assessment is a complex task largely dependent on the patient’s self-report. Artificial intelligence (AI) has emerged as a promising tool for automating and objectifying pain assessment through the identification of pain-related facial expressions. However, the capabilities and potential of AI in clinical settings are still largely unknown to many medical professionals. In this literature review, we present a conceptual understanding of the application of AI to detect pain through facial expressions. We provide an overview of the current state of the art as well as the technical foundations of AI/ML techniques used in pain detection. We highlight the ethical challenges and the limitations associated with the use of AI in pain detection, such as the scarcity of databases, confounding factors, and medical conditions that affect the shape and mobility of the face. The review also highlights the potential impact of AI on pain assessment in clinical practice and lays the groundwork for further study in this area. |
format | Online Article Text |
id | pubmed-10215219 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-102152192023-05-27 Using AI to Detect Pain through Facial Expressions: A Review De Sario, Gioacchino D. Haider, Clifton R. Maita, Karla C. Torres-Guzman, Ricardo A. Emam, Omar S. Avila, Francisco R. Garcia, John P. Borna, Sahar McLeod, Christopher J. Bruce, Charles J. Carter, Rickey E. Forte, Antonio J. Bioengineering (Basel) Review Pain assessment is a complex task largely dependent on the patient’s self-report. Artificial intelligence (AI) has emerged as a promising tool for automating and objectifying pain assessment through the identification of pain-related facial expressions. However, the capabilities and potential of AI in clinical settings are still largely unknown to many medical professionals. In this literature review, we present a conceptual understanding of the application of AI to detect pain through facial expressions. We provide an overview of the current state of the art as well as the technical foundations of AI/ML techniques used in pain detection. We highlight the ethical challenges and the limitations associated with the use of AI in pain detection, such as the scarcity of databases, confounding factors, and medical conditions that affect the shape and mobility of the face. The review also highlights the potential impact of AI on pain assessment in clinical practice and lays the groundwork for further study in this area. MDPI 2023-05-02 /pmc/articles/PMC10215219/ /pubmed/37237618 http://dx.doi.org/10.3390/bioengineering10050548 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review De Sario, Gioacchino D. Haider, Clifton R. Maita, Karla C. Torres-Guzman, Ricardo A. Emam, Omar S. Avila, Francisco R. Garcia, John P. Borna, Sahar McLeod, Christopher J. Bruce, Charles J. Carter, Rickey E. Forte, Antonio J. Using AI to Detect Pain through Facial Expressions: A Review |
title | Using AI to Detect Pain through Facial Expressions: A Review |
title_full | Using AI to Detect Pain through Facial Expressions: A Review |
title_fullStr | Using AI to Detect Pain through Facial Expressions: A Review |
title_full_unstemmed | Using AI to Detect Pain through Facial Expressions: A Review |
title_short | Using AI to Detect Pain through Facial Expressions: A Review |
title_sort | using ai to detect pain through facial expressions: a review |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10215219/ https://www.ncbi.nlm.nih.gov/pubmed/37237618 http://dx.doi.org/10.3390/bioengineering10050548 |
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