Clinical Research Directory

Patient Aid for Theory-Driven Health Communication (PATH): Translating Goal-Power Theory Into an Instrument and Generative AI Support for Palliative Care Communication

The goal of this clinical trial is to learn whether an artificial intelligence (AI)-supported communication tool can help improve communication between people receiving palliative care and their healthcare providers. The study will also evaluate a new questionnaire designed to measure the quality of healthcare communication. The main questions this study aims to answer are: 1. Can the new questionnaire accurately measure the quality of communication between participants and healthcare providers? 2. Does using the AI-supported communication tool, called NurseChat+, help participants feel more confident and prepared before meeting with their healthcare team? 3. Does NurseChat+ improve the quality of communication during palliative care visits? Researchers will compare participants who use NurseChat+ together with their usual care to participants who receive usual care alone to see whether the tool improves communication outcomes. The study will be conducted in three stages. First, researchers will develop and test a questionnaire that measures healthcare communication. Next, they will develop and refine the NurseChat+ tool. Finally, they will test the tool in adults receiving palliative care at National Taiwan University Hospital. Participants in the final stage of the study will be randomly assigned to one of two groups. One group will use NurseChat+ before their scheduled medical visits in addition to receiving usual care, while the other group will receive usual care only. Participants will complete questionnaires about their communication experiences during the study.

Large Language Models for Dental Radiology Report Generation From Structured Textual Data

The purpose of this observational methodological study is to evaluate whether large language models can transform structured dental radiology data into clear narrative radiology reports. Large language models are computer programs that can generate text from information provided to them. In this study, the input will consist of organized dental radiology findings, such as chart-style or diagram-based information about teeth and surrounding structures. Dental radiology reports are used by dentists and other health care providers to understand imaging findings and support clinical documentation. Preparing narrative reports may be time-consuming, and the wording of reports may vary between clinicians. This study will examine whether language-model-assisted report generation can produce reports that are complete, accurate, understandable, and clinically useful. The study will compare reports generated with support from large language models with traditionally prepared reports. Researchers will also assess how the wording of the prompt and selected model parameters influence report quality. In addition, the study will analyze errors and safety risks in generated reports and evaluate whether such a system could be practical in a dental radiology workflow. The language model will not make treatment decisions, and generated reports will be used for research evaluation only.