Designing an Ethical and Secure Pain Estimation System Using AI Sandbox for Contactless Healthcare - Khan, Umair Ali; Alamäki, Ari (International Journal of Online and Biomedical Engineering (iJOE), 2023)
Pain estimation in patients having communication difficulties is vital for preventing adverse consequences such as misdiagnosis, delayed treatment, and increased suffering. Traditional pain assessment tools relying on observer-based ratings and patient self-reporting are hampered by subjectivity and the need for continuous human monitoring, which have the potential to lead to inaccurate or delayed pain estimation. This paper presents an extensive literature review, a conceptual framework, and a systematic procedure for helping researchers develop a contactless, multimodal pain estimation system that leverages AI-based automation of standard pain assessment tools and scales within an AI sandbox environment. Our proposed concept aims to improve the efficiency of traditional pain estimation systems while reducing subjectivity and physical contact. This approach offers potential benefits, such as more accurate and timely pain assessment, reduced burden on healthcare professionals, and improved patient experiences. Moreover, the integration of the AI sandbox allows researchers and developers to experiment with AI models, algorithms, and systems safely and securely, ensuring that AI systems are reliable and robust before deployment. We also discuss potential challenges and ethical considerations related to the use of AI in pain estimation, emphasizing the importance of addressing these concerns to ensure the safe and responsible integration of this technology into healthcare systems. The paper lays a foundation for future research and innovation in pain management, ultimately contributing to better patient care and advancements in the field.