Development of oculomics artificial intelligence for cardiovascular risk factors: A case study in fundus oculomics for HbA1c assessment and clinically relevant considerations for clinicians.

Artificial Intelligence (AI) is transforming healthcare, notably in ophthalmology, where its ability to interpret images and data can significantly enhance disease diagnosis and patient care. Recent developments in oculomics, the integration of ophthalmic features to develop biomarkers for systemic diseases, have demonstrated the potential for providing rapid, non-invasive methods of screening leading to enhance in early detection and improve healthcare quality, particularly in underserved areas. However, the widespread adoption of such AI-based technologies faces challenges primarily related to the trustworthiness of the system.

Perception gaps of patient engagement for patient safety between healthcare professionals and the public in Korea.

Background: To ensure effective patient engagement, patients' efforts, partnerships with healthcare professionals, and the organisation's role in providing safe healthcare settings must be emphasised. Perception gaps regarding patient engagement between the public and healthcare professionals could prevent healthy partnerships from forming and hinder patient engagement activities. This study examined healthcare professionals' perception of patient engagement and compared the findings with that determined for the public in a previous study.