Lessons on AI implementation from senior clinical practitioners: An exploratory qualitative study in medical imaging and radiotherapy in the UK.

Introduction: Artificial Intelligence (AI) has the potential to transform medical imaging and radiotherapy; both fields where radiographers' use of AI tools is increasing. This study aimed to explore the views of those professionals who are now using AI tools.

Methods: A small-scale exploratory research process was employed, where qualitative data was obtained from five UK-based participants; all professionals working in medical imaging and radiotherapy who use AI in clinical practice.

Paramedics as Researchers: A Systematic Review of Paramedic Perspectives of Engaging in Research Activity From Training to Practice.

Background: The need for a stronger evidence-base in paramedicine has precipitated a rapid development of prehospital research agendas. Paramedics are increasingly involved in research, leading to changes in their role. Yet, the integration of research responsibilities has proven to be challenging, resulting in varying attitudes and levels of engagement.

TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods.

The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) statement was published in 2015 to provide the minimum reporting recommendations for studies developing or evaluating the performance of a prediction model. Methodological advances in the field of prediction have since included the widespread use of artificial intelligence (AI) powered by machine learning methods to develop prediction models. An update to the TRIPOD statement is thus needed.