Ethics

High-Quality CEST Mapping With Lorentzian-Model Informed Neural Representation.

Chemical Exchange Saturation Transfer (CEST) MRI has demonstrated its remarkable ability to enhance the detection of macromolecules and metabolites with low concentrations. While CEST mapping is essential for quantifying molecular information, conventional methods face critical limitations: model-based approaches are constrained by limited sensitivity and robustness depending heavily on parameter ...

An Australasian survey on the use of ChatGPT and other large language models in medical physics.

This study surveyed medical physicists in Australia and New Zealand on their use of large language models (LLMs), particularly ChatGPT. There is currently no literature on the application of ChatGPT and other LLMs by medical physicists. This survey targeted a mixed group of professionals, including clinical medical physicists, registrars, students, and other specialised roles. It reveals that many...

Ethical framework for responsible foundational models in medical imaging.

The emergence of foundational models represents a paradigm shift in medical imaging, offering extraordinary capabilities in disease detection, diagnos...

Balancing Act: Generative AI Tools and Scope of Practice in Health Coaching.

This article explores the effects of generative artificial intelligence (genAI) in health coaching, highlighting its potential benefits and ethical c...

The Ethics of Speaking (of) AIs Through the Lens of Natural Language.

This theoretical essay offers a critical exploration of the ethics involved in interacting with and talking about large language models (LLMs) of arti...

Push Forward Clinical Management of Hematological Toxicity due to Lenalidomide Overexposure: Model-Informed Precision Dosing for Chinese Population With Renal Insufficiency.

Dose-dependent hematological toxicity of lenalidomide has been reported previously, and thus, there is a clinical need for dose individualization to m...

Replacing non-biomedical concepts improves embedding of biomedical concepts.

Embeddings are semantically meaningful representations of words in a vector space, commonly used to enhance downstream machine learning applications. ...

Enhancing security in electromagnetic radiation therapy using fuzzy graph theory.

This research investigates the application of fuzzy graph theory to address critical security challenges in electromagnetic radiation therapy systems....

Ethics of biohybrid robotics and invertebrate research: biohybrid robotic jellyfish as a case study.

Invertebrate research ethics has largely been ignored compared to the consideration of higher order animals, but more recent focus has questioned this...

AI as a decision support tool in forensic image analysis: A pilot study on integrating large language models into crime scene investigation workflows.

This study evaluates the effectiveness of artificial intelligence (AI) tools (ChatGPT-4, Claude, and Gemini) in forensic image analysis of crime scene...

The Emergence of AI in Public Health Is Calling for Operational Ethics to Foster Responsible Uses.

This paper discusses the responsible use of artificial intelligence (AI) in public health and in medicine, and questions the development of AI ethics ...

[Artificial intelligence in medicine-Opportunities and risks from an ethical perspective].

Imaging disciplines, such as ophthalmology, offer a wide range of opportunities for the beneficial use of artificial intelligence (AI). The analysis o...

A hybrid object detection approach for visually impaired persons using pigeon-inspired optimization and deep learning models.

Visually challenged persons include a significant part of the population, and they exist all over the globe. Recently, technology has demonstrated its...

Global Cross-Entropy Loss for Deep Face Recognition.

Contemporary deep face recognition techniques predominantly utilize the Softmax loss function, designed based on the similarities between sample featu...

A deep learning approach: physics-informed neural networks for solving a nonlinear telegraph equation with different boundary conditions.

The nonlinear Telegraph equation appears in a variety of engineering and science problems. This paper presents a deep learning algorithm termed physic...

A semiempirical and machine learning approach for fragment-based structural analysis of non-hydroxamate HDAC3 inhibitors.

Interest in HDAC3 inhibitors (HDAC3i) for pharmacological applications outside of cancer is growing. However, concerns regarding the possible mutageni...

Vertical federated learning based on data subset representation for healthcare application.

BACKGROUND AND OBJECTIVE: Artificial intelligence is increasingly essential for disease classification and clinical diagnosis tasks in healthcare. Giv...

AI Ethics beyond Principles: Strengthening the Life-world Perspective.

The search for ethical guidance in the development of artificial intelligence (AI) systems, especially in healthcare and decision support, remains a c...

UnBias: Unveiling Bias Implications in Deep Learning Models for Healthcare Applications.

The rapid integration of deep learning-powered artificial intelligence systems in diverse applications such as healthcare, credit assessment, employme...

Transfer Learning Prediction of Early Exposures and Genetic Risk Score on Adult Obesity in Two Minority Cohorts.

Due to ethnic heterogeneity in genetic architecture, genetic risk score (GRS) constructed within the European population generally possesses poor port...