moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
PROTEUS: A PROspective Randomised Controlled Trial Evaluating the Use of Artificial Intelligence in Stress Echocardiology
Trial ID: NCT05028179
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-11-08
Completion date: 2023-12-31
Sponsor: Ultromics Ltd
Collaborators: University of Oxford; Academic Health Science Centres; National Institute for Health Research, United Kingdom
Conditions: Coronary Heart Disease
Interventions: EchoGo
Locations: Great Western Hospitals NHS Foundation Trust; Mid and South Essex NHS Foundation Trust; Royal United Hospitals Bath; University Hospitals Birmingham NHS Foundation Trust; Blackpool Victoria Hospital; University Hospitals Coventry & Warwickshire; Northumbria Healthcare NHS Foundation Trust; Buckinghamshire Healthcare NHS Trust; Ipswich General Hospital; Leicester General Hospital; St George's University Hospitals NHS Foundation Trust; Milton Keynes University Hospital; Northampton General Hospital NHS Trust; Royal Oldham Hospital; Oxford University Hospitals NHS Foundation Trust; North West Anglia NHS Foundation Trust; Royal Berkshire Hospital; Salford Royal Hospital; Stockport NHS Foundation Trust; Wrightington, Wigan and Leigh Teaching Hospitals NHS Foundation Trust
Countries: United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom
Enrollment: 2338
Age range: 18 Years - None
Sex: ALL

Summary:
PROTEUS is a multicentre, two arm, randomised controlled trial of a medical device to assess the impact of the introduction of EchoGo into the standard care pathway for stress echocardiology.

Eligibility criteria:
Inclusion Criteria:

* Willing and able to provide informed consent
* Male or female, ≥18 years of age at study entry
* Referred to an NHS Trust for stress echocardiography for investigation of ischaemic heart disease

Exclusion Criteria:

* More than moderate valvular heart disease
* Left ventricular outflow tract obstruction defined as a gradient \> 30mmHg (fixed or dynamic; supravalvular, valvular or sub-valvular)
* Significant co-morbidities (e.g. cancer) with an expected life-expectancy of under 12 months in the investigator's opinion
* Previous coronary artery bypass graft or other cardiac surgery
* Congenital or inherited myocardial disease

Outcomes:
Investigate if EchoGo plus standard care is non-inferior to standard care alone for aiding referral to coronary angiogram following stress echocardiogram (SE)
Application of the Belle.AI Dermatological Image Reference System on Patient Diagnosis Within an Active Clinical Setting
Trial ID: NCT06724627
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-09-16
Completion date: 2025-06-30
Sponsor: BelleTorus Corporation
Collaborators: Urban Health Plan Inc.
Conditions: Skin Condition; Dermatology; Dermatology/Skin - Other
Interventions: Dermatology/Skin - Other
Locations: Urban Health Plan Inc.
Countries: United States
Enrollment: 263
Age range: None - None
Sex: ALL

Summary:
The goal of this research study is to test a new, investigational tool that uses artificial intelligence (AI) to help primary care providers assess skin conditions. This tool is an AI-powered dermatology image reference app that works with a smartphone. For clarity, the AI makes no diagnoses; it provides reference images. Primary care providers then use their own medical judgement and training to make the diagnosis.

The sponsor aims to compare the diagnoses made by primary care providers (such as doctors, nurse practitioners, and physician assistants) with the support of the AI tool compared to a panel of dermatologists, who are setting the gold standard. By doing so, the sponsor can determine the value of the AI tool for primary care providers and understand how it might be used alongside traditional clinical care.

This AI capability complies with FDA regulatory guidelines and is not considered a medical device, similar to a Google image search, which returns similar looking images for reference purposes. For intervention, they healthcare providers use their own training and clinical judgement to make the diagnosis, and not the AI.

Eligibility criteria:
Inclusion Criteria:

* Patient must present to walk-in clinic with a primary dermatological complaint.
* Patient must have the ability and willingness to provide informed consent and comply with study procedures and visits.
* Participants must have access to the required technology (e.g., smartphone with internet access) and be capable of using it for the required image capture.

Exclusion Criteria:

* Patients who are unable to comply with study procedures due to physical or mental health limitations (as assessed by study coordinator).
* Pediatric, adolescent, and teen patients who present with dermatological conditions on their genitalia will not be included in the study (in support of patient privacy concerns).

Outcomes:
Accuracy of Belle AI Image References; Impact on Economic Burden
Automated Detection and Classification of Patient-Ventilator Dyssynchrony With a Machine Learning Algorithm
Trial ID: NCT06506123
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-05-25
Completion date: 2025-12-24
Sponsor: University of Sao Paulo General Hospital
Collaborators: Magnamed Tecnologia Medica S/A
Conditions: Respiratory Failure
Interventions: Artificial Intelligence Detection and Classification of Patient-Ventilator Dyssynchronies
Locations: Heart Institute, University of São Paulo
Countries: Brazil
Enrollment: 80
Age range: 18 Years - None
Sex: ALL

Summary:
This is a diagnostic study aiming to compare accuracy to detect and classify patient-ventilator dyssynchronies by a machine learning algorithm, compared to the gold-standard defined as dyssynchronies diagnosed and classified by mechanical ventilator and esophageal pressure waveforms analyzed by experts.

The main question of this study is:

• Are patient-ventilator dyssynchronies accurately detected and classified by an artificial intelligence algorithm when compared to experts analyzing esophageal pressure and mechanical ventilator waveforms?

Eligibility criteria:
Inclusion Criteria:

* Subjects under assisted or assist-controlled mechanical ventilation and monitored with esophageal pressure balloon.

Exclusion Criteria:

* Refusal from patient's family or attending physician

Outcomes:
Diagnostic Accuracy of the Artificial Intelligence algorithm
Assessment and Comparison of Diagnostic Technologies for the Detection of Dental Caries in Clinical Practice
Trial ID: NCT07560514
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-11-30
Completion date: 2026-08
Sponsor: University Hospital, Martin
Conditions: Dental Caries
Interventions: Diagnostic methods for dental caries
Locations: Jessenius Faculty of Medicine
Countries: Slovakia
Enrollment: 100
Age range: 18 Years - 65 Years
Sex: ALL

Summary:
This study aims to evaluate and compare different digital diagnostic technologies used for the detection of dental caries in clinical practice. The study focuses on assessing the diagnostic accuracy of various methods, including visual examination and digital diagnostic tools, in identifying dental caries in patients. The results will contribute to improving early detection and diagnostic strategies in dentistry.

Eligibility criteria:
Inclusion Criteria:

* Age 18-65 years
* Presence of permanent dentition only
* Presence of primary or secondary carious lesions
* Presence of all teeth in the lateral segment (teeth 4-7) in both maxilla and mandible
* Signed informed consent including agreement with photodocumentation, bitewing radiographs, and use of AI-based diagnostic methods (including DIAGNOcam)

Exclusion Criteria:

* Age under 18 or over 65 years
* Extensive crown destruction affecting more than two-thirds of the tooth
* Carious lesions reaching the dental pulp or threatening pulp vitality
* Inability or refusal to undergo radiographic examination (bitewing radiographs)
* Teeth with fixed prosthetic restorations in the examined area Refusal to participate or to use AI-based diagnostic methods (including DIAGNOcam)

Outcomes:
Diagnostic accuracy of technologies for detection of dental caries
AI-Powered OSCE Coaching in Improving Infertility Counseling Skills: A Randomized Controlled Study in Nursing Students
Trial ID: NCT07404020
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-03-01
Completion date: 2026-07-30
Sponsor: Sakarya University
Conditions: Infertility Counseling; Communication Skills
Interventions: AI-Powered OSCE Coaching
Locations: Bartın University
Countries: Turkey (Türkiye)
Enrollment: 96
Age range: 18 Years - None
Sex: ALL

Summary:
This randomized controlled experimental study aims to evaluate the effectiveness of a generative artificial intelligence-based "OSCE Coach" in improving infertility counseling communication and empathy skills among nursing students. The study will be conducted with undergraduate nursing students at Bartın University Faculty of Health Sciences and follows a two-group pretest-posttest design.

A total of 96 students will be randomly assigned to either the intervention group or the control group using computer-generated randomization. The intervention group will participate in an AI-powered OSCE coaching simulation consisting of 6-8 infertility counseling micro-scenarios, incorporating structured feedback and deliberate practice cycles. The control group will receive conventional infertility counseling education using standard teaching methods.

Outcomes will be assessed using the Therapeutic Communication Skills Scale for Nursing Students, the Jefferson Scale of Empathy for Nursing Students, and a rubric-based infertility counseling communication performance assessment based on the Kalamazoo Consensus Statement. Performance evaluations will be conducted via standardized virtual patient interviews and scored by two blinded independent raters.

Data will be analyzed using ANCOVA to assess intervention effects while controlling for baseline scores. This study aims to contribute evidence on the effectiveness of AI-supported OSCE coaching in enhancing communication and empathy skills in nursing education.

Eligibility criteria:
Inclusion Criteria:

* Undergraduate nursing students enrolled at Bartın University Faculty of Health Sciences
* Successfully completed the Women's Health and Diseases Nursing course (minimum passing grade)
* Currently enrolled in the infertility nursing course
* Able to read and understand Turkish
* Willing to participate and provide written informed consent

Exclusion Criteria:

* Students who do not complete the educational intervention
* Students who do not complete pretest or posttest assessments
* Students with prior formal training in AI-supported OSCE or similar simulation-based coaching programs

Outcomes:
Infertility Counseling Communication Performance
A Retrospective Observational Study to Use Artificial Intelligence for Prediction of Disease REcurrence of COlorectal Cancer Liver METastasis After Hepatic Resection
Trial ID: NCT07385521
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-02-19
Completion date: 2027-02-19
Sponsor: Francesco De Cobelli
Conditions: Colorectal Liver Metastasis (CRLM); Liver Resection; Hepatectomy; Liver Ablation
Interventions: AI-analysis
Locations: Radiology Department
Countries: Italy
Enrollment: 1000
Age range: 18 Years - None
Sex: ALL

Summary:
Colorectal cancer is the third most common cancer worldwide and the fourth most common cause of cancer-related death. Survival is primarily determined by stage of disease and the presence of metastases. The combination of chemotherapy and liver resection remains the treatment option with the highest survival benefit for patients with liver metastases from colorectal cancer, with surgery still being the only recognized potential curative treatment; surgical locoregional treatment can also be combined with thermal ablation to enhance the possibility of complete liver clearance. Despite significant improvements in prognosis, a large proportion of patients (almost half) will still experience recurrence following treatment. There is a clinical need to identify a priori patients who are different likely to develop disease recurrence after locoregional treatment (liver resection ± thermal ablation) and to respond differently to chemotherapy, in order to refine risk-based allocation of treatments and resources. Widespread digitalization of healthcare generates a large amount of data, and together with today accessible high-performance computing, artificial intelligence technologies can be applied to overcome the current limitations in estimating colorectal cancer liver metastases recurrence and response to locoregional and chemotherapy treatments, thus achieving better treatment allocation than current practice. All radiomic features can also help in training the neural network aimed at detecting liver metastases before they become visually detectable by the radiologist. Therefore, this study aims to evaluate whether a multifactorial machine learning model (including clinical and radiomic) can identify patients with colorectal cancer liver metastases with a high risk of progression after chemotherapy and recurrence after liver resection

Eligibility criteria:
Inclusion Criteria:

* Pathologically confirmed diagnosis (at final pathology) of liver metastases from colon or rectal adenocarcinoma
* \> 6 months of follow-up
* no other concomitant neoplastic disease

Exclusion Criteria:

* All subjects receiving hepatic resection but not fulfilling the inclusion criteria

Outcomes:
Development of an ML algorithm predicting which individuals diagnosed with CRLM are most likely to experience early recurrence of disease after liver resection.
Quality Improvement Intervention in Colonoscopy Using Artificial Intelligence
Trial ID: NCT03622281
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2018-10-20
Completion date: 2019-05-31
Sponsor: Shandong University
Conditions: Quality Control; Artificial Intelligence; Colonoscopy
Interventions: quality improvement intervention using artificial intelligence
Locations: Department of Gastroenterology, Qilu Hospital, Shandong University
Countries: China
Enrollment: 676
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
Quality measures in colonoscopy are important guides for improving the quality of patient care. But quality improvement intervention is not taking place, primarily because of the inconvenience and expense. To address the difficulties above, we used artificial intelligence for quality control of colonoscopy.

Eligibility criteria:
Inclusion Criteria:

* aged between 18 and 80;
* agree to give written informed consent.

Exclusion Criteria:

* patients with the contraindications to colonoscopy examination;
* patients with a history of inflammatory bowel disease (IBD), CRC, colorectal surgery;
* patients with prior failed colonoscopy and high suspicion of polyposis syndromes, IBD and typical advanced CRC;
* patients refused to participate in the trial;
* the colonoscopyprocedure cannot be completed due to stenosis, obstruction, huge occupying lesions, or solid stool;
* the colonoscopy procedure have to be terminated due to complications of anaesthesia.

Outcomes:
Adenoma detection rate
Using Artificial Intelligence-platform on Plaque Control and Periodontal Treatment Outcome in Patients With Periodontal Disease-Preliminary Results
Trial ID: NCT04567030
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2019-11-16
Completion date: 2025-12-31
Sponsor: Kaohsiung Medical University Chung-Ho Memorial Hospital
Conditions: Periodontal Diseases
Interventions: Dental monitoring (DM)
Locations: Kaohsiung Medical University Chung-Ho Memorial Hospital
Countries: Taiwan
Enrollment: 100
Age range: 35 Years - 65 Years
Sex: ALL

Summary:
The aim of this study was to evaluate the long-term effectiveness of an AI dental monitoring intervention on patients' periodontal disease, preventive behaviors and motivation. This randomized controlled trial included experimental group: AI group (EG-A), AI-humanized group (EG-B) and control group(CG), respectively.

The EG-A and EG-B received AI dental monitoring intervention by taking mouth image at home once a week for six month.

Baseline and follow-up surveys were used to collect the data in periodontal index, periodontal preventive behavior and motivation.

Eligibility criteria:
Inclusion Criteria:

* Age between 35-65 years old
* Diagnosed periodontal disease by dentist (Comprehensive Periodontal Treatment Project)
* More than 16 functional teeth

Exclusion Criteria:

* Have received periodontal treatment within 3 months
* Patient has disease related to periodontal disease (ex. Drug-related excessive gum growth or blood clotting problems, pregnancy, mental illness)
* Disability patients

Outcomes:
gingival index (GI); gingival index (GI); gingival index (GI); plaque index (PI); plaque index (PI); plaque index (PI); Probing Pocket Depth (PPD); Probing Pocket Depth (PPD); Probing Pocket Depth (PPD); Clinical Attachment Loss(CAL); Clinical Attachment Loss(CAL); Clinical Attachment Loss(CAL); Protection Motivation Theory (PMT) scores; Protection Motivation Theory (PMT) scores; Protection Motivation Theory (PMT) scores
Effect of Artificial Intelligence-assisted Personalized Microbiome Modulation by Diet in Functional Constipation: a Randomized Controlled Trial
Trial ID: NCT05429333
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2020-12-14
Completion date: 2021-12-27
Sponsor: Istanbul Medipol University Hospital
Conditions: Constipation - Functional
Interventions: Artificial intelligence-assisted personalized diet
Locations: Medipol University Esenler Hospital
Countries: Turkey (Türkiye)
Enrollment: 50
Age range: 20 Years - 65 Years
Sex: ALL

Summary:
Current medications and behavioral modifications have limited success in the treatment of functional constipation (FC). An individualized diet based on microbiome analysis may improve symptoms in FC. In the present study, we aimed to investigate the impacts of microbiome modulation on chronic constipation. patients fulfilling Rome IV criteria for functional constipation were randomized into two groups. The Control group received sodium picosulfate plus conventional treatments (i.e., laxatives, enemas, increased fiber, and fluid intake). The study group underwent microbiome analysis and received an individualized diet with the assistance of a soft computing system (Enbiosis Biotechnology®, Sariyer, Istanbul). Differences in Patient Assessment Constipation-Quality of Life (PAC-QoL) score and complete bowel movements per week (CBMpW) were compared between groups after 6 week-intervention.

Eligibility criteria:
Inclusion Criteria:

* Patients fulfilling Rome IV criteria for functional constipation (FC)
* Patients who had a colonoscopy performed within the last 5 years
* Patients who had a colonic transit time assessment within the last 3 months
* Patients who had a magnetic resonance defecography within the last 3 months

Exclusion Criteria:

* Use of antibiotics, probiotics, and/or prebiotics within the last 4 weeks
* Gastrointestinal endoscopy within the last 4 weeks
* Major gastrointestinal surgery (total/segmental gastrectomy, small bowel and/or colonic resection)
* Cholecystectomy
* Inflammatory bowel diseases
* Celiac disease
* Any etiology of chronic constipation other than FC (irritable bowel syndrome, rectocele, dyssynergic defecation, opioid use)
* Endocrine, metabolic or neurologic disorders causing constipation (hypothyroidism, Parkinson's disease, paraplegia)

Outcomes:
Three or more complete bowel movements per week at 10 weeks; Improvement in Patient Assessment Constipation Quality of Life
AI-Supported Teaching in Pediatric Surgical Emergency Case Management: Effects on Nursing Students' Knowledge and Clinical Decision-Making in a Randomized Controlled Study
Trial ID: NCT06999447
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-03-26
Completion date: 2025-03-26
Sponsor: Yeditepe University
Conditions: Nursing Education Research
Interventions: ChatGPT-Supported Case-Based AI Education (C-CASE); Standard Education
Locations: Yeditepe University
Countries: Turkey (Türkiye)
Enrollment: 66
Age range: 18 Years - 25 Years
Sex: ALL

Summary:
This clinical trial aims to explore whether an AI-supported teaching method can help nursing students improve their clinical decision-making skills and knowledge during case-based learning. The study focuses on third-year nursing students enrolled in an emergency care course. Participants are divided into two groups: one group receives traditional case-based instruction, while the other uses ChatGPT (an AI language model developed by OpenAI- (Chat Generative Pre-trained Transformer)) to support their case-solving activities. All students complete a pretest and posttest to assess their knowledge and perceptions of clinical decision-making. The main goals are to find out whether the AI-supported group performs better than the traditional group and to evaluate the relationship between students' knowledge and their clinical decision-making scores. By comparing these two teaching methods, researchers aim to understand whether integrating AI tools into nursing education can enhance learning outcomes.

Eligibility criteria:
Inclusion Criteria:

* Successful completion of prerequisite courses (Fundamentals of Nursing I-II,
* Medical-Surgical Diseases Nursing, and Pediatric Nursing) along with associated clinical internships
* Enrollment in the Emergency Care course during the study period
* Volunteering to participate and providing written informed consent
* Must be a third-year undergraduate nursing student.
* Must be enrolled in the "Emergency Care Nursing" course during the 2024-2025 spring semester.
* Must be attending the Faculty of Health Sciences, Department of Nursing, at Yeditepe University.
* Completion of all data collection forms

Exclusion Criteria:

* Failure to complete prerequisite courses or required clinical internships
* Irregular attendance in the Emergency Care course
* Declining to participate or failure to provide written informed consent
* Submission of incomplete data collection forms

Outcomes:
Clinical decision-making skills