Application of Infrared Thermography and Artificial Intelligence in Healthcare: A Systematic Review of Over a Decade (2013–2024)
Infrared thermography (IRT) is a non-invasive, radiation-free imaging technique that uses an infrared (IR) camera to record and produce an image using IR radiation emitted from the body. IRT imaging has shown promise as a screening method for breast cancer, diabetic foot ulcers, and dry eye disease,...
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| Format: | Article |
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10815963/ |
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| author | Jahmunah Vicnesh Massimo Salvi Yuki Hagiwara Hah Yan Yee Hasan Mir Prabal Datta Barua Subrata Chakraborty Filippo Molinari U. Rajendra Acharya |
| author_facet | Jahmunah Vicnesh Massimo Salvi Yuki Hagiwara Hah Yan Yee Hasan Mir Prabal Datta Barua Subrata Chakraborty Filippo Molinari U. Rajendra Acharya |
| author_sort | Jahmunah Vicnesh |
| collection | DOAJ |
| description | Infrared thermography (IRT) is a non-invasive, radiation-free imaging technique that uses an infrared (IR) camera to record and produce an image using IR radiation emitted from the body. IRT imaging has shown promise as a screening method for breast cancer, diabetic foot ulcers, and dry eye disease, among other medical disorders. The aim of this systematic review is to present a complete overview of the applications of artificial intelligence (AI) techniques with IRT imaging for medical decision support systems over the course of the last ten years (2013–2024). Several scientific databases, including PubMed, IEEE, and Google Scholar, were searched using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. After meeting the requirements for inclusion, 131 papers were selected. The reviewed studies demonstrated how various AI techniques, including deep learning and classical machine learning, can be used to develop automated diagnosis systems using IRT images. The efficacy of these AI systems differed depending on the medical field; for example, they could identify dry eye disease with 90–100% accuracy, classify diabetic foot ulcers with 85–95% accuracy, and detect breast cancer with 80–100% accuracy. This review highlights the value of IRT imaging in early disease detection, especially when combined with AI techniques. This work discusses challenges in using deep learning (DL) models in healthcare, including data scarcity and ethical considerations. It also, proposes three main recommendations: dataset standardization for ethical data management, clear governance models for ethical practices, and the use of Multimodal Large Language Models (MLLMs) to address explainability issues. |
| format | Article |
| id | doaj-art-55fd13f5e11746b690dad707c5d368e7 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-55fd13f5e11746b690dad707c5d368e72025-01-14T00:02:17ZengIEEEIEEE Access2169-35362025-01-01135949597310.1109/ACCESS.2024.352225110815963Application of Infrared Thermography and Artificial Intelligence in Healthcare: A Systematic Review of Over a Decade (2013–2024)Jahmunah Vicnesh0Massimo Salvi1https://orcid.org/0000-0001-7225-7401Yuki Hagiwara2https://orcid.org/0000-0002-5418-738XHah Yan Yee3https://orcid.org/0000-0003-1232-3328Hasan Mir4https://orcid.org/0000-0002-6863-3002Prabal Datta Barua5Subrata Chakraborty6https://orcid.org/0000-0002-0102-5424Filippo Molinari7https://orcid.org/0000-0003-1150-2244U. Rajendra Acharya8School of Engineering (SEG), Nanyang Polytechnic, Jurong West, SingaporeDepartment of Electronics and Telecommunications, Biolab, PolitoBIOMed Laboratory, Politecnico di Torino, Turin, ItalyFraunhofer Institute for Cognitive Systems IKS, Munich, GermanyOphthalmology and Visual Sciences Department, Khoo Teck Puat Hospital, Fusionopolis Place, SingaporeDepartment of Electrical Engineering, American University of Sharjah, Sharjah, United Arab EmiratesSchool of Business (Information System), University of Southern Queensland, Springfield, Springfield, QLD, AustraliaSchool of Science and Technology, Faculty of Science, Agriculture, Business and Law, University of New England, Armidale, NSW, AustraliaDepartment of Electronics and Telecommunications, Biolab, PolitoBIOMed Laboratory, Politecnico di Torino, Turin, ItalySchool of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, QLD, AustraliaInfrared thermography (IRT) is a non-invasive, radiation-free imaging technique that uses an infrared (IR) camera to record and produce an image using IR radiation emitted from the body. IRT imaging has shown promise as a screening method for breast cancer, diabetic foot ulcers, and dry eye disease, among other medical disorders. The aim of this systematic review is to present a complete overview of the applications of artificial intelligence (AI) techniques with IRT imaging for medical decision support systems over the course of the last ten years (2013–2024). Several scientific databases, including PubMed, IEEE, and Google Scholar, were searched using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. After meeting the requirements for inclusion, 131 papers were selected. The reviewed studies demonstrated how various AI techniques, including deep learning and classical machine learning, can be used to develop automated diagnosis systems using IRT images. The efficacy of these AI systems differed depending on the medical field; for example, they could identify dry eye disease with 90–100% accuracy, classify diabetic foot ulcers with 85–95% accuracy, and detect breast cancer with 80–100% accuracy. This review highlights the value of IRT imaging in early disease detection, especially when combined with AI techniques. This work discusses challenges in using deep learning (DL) models in healthcare, including data scarcity and ethical considerations. It also, proposes three main recommendations: dataset standardization for ethical data management, clear governance models for ethical practices, and the use of Multimodal Large Language Models (MLLMs) to address explainability issues.https://ieeexplore.ieee.org/document/10815963/Infrared thermographyartificial intelligencemedical imagingbreast cancer detectionmachine learningdeep learning |
| spellingShingle | Jahmunah Vicnesh Massimo Salvi Yuki Hagiwara Hah Yan Yee Hasan Mir Prabal Datta Barua Subrata Chakraborty Filippo Molinari U. Rajendra Acharya Application of Infrared Thermography and Artificial Intelligence in Healthcare: A Systematic Review of Over a Decade (2013–2024) IEEE Access Infrared thermography artificial intelligence medical imaging breast cancer detection machine learning deep learning |
| title | Application of Infrared Thermography and Artificial Intelligence in Healthcare: A Systematic Review of Over a Decade (2013–2024) |
| title_full | Application of Infrared Thermography and Artificial Intelligence in Healthcare: A Systematic Review of Over a Decade (2013–2024) |
| title_fullStr | Application of Infrared Thermography and Artificial Intelligence in Healthcare: A Systematic Review of Over a Decade (2013–2024) |
| title_full_unstemmed | Application of Infrared Thermography and Artificial Intelligence in Healthcare: A Systematic Review of Over a Decade (2013–2024) |
| title_short | Application of Infrared Thermography and Artificial Intelligence in Healthcare: A Systematic Review of Over a Decade (2013–2024) |
| title_sort | application of infrared thermography and artificial intelligence in healthcare a systematic review of over a decade 2013 x2013 2024 |
| topic | Infrared thermography artificial intelligence medical imaging breast cancer detection machine learning deep learning |
| url | https://ieeexplore.ieee.org/document/10815963/ |
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