Integrating AI in Wearable Devices with Thermal Sensors for Breast Cancer Detection: A Review and Conceptual Framework

The medical wearable devices market has experienced significant growth, with the potential to improve healthcare through continuous monitoring and management of health conditions. These devices have great potential to enhance breast cancer detection by addressing several limitations of traditional detection techniques, such as Mammography, Ultrasound, and Magnetic Resonance Imaging (MRI), including radiation exposure, false positive and negative rates, high cost, and inaccessibility. In this paper, we review the state of the art of wearable devices embedded with thermal sensors for breast cancer detection, highlighting their advantages as well as the challenges they face. Most of the reviewed devices are in the proof-of-concept stage, so to advance toward clinical implementation, we propose a three-phase AI integration framework—(1) data preparation, (2) model development, and (3) model evaluation. By integrating AI, these devices can provide cost-effective, noninvasive, and accurate early detection of breast abnormalities, particularly beneficial in low-resource settings.