Artificial Intelligence Ai

Chest X-rays are commonly used to help diagnose and manage chest conditions. Artificial intelligence (AI) tools are increasingly being used to support chest X-ray interpretation. However, it is not yet clear whether the timing of AI information affects how clinicians review images, make decisions, and use AI support. This study will look at whether showing AI information before or after a clinician first reviews a chest X-ray changes how they look at the image, how long they take, their interpretation decisions, their confidence, and their trust in AI support. Healthcare professional participants will complete two chest X-ray interpretation sessions in a controlled NHS research setting. During each session, participants will review de-identified chest X-ray images while wearing eye-tracking equipment. Eye-tracking will record where a participant looks on the image and how long they spend looking at different areas. In one session, AI information will be shown before the participant reviews the chest X-ray. In the other session, AI information will be shown after the participant has first reviewed the chest X-ray. The order of these two sessions will be balanced across participants. The study uses de-identified chest X-ray images from existing examinations. It does not involve patients directly, does not change clinical care, and no clinical decisions will be made from the study readings. Participants will also complete a short questionnaire about their experience of using AI support. A separate anonymous survey will collect wider views from clinicians, patients, members of the public, and healthcare staff about the use of AI in chest X-ray interpretation.

This prospective observational diagnostic accuracy study evaluates whether large language models (LLMs) - GPT-4o (OpenAI, gpt-4o-2024-11-20) and Claude (Anthropic, claude-sonnet-4-6) - can accurately calculate HEART scores from unstructured Turkish clinical notes and predict 30-day major adverse cardiac events (MACE) in emergency department patients presenting with non-traumatic chest pain. The study will enroll 600 consecutive adult patients. For each patient, the same anonymized data (free-text anamnesis, ECG report text, troponin value, and age) will be independently processed by both LLMs via separate API calls with deterministic settings (temperature=0, JSON format). A three-expert consensus HEART score - derived through blinded independent scoring by three emergency medicine physicians with majority-vote adjudication - serves as the reference standard for agreement analysis. Actual 30-day MACE (all-cause death, AMI Type 1/2/4b, unplanned revascularization) determined via national health database and telephone follow-up serves as the outcome for diagnostic accuracy analysis. A secondary documentation-quality sub-study will quantify how spontaneously Turkish emergency anamnesis notes capture HEART score parameters.

MOSAIC aims to determine whether oro-dental morphological anomalies, particularly palatal morphology, associated with rare bone and cartilage diseases can be precisely characterized using 3D digital models analysed through geometric morphometrics. The study will also evaluate whether these morphological signatures can train an artificial intelligence (AI) algorithm to classify syndromes. A prospective monocentric case-control cohort will be constituted, including 3D intra-oral scans and associated clinical data. The final goal is to improve diagnostic accuracy and reduce diagnostic delay in rare bone disorders.

This study aims to evaluate the accuracy and reliability of artificial intelligence (AI)-based cephalometric analysis compared with digital manual tracing. A total of 100 standardized lateral cephalometric radiographs will be analyzed using Delta-Dent software with manual landmark identification and three fully automated AI-based systems (WebCeph, QuantX, and Smartee). Sagittal, vertical, dental, and soft tissue cephalometric parameters will be compared among the different methods. Statistical analysis will assess inter-method agreement and the clinical relevance of any observed discrepancies. The study seeks to determine whether AI-based systems provide measurements comparable to conventional digital tracing and whether they can be considered reliable adjunctive tools in orthodontic diagnosis and treatment planning.

This study is conducted under the ethics-approved project titled "Artificial Intelligence Solution for Simplifying the Diagnostic Workflow of Breast MRI''.The goal of this observational study is to develop an integrated breast MRI system that uses diffusion-weighted imaging (DWI) to create synthetic contrast-enhanced images. This system aims to diagnose and screen for breast cancer without the need for contrast agents, while using a generated risk score to perform imaging-based triage and risk stratification. Participants will include people aged 18 and older who require a breast MRI either for evaluation of a suspicious finding or for high-risk screening. This study seeks to answer two main questions: * Can synthetic contrast-enhanced images generated from DWI match real contrast-enhanced images in their ability to distinguish benign from malignant breast lesions? * Can the risk score derived from DWI-based synthetic images enable imaging-level risk stratification, allowing people at lower risk to avoid contrast agent injection? Researchers will compare the quality of synthetic images against real contrast-enhanced images and will recruit radiologists to assess how well these images perform for diagnostic and screening tasks. MRI data from participants undergoing breast MRI will be used to train, validate, and test this integrated system.

This mixed-methods study aims to assess current perspectives, attitudes, and preparedness of perioperative nurses regarding the integration of artificial intelligence (AI) in clinical practice. The study targets nurses working in surgical wards and operating rooms to explore AI utilization, perceived usability, professional impact, and readiness for future implementation. Quantitative and qualitative data will be collected concurrently and integrated to generate comprehensive insights into AI adoption and future directions in perioperative nursing.

The goal of this observational study is to develop and pre-validate a machine learning algorithm to predict early recovery of mobility in patients undergoing hip or knee joint replacement surgery. The primary research question is: Can a machine learning model accurately classify patients with faster versus slower recovery of autonomous mobility in the first days after joint replacement surgery? Patients who have undergone elective hip or knee arthroplasty and received post-operative physiotherapy will have their clinical and perioperative data collected retrospectively (2020-2023) and prospectively (March 2026-December 2027). The algorithm will be trained on retrospective data and tested prospectively to evaluate its predictive performance for early mobilization and length of hospital stay.

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

The aim of this study is to determine the effect of AI-supported oncology case analysis on nursing students' knowledge, level of learning satisfaction, and clinical decision-making skills. This study is planned to be conducted using a single-blind randomized controlled trial design for the quantitative research component and an interview design for the qualitative research component. The students will be divided into two groups: an intervention group (artificial intelligence) and a control group (traditional instruction).

The aim of this study is to determine the effect of AI-supported internal medicine nursing case analysis on students' case management performance, learning outcomes, learning experience, clinical self-efficacy, and cognitive load levels. This study will be conducted using a single-blind randomized controlled trial design for the quantitative research and an individual interview design for the qualitative research. Students will be randomly assigned to either the intervention (artificial intelligence) or control (case analysis) group.

Gynecological cancers, particularly cervical, ovarian, and endometrial cancers, pose a global problem. Cervical cancers are quite common worldwide, and this rate is even higher in developing countries. Cervical cancers are easily treatable when detected early, and screening is quite easy. Diagnosis is routinely made through human papillomavirus (HPV) testing and cytological screening. Eliminating anxiety, fear, and uncertainty about gynecological examinations makes the examination process easier, thus enabling early diagnosis and treatment of diseases. Keeping up with developing and changing technology and using it to improve women's health is an undeniable change in recent times. This study aims to determine the effect of an AI-assisted informational training program on women's anxiety and satisfaction levels regarding gynecological examinations.

The purpose of this study is to develop an artificial intelligence-based chatbot application to support exercise behavior in individuals aged 60 and over who do not regularly exercise, and to evaluate its effectiveness. In addition, the study aims to examine the effects of changes in exercise habits on the cognitive (mental) functions of older adults. In this study, the impact of a chatbot-supported personalized exercise program on cognitive functions in older individuals will be evaluated. A total of 90 participants is planned for inclusion in this study. If you agree to participate in this study, depending on the group you are assigned to, you may receive: * An artificial intelligence-based chatbot program, along with educational materials about the importance of exercise, or * Only educational materials (brochures) prepared by the researchers about the importance of exercise. At the beginning of the study, you will be asked to complete a data collection form. The same form will also be administered at week 12 and week 24. This form will include: * Basic information such as your age and gender, * Questions about your exercise habits, * A brief test to assess your cognitive (mental) functions, * Questions evaluating your level of physical activity. The study duration is 24 weeks, including 12 weeks of intervention and 12 weeks of follow-up.

Inflammatory bowel disease (IBD), including Crohn's disease (CD) and ulcerative colitis (UC), is a chronic immune-mediated disorder requiring long-term management. Clinically, IBD may involve recurrent intestinal inflammation, ulcer formation, and complications such as strictures and fistulas. The etiology of IBD is associated with immune dysregulation, gut microbiome imbalance, and genetic susceptibility. Its clinical manifestations are heterogeneous; early symptoms such as abdominal pain, diarrhea, weight loss, hematochezia, or anemia often resemble gastroenteritis, irritable bowel syndrome, or infectious enterocolitis, leading to misdiagnosis and delayed diagnosis. According to international studies, the interval between initial symptom onset and confirmed diagnosis can range from several months to years, during which untreated disease progression increases the risks of hospitalization, surgery, bowel strictures, and fistulizing complications, resulting in significant impacts on patient quality of life. This study adopts a retrospective design, analyzing our hospital's electronic medical record data from 2023 to 2025.The objective is to evaluate the performance and feasibility of an artificial intelligence (AI) model-developed and incorporating natural language processing (NLP) and phenotypic recognition algorithms-in supporting early identification and diagnosis of IBD. The model has been validated in multiple European healthcare systems and is capable of recognizing high-risk phenotypic clusters from large-scale structured and unstructured medical data. This study represents the first application of this AI technology in the Taiwanese IBD population. All data processing will occur within a de-identified and secure computing environment to ensure data privacy and information security. The study will compare AI-generated diagnostic suggestions derived from medical records with actual clinical diagnoses to assess consistency and accuracy. The model's performance across different clinical characteristics, disease severity levels, and stages of illness will also be examined. In addition, statistical metrics such as precision and recall will be used to generate PRC curves for determining the optimal diagnostic threshold. The outcomes of this study are expected to validate the potential of AI technology in facilitating early recognition, accelerating diagnosis, and supporting clinical decision-making for IBD. The findings will provide essential data for developing localized AI models for IBD, ultimately enhancing diagnostic efficiency, shortening the diagnostic timeline, and improving long-term patient outcomes and quality of life. Objective 1：To retrospectively analyze the clinical characteristics and diagnostic pathways of patients with IBD (CD/UC). Objective 2：To evaluate the performance of the AI model in identifying and providing diagnostic suggestions for high-risk IBD cases. Objective 3：To compare the accuracy and consistency between AI-generated diagnostic suggestions and actual clinical diagnoses.

Prolonged Grief Disorder (PGD) is a severe, disabling condition characterized by intense yearning and difficulty accepting the reality of loss, which significantly impairs the academic and psychosocial functioning of bereaved adolescents. While Grief-Focused Cognitive Behavioral Therapy (GF-CBT) is effective, its high cost and resource-intensive nature limit its accessibility for adolescents in mainland China. Grief-Focused Guided Narrative Writing (GF-GNW) offers a scalable, low-cost alternative that facilitates memory integration. Furthermore, integrating Artificial Intelligence (AI) to provide personalized, structured feedback has the potential to simulate therapist functions and enhance intervention efficacy. However, the specific efficacy of AI-assisted feedback in this context remains empirically unvalidated. This parallel randomized controlled trial aims to examine the effectiveness of AI-assisted GF-GNW (AI-GF-GNW) in treating Chinese adolescents (aged 10-19) with subclinical PGD, compared to a no-feedback NF-GF-GNW group and a free writing group. Primary outcomes include PGD symptom severity, while secondary outcomes assess depression, anxiety, and daily functioning. We hypothesize that both active intervention arms will significantly alleviate PGD and related symptoms compared to the free writing group, and that the AI-GF-GNW group will demonstrate a significantly greater reduction in symptoms and functional impairment than the NF-GF-GNW group.

This study aims to evaluate the diagnostic performance and clinical utility of a multimodal medical imaging large model in identifying common systemic diseases. Through a retrospective reader study involving multiple centers, the research will compare the diagnostic accuracy, sensitivity, and specificity of radiologists with and without AI assistance. The goal is to validate the model's robustness and its impact on the diagnostic efficiency of clinicians across diverse healthcare settings.

Patient understanding of anaesthesia risks remains inconsistent due to time constraints, language barriers, and variable clinician communication styles. Traditional verbal consent may not consistently ensure comprehension or reduce preoperative anxiety. PEAR (Patient Education of Anesthesia Risks) is a multilingual, AI-driven chatbot developed to enhance patient education and improve the quality of anaesthesia risk counselling. Study Objective: To compare PEAR's performance in delivering anaesthesia risk consent against the standard face-to-face verbal method.

Behavioral health problems, such as depression and anxiety, are common yet often are not identified by emergency department doctors and nurses. These mental health conditions can be due to medical issues or can worsen medical problems. One way investigators hope to do a better job of learning about mental health is by training Artificial Intelligence (AI) software to detect anxiety and depression by analyzing facial expression and tone of voice. Participants are invited to participate in a study which may help improve emergency department care. An audio and video recording of the participant's responses to some simple, non-psychological questions will be analyzed by a computer to determine whether investigators can assess mood and anxiety by analyzing speech and visual patterns. The audio and video will not be listened to nor watched by study personnel, only analyzed by a computer. The investigator's hope is that it will help others in the future by aiding in the assessment of psychological state. This study is being conducted at CMC ED only.

Colorectal polyp size is related to the risk of exhibiting advanced histological features. Moreover, polyps larger than 10 mm are associated with an elevated risk of metachronous advanced neoplasia and colorectal cancer (CRC). Consequently, accurate measurement of polyp size, especially at the 10 mm threshold is critical for risk stratification and surveillance intervals. Furthermore, polyp size is also important for the choice of the appropriate resection procedures. Underestimation may lead to delayed diagnosis, thereby increasing the risk of colorectal cancer, while overestimation may result in unnecessary surveillance endoscopies.

This quasi-experimental pretest-posttest study aimed to evaluate the effect of artificial intelligence-assisted climate change education on pregnant women's climate change concerns and awareness. The study will be conducted with pregnant women attending a pregnancy school, and participants will be assigned to intervention and control groups. The intervention group will receive AI-supported climate change education in addition to routine training, while the control group will receive only routine education. Data will be collected using the Climate Change Anxiety Scale and the Maternal-Fetal Health Awareness of Climate Change Scale. The findings are expected to contribute to improving pregnant women's awareness and reducing concerns related to climate change through innovative educational approaches.

The goal of this study is to examine the facilitators and barriers to the comprehensive implementation of AI technology in nursing documentation. The main questions it aims to answer are: What are facilitators to the comprehensive implementation of AI technology in nursing documentation? What are barriers to the comprehensive implementation of AI technology in nursing documentation? What strategies can help to fully utilize artificial intelligence technology in nursing documentation?

This trial will prospectively evaluate the impact of integrating AI-ECG within the pay-for-performance program on improving the diagnosis, treatment, and clinical outcomes of occlusion myocardial infarction patients by promoting accurate and timely diagnoses through financial incentives.

Poor health literacy and patient comprehension have been associated with adverse health outcomes. Patient educational materials (PEMs) are articles that are intended to assist patients in their understanding of a given medical condition. Given that the average American adult reads at the 8th grade level, the American Medical Association and the Center for Disease Control recommend PEM be written at the 6th grade level. However, literature has found the majority of PEMs to be written significantly higher than the 8th grade level. In order to improve their readability, a number of studies have displayed the effectiveness of large language models (LLMs) such as ChatGPT to simplify the text of a given PEM. Despite the improvement in readability, the effectiveness of these simplified PEMs on improving patient comprehension of the AI augmented material has yet to be investigated. The purpose of our study is to test whether the improvement in readability found in AI-simplified PEMs corresponds to a greater understanding of the material compared to the original PEM. Understanding if AI-simplified PEM truly improves comprehension could further support this use case for AI and aid providers and healthcare organizations in improving the health literacy of their patients. This study aims to answer the following question: Do AI simplified PEMs improve the comprehension of pediatric orthopaedic conditions? Researchers will compare AI-simplified PEMs to their original, unmodified counterparts in order to see if there is any difference in post reading comprehension of the participants. Participation in the study will include: * A brief baseline survey (e.g. demographics and educational attainment) * A randomly assigned reading of either the original PEM or the AI simplified version. * A 10 question post-reading multiple choice quiz

A randomized prospective study comparing the evaluation of mammography images in a breast cancer screening programme by a single radiologist with AI support versus standard double reading by two radiologists without AI support.

This study is a prospective,multi-center and observational clinical study.Investigators would like to innovatively construct a "trinity" database of colorectal tubular adenomas based on white light - magnifying chromo - pathological images.It simulates the decision - making logic of doctors, and based on the multimodal endoscopic LAFEQ method previously proposed, develop a multimodal deep - learning diagnostic model for colon adenomas and an interpretable risk prediction model for intestinal adenomas. While achieving high - precision auxiliary treatment decisions, clearly present the decision - making basis, and break through the limitation of poor interpretability of previous medical imaging AI models.

A domain-specific, custom-trained large language model for the differential diagnosis and treatment planning of lymphedema, lipedema, and venous insufficiency.