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Large language models (LLMs) use autoregression to generate text in response to queries. Crafting an appropriate prompt to elicit the desired response from these generative artificial intelligence (AI) models to solve a clinical problem can be a challenge to clinicians who may be unfamiliar with this technology. The use of checklists to generate carefully worded queries can leverage the potential of LLMs as a brainstorming aid for medical problem-solving. Systematically using different prompts to generate the most appropriate differential diagnoses for selected clinical case scenarios, a potential checklist for prompt generation has been created and is reported here.
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http://dx.doi.org/10.59556/japi.72.0518 | DOI Listing |
JMIR Med Inform
September 2025
Departments of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, 600 Tianhe Road, Guangzhou, Guangdong, 510630, China, 86 18922109279, 86 20852523108.
Background: Despite the Coronary Artery Reporting and Data System (CAD-RADS) providing a standardized approach, radiologists continue to favor free-text reports. This preference creates significant challenges for data extraction and analysis in longitudinal studies, potentially limiting large-scale research and quality assessment initiatives.
Objective: To evaluate the ability of the generative pre-trained transformer (GPT)-4o model to convert real-world coronary computed tomography angiography (CCTA) free-text reports into structured data and automatically identify CAD-RADS categories and P categories.
J Craniofac Surg
September 2025
Department of Breast Plastic Surgery, Plastic Surgery Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shijingshan, Beijing, China.
Background: With the development of artificial intelligence, obtaining patient-centered medical information through large language models (LLMs) is crucial for patient education. However, existing digital resources in online health care have heterogeneous quality, and the reliability and readability of content generated by various AI models need to be evaluated to meet the needs of patients with different levels of cultural literacy.
Objective: This study aims to compare the accuracy and readability of different LLMs in providing medical information related to gynecomastia, and explore the most promising science education tools in practical clinical applications.
Clin Pharmacol Ther
September 2025
Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark.
This study aimed to assess the ability of two off-the-shelf large language models, ChatGPT and Gemini, to support the design of pharmacoepidemiological studies. We assessed 48 study protocols of pharmacoepidemiological studies published between 2018 and 2024, covering various study types, including disease epidemiology, drug utilization, safety, and effectiveness. The coherence (i.
View Article and Find Full Text PDFJ Palliat Med
September 2025
Skaggs School of Pharmacy & Pharmaceutical Sciences, UC San Diego Health Sciences, San Diego, California, USA.
Artificial intelligence (AI), particularly large language models (LLMs), offers the potential to augment clinical decision-making, including in palliative care pharmacy, where personalized treatment and assessments are important. Despite the growing interest in AI, its role in clinical reasoning within specialized fields such as palliative care remains uncertain. This study examines the performance of four commercial-grade LLMs on a Script Concordance Test (SCT) designed for pharmacy students in a pain and palliative care elective, comparing AI outputs with human learners' performance at baseline.
View Article and Find Full Text PDFJ Speech Lang Hear Res
September 2025
Department of Speech, Language, and Hearing Sciences, Boston University, MA.
Purpose: Prior studies of vocal auditory-motor control in people with hyperfunctional voice disorders (HVDs) have found evidence of unusually large responses to auditory feedback perturbations of fundamental frequency (0) and more variable voice onset times in unperturbed speech. However, it is unknown whether people with HVDs perform similarly to people with typical voices when asked to make small changes in vocal parameters in volitional tasks. The purpose of this study was to compare performance on minimal movement tasks for 0 and intensity in people with and without HVDs.
View Article and Find Full Text PDF