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Showing 1-20 of 24 trials
NCT07485465
A domain-specific, custom-trained large language model for the differential diagnosis and treatment planning of lymphedema, lipedema, and venous insufficiency.
NCT07079592
This study aims to validate the use of an artificial intelligence-enabled electrocardiogram (AI-ECG) to screen for elevated PAP. We hypothesize that the AI-ECG model can early identify patients with pulmonary hypertension in high-risk patients, prompting further evaluation through echocardiography, potentially resulting in improving cardiovascular outcomes.
NCT07236840
The goal of this observational study is to evaluate the feasibility and accuracy of a self-administered remote neurological examination using the "Iskhaa" mobile application in patients with brain tumors aged above 5 years who are able to follow app-based instructions. The main questions it aims to answer are: 1. Development of a mobile application equipped with symptom assessment and recording videos as patients perform specific neurological tasks. 2. Development and validation of the AI model to detect functional changes and predict subsequent neurological deterioration. Participants will: 1. Use the Iskhaa mobile application to perform guided self-neurological examinations following pre-recorded video instructions. 2. Complete EORTC QLQ-C30 and BN20 questionnaires for quality of life assessment. 3. Record and upload videos (e.g., speech, walking, limb movements) using their mobile camera for analysis. 4. In Phase 1 (onsite), 100 participants will use the app under supervision to ensure usability and accuracy. 5. In Phase 2 (offsite), 500 participants will use the app independently at home for monthly self-assessments, with reminders and follow-up support. 6. Continue routine clinic visits every 3-6 months and imaging every 6-12 months as per standard clinical care. The study will compare app-recorded data with physician assessments to determine agreement and validity of remote neurological monitoring using artificial intelligence analysis.
NCT07333560
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.
NCT07406919
The goal of this clinical trial is to learn whether access to an artificial intelligence (AI) clinical decision support assistant can improve diagnostic accuracy during real-world telemedicine consultations among primary care physicians in El Salvador. The main questions it aims to answer are: * Does access to the AI assistant increase the proportion of correct diagnoses compared to telemedicine without AI assistance? * Does the effect of the AI assistant differ according to the physician's prior experience using AI in telemedicine? Researchers will compare physicians with the AI assistant enabled to physicians with the AI assistant temporarily disabled to see if access to AI improves diagnostic accuracy. Participants (physicians) will: * Provide telemedicine consultations as part of their routine clinical duties. * Be randomly assigned to either have the AI assistant enabled or disabled during the study period. * Continue documenting clinical encounters in the electronic platform as usual. * Have their anonymized consultation notes reviewed by an independent expert panel to determine diagnostic accuracy.
NCT07408492
The goal of this clinical trial is to find out whether an artificial intelligence (AI)-powered research training course can improve nursing students' research skills, attitudes toward artificial intelligence, and readiness to use AI in research and education. The main questions this study aims to answer are: Does AI-powered research training improve nursing students' understanding of research methods? Does this training improve nursing students' attitudes toward artificial intelligence? Does the course increase nursing students' readiness and confidence to use artificial intelligence in research-related activities? Researchers will compare nursing students who take an AI-powered research training course with students who receive usual education without AI-based training. Participants will: Be randomly assigned to either the AI-powered research training group or the usual education group Complete online questionnaires about research skills, attitudes toward artificial intelligence, and readiness to use AI Attend assessments at three time points: before the course, immediately after the course, and three months later The AI-powered research training course includes structured sessions on research methods and the responsible use of artificial intelligence tools for literature review, research design, data analysis support, and academic writing. The results of this study may help improve research education and support the safe and effective use of artificial intelligence in nursing education and research.
NCT07387055
The goal of this observational study was to evaluate an artificial intelligence-assisted projective method for assessing dental anxiety in young children and to understand how the first child-dentist interaction affected dental anxiety. The main questions it aimed to answer were: Did the artificial intelligence-assisted projective method provide a valid and reliable assessment of dental anxiety in children aged 3 to 6 years? Did dental anxiety change after the child's first interaction with the dentist during the first dental visit? Children aged 3 to 6 years who attended their first dental visit as part of routine dental care took part. During the same visit, dental anxiety was assessed before and after the initial child-dentist interaction using picture-based dental anxiety scales and the newly developed projective method. All assessments were completed on the same day.
NCT07369947
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.
NCT07370285
The nurse-patient communication environment in pediatric care is characterized by high uncertainty and complexity. Due to children's limited language development and emotional regulation abilities, coupled with parents' high level of involvement, nursing students often experience anxiety, lack of confidence, and avoidance behaviors, which negatively affect their clinical learning outcomes and the establishment of therapeutic relationships. Therefore, providing effective communication support strategies is essential in pediatric nursing education. This study aims to implement an instructional scaffolding model using artificial intelligence (AI)-generated empathy maps to enhance the communication skills, empathy performance, and grit of nursing students during pediatric clinical practicums when encountering communication challenges. A mixed-methods research design was adopted, and the participants were third-year nursing students enrolled in a pediatric nursing practicum course. The teaching intervention included AI-assisted generation of age-appropriate communication strategies, the construction of a grit-oriented empathy map, small group scenario-based exercises, and the application of learned strategies in clinical settings. Quantitative data were collected using pre- and post-intervention assessments, including an empathy scale, a communication skills scale, and a grit scale, to evaluate changes in learning outcomes. Qualitative data, including reflective journals, clinical observations, and focus group interviews, were analyzed to explore students' learning processes and strategy adaptations. Triangulation was applied to strengthen the validity of the findings. It is anticipated that this teaching model will enhance students' understanding of pediatric patients' emotional needs, strengthen their communication strategy application and clinical interaction quality, and promote persistence and adaptability in challenging situations. Through evidence-based teaching practice, this study is expected to provide a feasible and scalable innovative instructional model that supports the effective integration of AI into clinical nursing education, thereby improving pediatric nursing competence and the quality of care for children.
NCT06957093
Purpose: To evaluate the efficacy of artificial intelligence (AI)-based decision-making technology in managing glycated hemoglobin (HbA1c) and blood glucose levels compared to the control group. Methods: For the AI Intervention group, the patients will be trained to independently use the diabetes telemedicine platform application. Each patient will be equipped with a glucometer and exercise bracelet, and the data will be automatically transmitted to the medical server via Bluetooth. The healthcare platform will analyze the uploaded data and provide feedback suggestions on medication, diet, and exercise automatically. The platform will also monitor the medical and lifestyle data of the patients every two weeks, offer feedback based on the analyses, and remind the patient to adhere to the self-management protocol based on the platform. The platform is a digitally integrated healthcare platform that patients can use independently without the need for monitoring and assistance by healthcare professionals. The glucometer and pedometer bracelet will automatically connect to the platform through Bluetooth. The patient lab sheet identification and structured conversion system, AI for food picture identification and calorie calculation systems, and the AI decision-making system are on the cloud server. Patients upload image information, such as lab sheets and meal pictures, through the patient's diabetes mobile health system, and the cloud platform intelligently analyzes the patient's disease, medication, and daily life status to develop personalized solutions according to individual control goals. Free outpatient visits will be provided to both the intervention and control groups every twelve weeks. For the conventional treatment group, patients will receive a free blood glucometer and will have regular outpatient appointments. There is no limit to the number of outpatient visits; however, they are required to regularly monitor and record their blood glucose, diet, and exercise data to ensure that the medical team objectively conduct their diagnosis and treatment activities. The medical team will provide free outpatient visits every 12 weeks, along with advice on medication, diet, and exercise based on the individual's blood glucose level. Expected results: A significant difference in HbA1c change from baseline to 48 weeks and improved FPG and 2-hour postprandial blood glucose levels in the AI intervention group were observed.
NCT06934239
The goal of this clinical trial is to compare patient-centered outcomes when screening digital breast tomosynthesis (DBT) exams are interpreted with versus without a leading FDA-cleared artificial intelligence (AI) decision-support tool in real-world U.S. settings and to assess patients' and radiologists' perspectives on AI in medicine. The main question it aims to answer is: Does an FDA-cleared AI decision-support tool for digital tomosynthesis (DBT) improve screening outcomes in real world US clinical settings? This trial will include all interpreting radiologists and all adult patients undergoing screening mammography at any of the participating breast imaging facilities across 6 regional health systems (University of California, Los Angeles (UCLA), University of California, San Diego (UCSD), University of Washington-Seattle, University of Wisconsin-Madison, Boston Medical Center, and University of Miami) during the trial period. All screening mammograms at these facilities will be randomized to either intervention (radiologist assisted by an AI decision support tool) versus usual care (radiologist alone) to see if interpreting these mammograms with the AI tool's assistance improves patient screening outcomes. We are targeting 400,000 screening exams across the participating health systems in this trial.
NCT07237919
WHY ARE WE DOING THIS? When patients contact their GP practice, the first step is to work out what kind of help they need and how quickly it's needed. This is called 'triage' and is important for patient safety. Artificial Intelligence (AI) can help make triage faster. While AI is already being used in the NHS, we don't know how accurate it is or if it treats all patients fairly. WHAT WILL WE DO? We will collect anonymised data from patients that use an AI triage system called Patchs in GP practices in England. The project will last four years. We will analyse the data in four steps: 1. Look at data from GP practices using Patchs without AI triage to see how they currently triage patients and what problems they face. 2. Use data from GP practices using Patchs (both with AI on and off) to make the AI triage more accurate. 3. Check data from GP practices using Patchs with AI triage off to measure how well the updated AI system works. 4. Give the improved AI triage system to GP practices already using AI. At each step, we will check whether patients from different backgrounds are treated fairly. HOW WILL WE ANALYSE THE DATA? We will use statistical methods to compare the triage decisions made by the AI with those made by clinical staff. This analysis will also be used to check that the AI works fairly for patients from different backgrounds. WHAT DIFFERENCE WILL WE MAKE? Our research will show the problems with triage and explain how an improved AI system could help patients get the care they need more quickly.
NCT07138105
This is a national survey of doctors in Sudan who are involved in providing surgical care. The aim is to understand their awareness, experiences, and opinions about using artificial intelligence (AI) in surgery. The survey includes all cadres-house officers, medical officers, registrars, residents, specialists, consultants, and general practitioners who perform surgical procedures-working in public, private, NGO, and military hospitals across Sudan. Participants are asked about their familiarity with AI, barriers to its use (such as poor infrastructure, lack of training, or cost), interest in training, and possible uses in Sudan's health system, especially in conflict-affected areas. The study does not test any AI tools but collects information to help design future AI solutions that work offline, in low-bandwidth conditions, and meet the needs of surgeons and surgical teams in Sudan.
NCT06473558
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.
NCT06766422
Aneurysmal subarachnoid hemorrhage (SAH) is one of the critical diseases that severely threaten human health, with a clinical mortality rate reaching as high as 30%. Early diagnosis and intervention before rupture are considered key to improving the prognosis of aneurysmal SAH. With the widespread clinical application of non-invasive cerebrovascular imaging techniques, such as CTA and MRA, the detection rate of unruptured intracranial aneurysms (UIAs) has significantly increased. However, addressing the growing demand for clinical cerebrovascular imaging diagnostics raises the challenge of improving diagnostic accuracy while alleviating the workload of diagnostic physicians. Furthermore, considering that not all detected UIAs will rupture, it is crucial to accurately identify high-risk aneurysms prone to rupture to avoid unnecessary overtreatment, which could lead to significant socioeconomic burdens and iatrogenic harm to patients.To meet this clinical need, researchers have developed an artificial intelligence (AI) algorithm to create software capable of automatically identifying intracranial aneurysms based on non-invasive vascular imaging data, enabling accurate diagnosis of aneurysms. To evaluate the clinical utility of this AI algorithm, a prospective, multicenter, registry study was proposed. Through long-term standardized and uniform non-invasive imaging follow-up, individualized imaging analysis profiles will be established. By correlating these profiles with aneurysm outcome events (growth or rupture), imaging features capable of accurately predicting aneurysm growth and rupture will be identified and analyzed. This approach is expected to enhance the accuracy of UIA diagnosis and enable risk stratification for unruptured intracranial aneurysms through the utilization of relevant data.
NCT06911398
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.
NCT06857188
The assessment of AI -based prediction models in detecting AKI early in critically ill patients. Specifically, the aim is to evaluate the model's ability to predict the onset of AKI before it clinically manifests allowing for early interventions
NCT06754826
This experiment aims to determine the effect of AI-generated questions and feedback on diagnostic reasoning in preclinical medical students. Main Research Question: What is the effect of AI-generated questions and feedback on diagnostic reasoning skills in preclinical medical students?
NCT06864702
Hepatocellular Carcinoma(HCC) is a common disease in China, ranking as the fourth most prevalent malignant tumor and the third leading cause of cancer-related deaths in the country. Along with other liver, biliary, pancreatic, and splenic diseases, it poses a serious threat to the lives and health of the Chinese population. Precise organ resection techniques, centered around accurate preoperative imaging and functional assessment as well as meticulous surgical operations, have become the mainstream in hepatobiliary surgery in the 21st century. These techniques require precise dissection of intrahepatic blood vessels, the biliary system, and the pancreatic-splenic duct system to achieve an optimal balance between eradicating lesions and preserving the normal function of the organs while minimizing trauma to the body. Precise tissue resection via laparoscopy is a prerequisite for successful hepatobiliary surgery. Addressing how to assist surgeons in performing surgeries more safely and effectively, as well as how to enhance learning outcomes during training, are pressing issues that need to be resolved. Efficient learning and analysis of surgical videos may help improve surgeons' intraoperative performance. In recent years, advancements in artificial intelligence (AI) have led to a surge in the application of computer vision (CV) in medical image analysis, including surgical videos. Laparoscopic surgery generates a large amount of surgical video data, providing a new opportunity for the enhancement of laparoscopic surgical CV technology. AI-based CV technology can utilize these surgical video data to develop real-time automated decision support tools and surgical training systems, offering new directions for addressing the shortcomings of laparoscopic surgery. However, the application of deep learning models in surgical procedures still has some shortcomings. Based on this, the present study aims to conduct a retrospective analysis of cases involving laparoscopic hepatobiliary and pancreatic surgeries performed at Zhujiang Hospital, Southern Medical University, between 2017 and 2024. The goal is to investigate the recognition and validation of deep learning models for classifying surgical phase images in medical imaging, as well as for semantic segmentation of anatomical structures, surgical instruments, and surgical gestures, including abdominal CT and MRI.
NCT06785155
The aim of this project is to evaluate the effectiveness of using chatbots for patient education about dry eye disease. The study will examine how the chatbot affects patients' levels of information, managing symptoms, and overall satisfaction.