Introduction to Artificial Intelligence (AI) and Machine Learning (ML) in Pathology & Medicine: Generative & Non-Generative AI Basics.

This manuscript serves as an introduction to a comprehensive seven-part review article series on artificial intelligence (AI) and machine learning (ML) and their current and future influence within pathology and medicine. This introductory review provides a comprehensive grasp of this fast-expanding realm and its potential to transform medical diagnosis, workflow, research, and education. Fundamental terminology employed in AI-ML is covered using an extensive dictionary.

Antithrombotic therapy for secondary Stroke prevention in patients with symptomatic large artery atherosclerosis and atrial fibrillation (FIB-CAS study) A single-center experience.

Introduction: Atrial fibrillation (AF) and large artery atherosclerotic diseases are major causes of ischemic stroke and their coexistence increases the risk of stroke and mortality. Research on antithrombotic strategies for AF patients with symptomatic large artery atherosclerosis is limited. This study aims to report a single center's experience regarding the antithrombotic regimens prescribed for this population and the association with stroke recurrence and hemorrhagic events.

Ethical and Bias Considerations in Artificial Intelligence (AI)/Machine Learning.

As artificial intelligence (AI) gains prominence in pathology and medicine, the ethical implications and potential biases within such integrated AI models will require careful scrutiny. Ethics and bias are important considerations in our practice settings, especially as increased number of machine learning (ML) systems are being integrated within our various medical domains. Such machine learning based systems, have demonstrated remarkable capabilities in specified tasks such as but not limited to image recognition, natural language processing, and predictive analytics.

Research active medical imaging professionals in Australian metropolitan health services embrace opportunities and can overcome obstacles to engage in research: A mixed methods study.

Introduction: A minority of medical imaging professionals within Australian metropolitan healthcare services are engaging in research activity as part of an emerging research culture. This study aimed to explore the characteristics and experience of medical imaging professionals who engage in research to identify contextual and individual factors that empower them to participate in research.

Methods: A mixed methods observational study consisting of quantitative (survey) and qualitative (semi-structured interview) components using an interpretative description approach was completed with research active medical imaging professionals (radiographers, nuclear medicine technologists and sonographers).