Loading clinical trials...
Loading clinical trials...
Showing 1-20 of 145 trials
NCT07637461
This research plan aims to establish an effectiveness assessment system for promoting clinical communication, empathy, and emotion regulation in pediatric nursing students through a combination of AI communication simulation and human library narrative. This study sets five specific objectives: 1. To develop and implement a pediatric nursing teaching model that integrates AI communication simulation and human library narrative. 2. To evaluate the effectiveness of this teaching model in improving nursing students' clinical communication skills. 3. To examine the effectiveness of this teaching intervention in enhancing nursing students' empathy. 4. To explore the impact of this teaching intervention on nursing students' emotion regulation abilities. 5. To understand students' learning experiences, feelings, changes in emotion regulation, and suggestions for improvement regarding this integrated teaching intervention.
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.
NCT07558746
This study aims to enroll intern doctors and have them sit one of three identical radiology exams. The only difference between them is an AI-assistant. The differences between these groups will be used to measure the extent of AI reliance among intern doctors in Palestine.
NCT07556900
EARLIEST-AI Study type: Two-phase, multi-reader, blinded retrospective observational study based on data from a single clinic. The primary objective of the study is to assess the sensitivity and specificity of radiologists and Artificial Intelligence (AI) in interpreting mammographic examinations for breast cancer detection in a scenario where no previous examinations are available, and to compare the diagnostic performance of radiologists with and without AI support. The secondary objectives of the study are to assess the independent diagnostic performance of Computer Aided Detection (CAD) software, including sensitivity and specificity in identifying histopathologically confirmed breast cancer cases; to assess inter-reader and intra-reader variability in interpretation with and without AI support; to assess the agreement between AI outputs and histopathological findings; and to assess the impact of mammogram technical parameters on AI performance. Time frame: Review of a subset of mammograms randomly selected from those performed between January 1, 2012 and December 31, 2024. Imaging findings were classified according to the Breast Imaging Reporting and Data System (BI-RADS). Inclusion criteria: 1. Female patients aged 30 years or older. 2. One or more mammograms performed between January 1, 2012 and December 31, 2024. 3. Meets one of the following criteria: 3.1 Histopathologically confirmed diagnosis of breast cancer within 6 months of mammogram, 3.2 or two consecutive mammograms with BI-RADS 1 or BI-RADS 2. Exclusion criteria: 1. Mammograms of poor quality or artifacts that do not allow for reliable assessment. 2. History of breast surgery or previous breast cancer treatment that has significantly altered breast morphology. 3. Data deficiencies, including: 3.1. lack of previous histopathological data 3.2. or lack of available follow-up data.
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.
NCT07515118
To evaluate, in a randomized controlled trial, whether AI-guided monitoring and ovulation triggering leads to clinical outcomes comparable to those achieved through physician-led decision-making in patients undergoing ovarian stimulation for IVF.
NCT06902675
This study will evaluate the performance of a large language model (LLM)-based clinical decision support system in the emergency department at Rambam Health Care Campus. The system analyzes structured patient data from the electronic health record and generates diagnostic and treatment recommendations for physicians. The study will assess the system's ability to support diagnostic reasoning, its impact on diagnostic accuracy when used by physicians, and its perceived clinical usefulness. In addition, a retrospective analysis of de-identified patient records will be conducted to compare LLM-generated recommendations with actual clinical outcomes, including diagnosis, disposition decisions, and length of stay. The study will also examine the performance of the system in a multilingual clinical environment where both Hebrew and English are used in medical documentation and communication.
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?
NCT07522658
This prospective observational study aims to evaluate the effectiveness and educational value of artificial intelligence (AI)-generated multiple true/false questions compared to those developed by experienced academicians in anesthesiology training. A total of 27 anesthesiology residents will be included in the study. Question sets consisting of 200 multiple true/false items will be created, with half generated by academicians and the other half generated using an artificial intelligence model (ChatGPT-based system). The questions will be based on standardized educational materials from the anesthesiology training curriculum. Participants will complete the test in a single session. Each correct answer will be scored as one point, and total scores will be calculated. In addition to test performance, item difficulty, discrimination indices, and test reliability will be analyzed. Furthermore, participants' perceptions regarding question quality will be evaluated. The study aims to determine whether AI-generated questions can provide a reliable and effective alternative to traditional question development methods in medical education and contribute to more objective and standardized assessment processes.
NCT07087418
The goal of this observational, retrospective and prospective study is to develop a noninvasive disease assessment system by leveraging artificial intelligence (AI) to comprehensively analyze multi-modal imaging features, including magnetic resonance enterography (MRE) and computed tomography enterography (CTE), for the diagnosis and prognostication of digestive diseases. To this end, the investigators retrospectively enrolled imaging, endoscopic, and clinical data from 21 centers across China to construct and iteratively optimize the AI model. The model's performance will be prospectively validated in two centers, and its accuracy in lesion localization will be verified through real-world deployment in endoscopy suites.
NCT07518797
Beacon is a digital platform that processes objective and subjective aggregated data provided by patients. Objective data is provided by standard wearables, while subjective data is provided by patient-reported outcome measures (PROMs), comprising written and vocal patient reporting. The ASPIRE.AI study is a prospective study evaluating the feasibility of clinicians' use of aggregated data that was provided by patients and analyzed through "Beacon", and its influence on advanced cancer patients' palliative symptoms management. Approximately 40 consecutive eligible ambulatory advanced cancer patients first attending the palliative unit in the Davidoff Center will be enrolled. The trial will continue for \~1 year, with each patient participating in this trial for a total of about 12 weeks. All participants will receive the intervention. The intervention comprises the palliative standard of care treatment along with the usage of the Beacon digital platform, which enables comprehensive data collection and aggregation regarding the patient's biopsychosocial status, and thus, the patient's symptom burden. Data collected and aggregated through Beacon includes Beacon data provided by the patients via wearables (smartwatch/sensors), smartphones, and written and recorded PROMs. Researchers will then evaluate physician engagement with the platform, Influence on treatment, and the physician user experience rating as well as patients' adherence, satisfaction with Beacon usage, and changes in patients' symptom burden and quality of life.
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.
NCT07479654
The goal of this three-year mixed-methods observational study with an embedded randomized controlled trial is to develop and validate a frailty risk prediction model and evaluate an artificial intelligence-based voice emotion detection-guided counselling intervention in adults with congenital heart disease (ACHD). The main questions it aims to answer are: Are symptom clusters associated with frailty and psychological outcomes in adults with congenital heart disease? Can symptom clusters and psychosocial factors be used to predict frailty risk over time in ACHD patients? Does an AI-based voice emotion detection-guided counselling intervention improve psychological outcomes, fatigue, and quality of life among high-risk ACHD patients? Researchers will compare ACHD patients receiving AI-based voice emotion detection-guided counselling with those receiving usual care to determine whether the intervention reduces depression, anxiety, sleep disturbance, fatigue, and frailty risk, and improves grit and quality of life. Participants will: Complete longitudinal assessments of symptom clusters, frailty, and psychological status at baseline and follow-up time points Participate in qualitative interviews to explore lived experiences related to symptoms and frailty Receive AI-based voice emotion detection-guided counselling (intervention group only in Year 3)
NCT07471984
This clinic trial aims to investigate whether artificial intelligence (AI) diagnostic tools at neurological diseases diagnosis on brain CT/MRI can improve the work efficiency of specialized neuroimaging physicians, with a specific focus on its clinical value in distinguishing normal from abnormal findings, critical value identification, and neurological disease classification. Using pathological and/or discharge diagnoses of neurological diseases as the gold standard, an AI model will be trained on over 10,000 CT/MRI cases to achieve diagnostic performance comparable to that of neurological radiologists before being transformed and putted to use. Furthermore, clinical trials will be conducted in sub-studies (abnormal cases identification, critical value assessment, and neurological disease classification) to validate the clinical utility of AI and human-AI collaboration in the precise diagnosis of neurological disorders. The expected outcomes include reducing missed and misdiagnosis rates, enabling rapid screening of critical conditions, and achieving precise imaging-based diagnosis by using AI tools.
NCT07464171
What is the study about? This study is testing "Dora", an AI-powered assistant that can make phone calls to patients, for use in the Fracture Liaison Service (FLS). The FLS is a clinic that helps prevent more bone fractures after an initial "fragility fracture" (a break that happens easily, usually due to osteoporosis). Why is this being done? FLS clinicians often have to spend a lot of time on routine phone calls for assessments and follow-ups. If Dora can safely and accurately collect patient information, it might save time for staff and still give patients a good experience. What will happen to patients in the study? Invitation and consent - Patients with a new fragility fracture who are eligible will be invited to take part after informed consent. Dora call - Patients will receive an automated phone call from Dora, at the start of their FLS pathway and at follow-up. At intake, Dora will ask about risk factors for bone problems (e.g., smoking, alcohol use, family fracture history). At follow-up, Dora will ask about medication use, side effects, falls, or new fractures. Clinician call - Soon after, patients will have their usual phone appointment with an FLS clinician, who asks similar questions. Surveys/interviews - Patients will be asked to complete a short questionnaire and take part in an optional interview to say how they felt about talking to Dora. What about clinicians? Clinicians involved in the FLS pathway will be asked to complete a short survey and to take part in an optional interview to understand how useful Dora's reports might be in their work. Who can take part? Patients - Age 50+, English-speaking, with a new fragility fracture, and able to use the phone. Clinicians - Those working in FLS or similar bone health services. How long will it take? Each patient might be involved for up to about 7 months. The whole study will take about a year.