Clinical Research Directory

Narrow Band Imaging Versus Artificial Intelligence for Colonic Surveillance in Inflammatory Bowel Disease

Patients with inflammatory bowel disease (IBD) - ulcerative colitis or Crohn's disease - have a higher risk of developing colorectal cancer than the general population. For this reason, regular colonoscopies are recommended to look for early warning signs, such as abnormal areas of tissue called dysplasia, which can develop into cancer over time. Finding these abnormal areas during colonoscopy can be difficult because IBD causes ongoing inflammation that can make the bowel mucosa look irregular, hiding subtle changes. Doctors currently use a technique called narrow-band imaging (NBI), a special light setting on the colonoscope that enhances the visibility of the bowel, to help spot these areas more easily. A newer tool, artificial intelligence (AI)-assisted detection, has shown promise in helping doctors find more polyps during routine colonoscopies in the general population. However, this AI tool was developed and tested mostly in people without IBD, so it is not yet known whether it works as well in people with IBD, whose bowel can look very different due to chronic inflammation. This study will directly compare the AI tool (CADe; ENDO-AID, Olympus) with narrow-band imaging to see which one is better at finding abnormal areas during colonoscopy in patients with long-standing ulcerative colitis or Crohn's disease who are having their routine cancer surveillance exam. Each participant will have one colonoscopy in which the bowel is examined twice in a row, once with each technique, by two different doctors, in random order. This lets researchers compare both methods directly within the same patient, which is the fairest comparison. The study aims to enroll 60 patients at Vall d'Hebron University Hospital in Barcelona, Spain. Researchers hope the results will help determine whether AI tools - which are widely available and easier to use than NBI - can be a reliable alternative for IBD surveillance, potentially making this important cancer-screening exam more accessible in the future.

AI Virtual Patient Training for Dental Student History Taking

This randomized study evaluated whether an artificial intelligence-assisted virtual patient could support third-year dental students in practicing medical and dental history taking. Fifty-six students were assigned to either a Gemini-based virtual-patient training group or a conventional role-play group. Both groups completed four training sessions over 2 weeks using comparable clinical cases, practice time, and feedback criteria. The study compared changes in case-based history-taking competency as well as student satisfaction and perceived usefulness of the training methods.

AI Timing in Chest X-ray Interpretation Using Eye-Tracking

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.

The Effect of Artificial-Intelligence-Assisted Videos on Breastfeeding Self-Efficacy, Motivation, and LATCH Scores in First-Time Mothers

As of 2024, nearly half (48%) of infants under six months worldwide are exclusively breastfed, approaching the global target of 50%. Building on this progress, the World Health Organization has extended the target to 60% by 2030, emphasizing the need for innovative, scalable, and supportive interventions to strengthen breastfeeding practices. Breastfeeding has well-established benefits for infant growth, immunity, and long-term health, while also reducing maternal postpartum complications and chronic disease risks. Early postpartum support, particularly within the first hours after birth, is critical for successful and sustained breastfeeding. However, in busy clinical settings, providing continuous and individualized support can be challenging, especially for primiparous women who may experience low confidence, pain, and insufficient guidance. This randomized controlled trial aims to evaluate the effect of an artificial intelligence (AI)-supported relaxing breastfeeding video on breastfeeding self-efficacy, breastfeeding motivation, and LATCH scores among primiparous women. Unlike instructional videos, the AI-based video is designed to promote emotional relaxation, instinctive breastfeeding perception, and maternal confidence during the early postpartum period. The study adopts a two-arm randomized controlled experimental design. The population consists of primiparous women who deliver vaginally at Ağrı Training and Research Hospital postpartum unit between February and June 2026. A priori power analysis (α=0.05, power=0.95) indicated a minimum sample size of 38 participants; considering a 20% attrition rate, a total of 46 women (23 per group) will be recruited. Eligible participants include primiparous, Turkish-speaking women without postpartum or neonatal complications. Women who undergo cesarean delivery, have medical or psychiatric conditions preventing breastfeeding, or whose newborns require intensive care will be excluded. Participants will be randomized into intervention and control groups using an online randomization tool. All participants will receive a standardized 5-minute breastfeeding education based on the Turkish Ministry of Health breastfeeding counseling guidelines. In addition to standard care, the intervention group will watch a 10-minute AI-supported relaxing video at the 2nd and 6th postpartum hours during breastfeeding. The video will be displayed via tablet while the mother is in a comfortable breastfeeding position. The control group will receive standard care only. The AI-generated video will be produced using Kling AI, a generative video platform that enables controlled text-to-video workflows. To ensure ethical and cultural sensitivity, the video will not include real human or animal breastfeeding images. Instead, it will feature abstract, metaphorical visuals (e.g., pastel silhouettes, minimalist line art, or flat illustrations) that convey calmness, bonding, rhythm, and instinctive closeness. The final version will be selected following expert review and pilot testing with three postpartum women. Low-level white noise (\<60 dB) will accompany the video to enhance maternal relaxation and infant comfort. Data collection tools include a demographic information form, the Breastfeeding Self-Efficacy Scale-Short Form, the Primipara Breastfeeding Motivation Scale, and the LATCH Breastfeeding Assessment Tool. Breastfeeding observations and LATCH scoring will be conducted by an independent midwife blinded to group allocation. Statistical analyses will include descriptive statistics, paired and between-group comparisons, and repeated-measures analyses where appropriate. Ethical approval will be obtained from the relevant institutional ethics committee, and written informed consent will be secured from all participants. The findings are expected to contribute novel evidence on the role of AI-supported emotional and relaxing digital interventions in enhancing early postpartum breastfeeding outcomes and maternal confidence.

AI Role-Play Coaching for Breastfeeding Counseling

Breastfeeding counseling is critically important for both maintaining the mother's motivation to breastfeed and ensuring the baby's healthy nutrition. Artificial intelligence (AI)-based role-playing coaching is an innovative teaching approach that allows students to experience the counseling process through digital scenarios, identify and correct their mistakes in a risk-free environment, and receive instant feedback based on their performance. In this context, this study aims to examine the effect of AI-assisted role-playing coaching on breastfeeding counseling skills in nursing students using a randomized controlled experimental design. The study was conducted in the Nursing Laboratory of a university's Nursing Department between March 2026 and July 2026. Participants were divided into three groups: Peer role-playing group, Classical theoretical training (control) group, and AI-assisted role-playing group. Data were collected using a personal information form, the Self-Efficacy Scale for Supporting Breastfeeding Mothers, and the Breastfeeding Counseling Role-Playing Assessment Rubric. The findings show that the peer role-playing group has a higher ability to establish open, supportive, and empathetic communication with the mother, while the AI-assisted role-playing group has a higher ability to collect, evaluate, and provide feedback on data. Furthermore, breastfeeding self-efficacy was found to be higher in the AI-assisted role-playing group. The data obtained have the potential to generate evidence for the use of next-generation learning technologies in nursing education and contribute to training graduate nurses ready for clinical practice. It will also provide a scientific basis for developing training strategies aimed at improving the quality of breastfeeding support services.

Comparison of Digital Analysis and Artificial Intelligence for Cephalometric Tracing

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.

Morphology in Oral Rare Syndromes & Artificial Intelligence for Clinical Diagnosis

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.

Diagnostic Accuracy of GPT-4o and Claude for HEART Score Calculation in Chest Pain

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.

Non-Contrast Breast MRI Diagnosis and Risk Stratification Using DWI-Generated Synthetic Contrast Enhancement

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.

Artificial Intelligence in Perioperative Nursing

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.

Structured Handoff Using Intelligent Framework for Transitions Trial

Inpatient general medicine attendings will be randomized to have an LLM feature turned on to provide a draft of an off-service handoff within Carelign (an EHR-adjacent provider communication tool). Providers who have access to this feature will be clearly instructed that if they use the LLM-generated draft, they must review and edit it as necessary before finalizing. The study will assess measures of documentation burden (as it relates to writing handoff) - including time spent writing handoff - and work exhaustion in both intervention and control groups.

Development and Pre-validation of a Machine Learning-based Prediction Algorithm for Early Functional Recovery in Patients Undergoing Hip and Knee Replacement Surgery

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.

Effect of AI-Supported Case Analysis on Nursing Students

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).

Ambient AI Clinical Trial

This is a single-site pragmatic randomized control trial studying the effect of ambient artificial intelligence (AI) scribes on the delivery of medical care to patients in the ambulatory setting. The study will last 150 days and include up to 65 providers in the intervention group. Providers will be recruited from three medical specialties, including primary care, oncology, and urology. The study will enroll providers and randomize them to an intervention group (access to the ambient AI scribe product) or a control group (routine patient care). Providers will be evaluated for burnout and task load measures through digital surveys at the beginning, middle, and end of the study. Provider electronic health record (EHR) usage data will also be evaluated for time spent documenting, time spent after hours on days with scheduled clinical care, and time between the start of the clinical encounter and signing it.

Development of a Chatbot-supported Personalized Exercise Program for Older Adults and Evaluation of Its Effects on Cognitive Functions

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.

AI-GF-GNW on Prolonged Grief Reactions

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.

Effectiveness of Large Language Model for Anaesthesia and Procedural Consent

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.

Identification of Depressive and Anxiety Symptoms Among a Sample of Emergency Department Patients Using Artificial Intelligence (AI) Technology

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.

A Randomized Controlled Trial of Ambient Artificial Intelligence Scribe Technologies

This is a three-arm pragmatic RCT of 238 outpatient physicians at a large academic health system, randomized 1:1:1 to one of two AI scribe tools or a usual-care control group. The two-month study will observe and compare the effects of each tool prior to system-wide roll out of selected tool (anticipated Spring 2025). We will use covariate-constrained randomization to balance the arms in terms of physician baseline time in notes, survey-measured level of burnout, and clinic days per week. The primary purpose of the initiative is to improve quality, efficiency, and business operations at University of California, Los Angeles (UCLA) Health, and this initiative is not being done for research purposes. The results of this operational initiative will inform the widespread roll out of AI scribe tools across all providers within the UCLA Health System. Nevertheless, the UCLA study team plans to rigorously examine and publish the impact of this intervention across the health system, which is why the study team pre-registered the initiative.

A Prospective Study to Evaluate the Performance of a Real-time System in the Estimation of Colorectal Polyp Size

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.

Artificial Intelligence Education and Climate Awareness in Pregnancy

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.

AMIE's Clinical Conversational Abilities in an Urgent Care Setting

The purpose of this study is to determine the feasibility of a conversational artificial intelligence (AI) system to have a meaningful clinical conversation with a patient prior to an urgent care visit with their primary care physician. In this study, patients who are seeking an urgent care visit (that is, any type of medical visit with their primary care provider for a new complaint) will first have a conversation with an AI system. This interaction with the AI system will happen less than a week before their visit with their physician, and will be supervised by an independent physician who will interrupt in case there are any concerns about patient safety. After the interaction, a summary of the conversation will be sent to the patient's PCP, who will review prior to the in-person visit. The researchers will investigate: * Patient views on the AI system * PCP views on the AI system * Overall safety, as measured by the physician safety supervisor * Quality of clinical conversations, measured by standardized rubrics * Quality of diagnostic and management plans generated by the AI; these will not be shared with the patient or physician, but will be generated after the fact and compared with the actual diagnosis and management plan.

Barriers and Facilitators to Nursing Record With AI Technology Application

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?

Assessing Intensive Care Unit (ICU) Indications: Human vs. ChatGPT-4o Predictions

This retrospective study evaluates the accuracy of ICU admission indications by comparing clinical decisions with predictions from ChatGPT-4. Patient data, including demographics, vital signs, laboratory results, imaging findings, and clinical decisions, will be retrospectively collected and documented systematically using Case Report Forms. The model will be trained using ICU admission guidelines and tasked to predict ICU needs based on collected patient data. This study aims to systematically assess the alignment between AI-based predictions and clinical decisions for ICU admissions.