Clinical Research Directory

What Should we Measure? A Qualitative Interview Study on Outcomes for Evaluating AI-assisted Medical Image Interpretation.

Background: AI (artificial intelligence) tools are increasingly used to help clinicians interpret medical images such as X-rays, scans, skin photographs and eye images. In most cases the AI does not work alone: a clinician makes the final decision, using the AI as support. Before these tools are used in the NHS, they must be tested to show they are safe and helpful. They are usually tested in a "reader study", where several clinicians each interpret the same set of images, sometimes with the AI's help and sometimes without, so performance can be compared. These studies tend to focus on whether the diagnosis was correct. However, accuracy alone may not measure everything that matters. For example, what happens when the AI gets it wrong, whether clinicians rely on it too much or too little, or how the tool affects efficiency. At present there is no agreed standard for what these studies should measure, so important things are often missed and studies are hard to compare. Aim: The aim of this study is to find out, by talking to people with different perspectives, which outcomes are important when evaluating AI tools that help clinicians read medical images. Objectives: 1. Explore which outcomes people think matter when judging whether an AI tools is safe and good enough to use 2. Understand the reasons behind their views 3. Explore how priorities differ between groups, such as patients and developers. The output will be a list of important candidate outcomes, which will feed into a future project to agree a recommended set of outcomes for these studies (a "core outcome set"). Research value: Without agreement on what to measure, studies of AI tools may overlook outcomes that matter, and their results are difficult to compare and combine. Hearing directly from the people affected, rather than relying only on what previous studies happened to measure, helps ensure the recommended set reflects people's priorities. This will lead to better-designed studies and AI tools that are safer and more useful in practice. Outline of the research: The investigators will interview 24-30 people who have a stake in how these tools are tested and used, including clinicians, researchers, developers, people involved in regulation, and patients and members of the public. Interviews will last 45-60 minutes and will take place using Microsoft Teams. The investigators will ask people what they think is important to measure and why, and then explore which of those things could realistically be measured in a reader study. Risks/benefits: This is a low-risk study, with no direct benefits to participants. The topics concern methodological matters rather than people's personal health, so personal distress is not anticipated. Taking part is voluntary: interviewees can skip any question or stop at any time. The wider benefit will be better testing of AI imaging tools in future. Impact: The interviews will inform the candidate list of outcomes for the future project and the investigators will publish and present the findings.

The Effect of AI-Assisted Nursing Process Training on Nursing Process Competence, Perception and Attitudes Towards Artificial Intelligence in Nurses: A Randomized Controlled Study

This study aims to determine how applied artificial intelligence (AI) training affects nurses' ability to manage the nursing process and their perceptions and attitudes toward AI technology * The nursing process is a scientific, six-stage approach used by nurses to identify patient needs and provide holistic care The research is a randomized controlled trial involving 78 nurses at Yalova Education and Research Hospital . Participants will be split into two groups: Both groups will receive standard theoretical training on the nursing process . The intervention group will receive additional specialized training on using AI tools (such as ChatGPT and Deepseek) to help create nursing care plans through practical case studies . Nurses' skills and views will be measured using specific scales before the training and one month after the intervention to evaluate the training's effectiveness * This study is expected to provide valuable insights into how AI can support clinical decision-making and help healthcare providers adapt to new technologies * The research has been approved by the Yalova University Ethics Committee (Protocol 2026/183) and will be conducted between May and December 2026