Privacy Preserving Technology using Federated Learning and Blockchain in protecting against Adversarial Attacks for Retinal Imaging.

Purpose: Collaboration provides valuable data for robust artificial intelligence (AI) model development. Federated learning (FL) is a privacy enhancing technology that allows collaboration while respecting privacy via the development of models without raw data transfer. However state-of-the-art FL models still face challenges in non-independent and identically distributed (non-i.

Evaluation of Visual Outcomes in Patients With Aberrated Corneas Implanted With the IC-8 Small-Aperture IOL.

Purpose: To assess the visual outcomes in patients with cataract implanted with a small-aperture intraocular lens (IOL) in eyes with aberrated corneas.

Methods: This prospective, non-interventional, single-center clinical study was conducted at Singapore National Eye Centre, Singapore. Twenty-one patients with aberrated corneas had IC-8 IOL (Bausch & Lomb, Inc) implantation.

Cybersecurity in the generative artificial intelligence era.

Generative Artificial Intelligence (GenAI) are algorithms capable of generating original content. The ability of GenAI to learn and generate novel outputs alike human cognition has taken the world by storm and ushered in a new era. In this review, we explore the role of GenAI in healthcare, including clinical, operational, and research applications, and delve into the cybersecurity risks of this technology.

Evaluation of jigsaw collaborative learning strategy on students' learning of clinical pharmacokinetics of special populations.

Objectives: To evaluate the learning gain and students' perceptions towards Jigsaw collaborative learning in comparison with lectures in learning about pharmacokinetic changes in special populations.

Methods: Undergraduates learn about A-D-M-E of specific populations via Jigsaw collaborative learning and didactic lectures. Pre- and post-lesson quizzes were conducted to evaluate the effectiveness of the teaching method in terms of knowledge gain.

Clinical performance of automated machine learning: A systematic review.

Introduction: Automated machine learning (autoML) removes technical and technological barriers to building artificial intelligence models. We aimed to summarise the clinical applications of autoML, assess the capabilities of utilised platforms, evaluate the quality of the evidence trialling autoML, and gauge the performance of autoML platforms relative to conventionally developed models, as well as each other.

Method: This review adhered to a prospectively registered protocol (PROSPERO identifier CRD42022344427).