Archive · 2024-01-12
AI ethics on Friday, 12 January 2024
4 items published this day, across 1 categories.
Research (4)
Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare
Integrating Artificial Intelligence (AI) in healthcare represents a transformative shift with substantial potential for enhancing patient care. This paper critically examines this integration, confronting significant ethical, legal, and technological challenges, particularly in patient privacy, decision-making autonomy, and data integrity. A structured exploration of these issues focuses on Differential Privacy as a critical method for preserving patient confidentiality in AI-driven healthcare s
Unsupervised spatially embedded deep representation of spatial transcriptomics
Optimal integration of transcriptomics data and associated spatial information is essential towards fully exploiting spatial transcriptomics to dissect tissue heterogeneity and map out inter-cellular communications. We present SEDR, which uses a deep autoencoder coupled with a masked self-supervised learning mechanism to construct a low-dimensional latent representation of gene expression, which is then simultaneously embedded with the corresponding spatial information through a variational grap
Contributing roles of mitochondrial dysfunction and hepatocyte apoptosis in liver diseases through oxidative stress, post-translational modifications, inflammation, and intestinal barrier dysfunction
This review provides an update on recent findings from basic, translational, and clinical studies on the molecular mechanisms of mitochondrial dysfunction and apoptosis of hepatocytes in multiple liver diseases, including but not limited to alcohol-associated liver disease (ALD), metabolic dysfunction-associated steatotic liver disease (MASLD), and drug-induced liver injury (DILI). While the ethanol-inducible cytochrome P450-2E1 (CYP2E1) is mainly responsible for oxidizing binge alcohol via the
Using Generative Artificial Intelligence Tools to Explain and Enhance Experiential Learning for Authentic Assessment
The emergence of generative artificial intelligence (GenAI) requires innovative educational environments to leverage this technology effectively to address concerns like academic integrity, plagiarism, and others. Additionally, higher education needs effective pedagogies to achieve intended learning outcomes. This emphasizes the need to redesign active learning experiences in the GenAI era. Authentic assessment and experiential learning are two possible meaningful alternatives in this context. A