Loading clinical trials...
Loading clinical trials...
Showing 1-20 of 44 trials
NCT07251907
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.
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.
NCT07566728
Dementia is a neurocognitive disorder that causes a deterioration in cognitive function, significantly impacting social and work abilities and daily activities. Alzheimer's disease is diagnosed when cognitive decline affects at least two cognitive domains, one of which must involve memory. Mild Cognitive Impairment (MCI) is a critical diagnosis as it represents a potentially early stage of cognitive decline. In the DSM-5, MCI is defined as a "minor neurocognitive disorder," characterized by functional decline affecting at least one of six cognitive domains: memory and learning, language, visuospatial function, attention, executive function, and social functioning. It is important to emphasize that this decline is not severe enough to significantly impair the patient's daily activities. In this context, support for people with MCI and dementia is crucial, not only at the family and social level, but also through the adoption of innovative technological solutions. Artificial intelligence (AI) is emerging as a valuable tool for early diagnosis, and through machine learning processes, it is possible to predict cognitive decline, thus providing personalized treatment and day-to-day patient management. This allows for intervention at a less advanced stage of the disease, thus slowing its progression, while maintaining autonomy and independence for as long as possible, which tends to decline over time in this patient population. Investing in innovative technologies is therefore essential not only to improve prevention and treatment opportunities but also to provide concrete support to caregivers, especially at a time when the aging population requires an increasingly structured and effective global response. The objectives of the study are as follows: * The objective of this study is to evaluate the effectiveness of software in administering cognitive and motor tests via a humanoid robot in patients with early-stage Alzheimer's disease (AD) or other forms of mild to moderate dementia. * Support medical professionals in personalizing therapeutic treatments, using predictive models based on advanced artificial intelligence systems. These models will begin by collecting, monitoring, and processing demographic and clinical data and the results of cognitive and motor assessments obtained from patients to predict the course of the disease and the effectiveness of rehabilitation treatments. This will then allow them to suggest personalized treatment options and optimize care pathways, thus improving overall clinical outcomes.
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.
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.
NCT07532343
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?
NCT07505719
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
NCT07075679
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.
NCT07485465
A domain-specific, custom-trained large language model for the differential diagnosis and treatment planning of lymphedema, lipedema, and venous insufficiency.
NCT07452354
Diabetic foot ulcer (DFU) is a major adverse outcome of diabetes, which itself is one of the most significant chronic diseases. The recurrence of DFU involves multiple risk factors, including altered foot loading patterns, patient compliance, family care capacity, blood glucose monitoring, degree of ischemia, and systemic disease control. Early identification of recurrence signs and timely follow-up interventions are crucial for improving prognosis, reducing disability rates, and lowering healthcare costs. However, traditional follow-up systems lack individualized strategies-such as risk stratification, inflexible follow-up intervals, and insufficient compliance management-often resulting in suboptimal outcomes. High-risk patients prone to recurrence may not be followed up frequently enough for early detection, while low-risk patients may undergo unnecessary visits, increasing burdens on both patients and healthcare providers. This inefficiency contributes significantly to the persistently high rates of disability and mortality among recurrent DFU patients. Establishing an individualized follow-up strategy for DFU, supported by advanced technology to address core bottlenecks such as delayed recurrence warnings and inadequate home-based management, represents an effective technical pathway to tackle these issues. Our center proposes to develop a dedicated DFU cohort with comprehensive active follow-up and a multimodal database encompassing well-defined indicators. We aim to explore a high-risk foot grading system for preventing DFU recurrence and design targeted follow-up protocols. By leveraging AI technology, we intend to build a wound warning system capable of identifying DFU recurrence. Furthermore, we seek to establish a telemedicine and AI-assisted, patient-centered home-based self-management framework for early warning and prevention of DFU recurrence.
NCT07441759
Cardiovascular diseases are the leading cause of mortality from treatable conditions in the European Union and the second from preventable causes, with a standardized mortality rate of 257.8 deaths per 100,000 inhabitants. In 2022, more than 1.11 million deaths in individuals under 75 years could have been avoided. Atrial fibrillation (AF) and major adverse cardiovascular events (MACE) are highly prevalent in the elderly and generate substantial healthcare costs. AF significantly increases the risk of MACE and is projected to rise markedly in the coming decades. In Europe, AF prevalence is expected to increase 2.5-fold over the next 50 years, with a lifetime risk of 1 in 3-5 individuals after age 55. AF-related strokes are projected to increase by 34%, and ischemic strokes in individuals over 80 are expected to triple between 2016 and 2060. Additionally, a 27% increase is anticipated among stroke survivors who subsequently develop AF or related conditions. AF substantially impacts morbidity, mortality, and disease progression, and early detection and treatment are crucial to prevent severe outcomes. European action plans (2018-2030) and the 2024 ESC/ESO guidelines emphasize early detection and management of AF in primary care. Although several AF prediction models exist, their integration into clinical practice remains challenging. AF represents a clinical continuum, with thrombotic risk present even before arrhythmia onset. High-risk patients for AF also show a high incidence of MACE, defined as a composite of myocardial infarction, stroke, systemic embolic events, and cardiovascular death. The proposed strategy involves developing and clinically validating an Artificial Intelligence (AI) model to improve early thrombotic risk prediction in patients at high risk of AF, using MACE as the primary outcome. This model aims to outperform the traditional CHA₂DS₂-VASc score by incorporating both classical and emerging clinical factors. The estimated timeline from clinical validation to commercialization is approximately 48 months. AI-based prediction is expected to enable personalized treatment, reduce the incidence of MACE, hospitalizations, and disability, and improve cost-effectiveness, ultimately decreasing the social and economic burden of AF and stroke in Europe.
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.
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.
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.
NCT07314853
The goal of this clinical trial is to learn if using artificial intelligence to guide intravenous fluid therapy during major cancer surgery can help keep blood pressure more stable compared with usual care in adult patients undergoing major cancer surgery. The main questions it aims to answer are: * Does artificial intelligence-guided fluid therapy reduce hypotensive events during surgery? * Does this approach improve recovery and reduce complications after major cancer surgery? Researchers will compare artificial intelligence-guided fluid therapy with standard fluid management to see if the artificial intelligence-guided approach provides better support during surgery. Participants will: * Undergo major cancer surgery under general anesthesia * Receive either artificial intelligence-guided fluid management or standard fluid management during surgery * Be monitored during and after surgery as part of routine clinical care * Be followed after surgery to assess recovery and possible complications
NCT07112599
The pathological-omics and imaging-omics in this study are combined to construct an artificial intelligence (AI) model that can predict whether high-risk prostate cancer patients may have lymph node metastasis. The model determines whether the patient has lymph node metastasis based on the MRI results and the pathological section image information of the case combined with clinical data before radical resection of the prostate. This study is a multicenter, prospective clinical study to verify the model's ability to predict whether high-risk prostate cancer patients may have lymph node metastasis.