Application of Infrared Thermography and Artificial Intelligence in Healthcare: A Systematic Review of Over a Decade (2013–2024) - Jahmunah Vicnesh; Massimo Salvi; Yuki Hagiwara; Hah Yan Yee; Hasan Mir; Prabal Datta Barua; Subrata Chakraborty; Filippo Molinari; U. Rajendra Acharya (IEEE Access)
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.