Ethical AI and Sustainability Governance in Industry 5.0 Healthcare

Although eye rubbing is known to contribute significantly to the progression of keratoconus and corneal ectasia following refractive surgery, no commonly used device objectively measures this behavior. A crucial gap still exists in the integration of real-time sustainability governance systems for healthcare applications with ethical artificial intelligence (AI) frameworks, despite notable advancements in AI and the Internet of Things (IoT). We addressed this by creating a smartwatch-based application that uses deep learning algorithms, more precisely a long short-term memory (LSTM) architecture, to analyze sensor data collected during simulated eye rubbing and normal daily activities performed by four trained participants representing both patient and healthy control behaviors. The result allows for proactive interventions that can improve patient outcomes in the treatment of keratoconus and post-refractive surgery care. This study highlights the potential of combining ethical AI and IoT solutions to improve sustainability governance in healthcare and confirms that automated detection of eye rubbing using cutting-edge deep learning techniques is feasible.