Ethical Challenges in the Use of Artificial Intelligence for Glaucoma Diagnosis
DOI:
https://doi.org/10.63075/me6nze78Abstract
The combination of Artificial Intelligence, in particular deep learning, into glaucoma diagnosis assure a model alteration in ophthalmology. By analyzing compound ocular images like fundus images and Optical Coherence Tomography (OCT), AI can achieve symptomatic performance, offers potential for timely detection and improve management. Nonetheless, to ensure impartiality, secure and reliable implementation a complex collection of ethical objections must be addressed . The ethical concerns primarily originate from the use of AI in glaucoma detection are examined in this paper. Key issues known comprise algorithmic partiality and health inequity, originated from unbalanced training data, lack of maintainability, “black box” problem that reduces clinical responsibility and trust; privacy and security of data in handling retinal images, the capability for loss of expertise of ophthalmologists. The paper wraps up with a array of proposals for the accountable growth and implementation of AI in this domain, Highlighting the need for different and balanced datasets, the progress of AI techniques, strong data control, ongoing clinician education, and the application of efficient human-AI cooperation structures . In advance addressing these ethical requirements is important to utilize the full potential of AI while maintaining the basic principles of medical ethics.
Keywords: Glaucoma, Deep Learning, Convolutional Neural Networks (CNN), Medical Image Analysis, Fundus Imaging, Optical Coherence Tomography (OCT), Survey, Computer-Aided Diagnosis (CAD).
