moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
To Analyze the Determining Factors in the Evolution of Subjects Diagnosed With Long COVID at Four Years of Follow-up. BioICOPER Follow-up Study.
Trial ID: NCT07295483
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-02-09
Completion date: 2029-06
Sponsor: Instituto de Investigación Biomédica de Salamanca
Collaborators: Carlos III Health Institute
Conditions: Persistent COVID Condition
Interventions: BioICOPER Long COVID Cohort
Locations: Av. de Portugal 83. Planta 2
Countries: Spain
Enrollment: 400
Age range: 18 Years - None
Sex: ALL

Summary:
Long COVID (persistent COVID) represents a major global health challenge due to its high prevalence (approximately 7%), significant impact on quality of life, and socioeconomic burden. Despite extensive research, diagnostic tools to objectively identify or predict long COVID evolution are still lacking.

The BioICOPER Follow-up Study aims to analyze the influence of biomarker evolution on clinical symptomatology (particularly chronic fatigue) and vascular health after four years of follow-up among 400 participants previously included in the original BioICOPER cohort.

Advanced proteomic analysis, vascular function assessment, and machine-learning-based predictive modeling will be used to identify biomarkers associated with disease progression, stratified by sex. This project will contribute to personalized clinical management of long COVID and improved diagnostic and therapeutic strategies in primary care.

Eligibility criteria:
Inclusion Criteria:

* Adults ≥18 years old.
* Confirmed previous SARS-CoV-2 infection.
* Diagnosis of long COVID according to WHO criteria.
* Participation in the baseline BioICOPER study.
* Signed informed consent for re-evaluation.

Exclusion Criteria:

* Acute illness preventing participation.
* Cognitive or physical impairment limiting data collection.
* Withdrawal of informed consent.
* Age \< 18 years old

Outcomes:
Progression of vascular stiffness measured by carotid-femoral pulse wave velocity (cfPWV)
An EHR-Embedded Pragmatic Stepped-Wedge Clinical Trial of Ambient Artificial Intelligence to Reduce Nursing Staff Documentation Time
Trial ID: NCT07456241
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-05-05
Completion date: 2026-12
Sponsor: University of Wisconsin, Madison
Conditions: Nursing Documentation Burden
Interventions: Artificial Intelligence
Locations: UW Health - East Madison Hospital
Countries: United States
Enrollment: 250
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this clinical trial is to learn whether using Ambient Artificial Intelligence for nursing staff documentation in an inpatient setting will reduce the time spent in flowsheet documentation and enhance nurse staffing wellbeing.

Participants will use Ambient Listening AI software to draft documentation in discrete fields.

Eligibility criteria:
Inclusion Criteria:

* Willingness to engage and use ambient technology
* English speaking
* All Registered Nurses and Nursing Assistants with the study inpatient units
* Attest to completing all required training

Exclusion Criteria:

* Planned leave more than 6 weeks during study timeframe

Outcomes:
Change in Active Time Spent in Flowsheets per shift hour
Artificial Intelligence in the Characterization of Small and Diminutive Colorectal Polyps: A Prospective Study in a Clinical Setting Using CAD EYE®
Trial ID: NCT04749277
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2021-01-01
Completion date: 2021-04-30
Sponsor: Unidade Local de Saúde de Coimbra, EPE
Conditions: Colonic Polyps
Interventions: CAD EYE® (Fujifilm, Europe, Gesellschaft mit beschränkter Haftung, Dusseldorf, Germany)
Locations: Centro Hospitalar e Universitário de Coimbra
Countries: Portugal
Enrollment: 197
Age range: 18 Years - None
Sex: ALL

Summary:
Current guidelines recommend resection and histopathological analyses of all colorectal polyps. Real-time optical diagnosis can obviate non-neoplastic polyp resection ("diagnose-and-leave-behind") and histopathological analyses of diminutive polyps ("predict-resect-and-discard") reducing healthcare and cost burden. The investigators aimed to evaluate the diagnostic accuracy of computer-aided diagnosis using CAD EYE® (Fujifilm,Germany) in real-time optical characterization of colorectal polyps compared to endoscopic diagnosis with histopathology as the gold-standard. For this purpose, a single-centre prospective study of diminutive/small colorectal polyps is ongoing.

Eligibility criteria:
Inclusion Criteria:

* Age ≥18 years;
* The presence of at least one polyp in an elective colonoscopy.

Exclusion Criteria:

* Poor bowel preparation (Boston Bowel Preparation Score \<6 at the total score or \<2 at one of colorectal segment);
* No recovery of excised polyps for histopathological analysis;
* The presence of polyps not amenable to endoscopic excision or with contraindication for their excision at the time of colonoscopy;
* The absence of explicit indication for colonoscopy.

Outcomes:
Real-time optical characterization of colorectal polyps (CAD EYE® versus Histopathology); Real-time optical characterization of colorectal polyps (Endoscopist versus Histopathology)
Development and Evaluation of an Artificial Intelligence Tool for Colposcopy Assistance
Trial ID: NCT06208319
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2024-03-01
Completion date: 2024-10-31
Sponsor: Assistance Publique - Hôpitaux de Paris
Collaborators: Sorbonne University
Conditions: Cervical Cancer; HSIL; LSIL; Cervical Ectropion
Interventions: data collection
Enrollment: 1000
Age range: 18 Years - None
Sex: FEMALE

Summary:
Project aiming to develop an algorithm to help the interpretation of colposcopy images, then to evaluate the effectiveness of this algorithm by using it on new cases and comparing the results obtained to the impression of expert clinicians

Eligibility criteria:
Inclusion Criteria:

1. Adult patients who underwent colposcopy with directed biopsies at La Pitié-Salpêtrière between September 1, 2022 and December 31, 2023
2. Known clinical context (why the patient underwent colposcopy)
3. Histological results of cervical biopsies (+/- conization or even hysterectomy) known
4. Images available (photographs of the cervix without preparation, after application of acetic acid and after application of lugol)
5. Informed of the study and not opposing the use of their data

Exclusion Criteria:

* Patients who do not speak French

Outcomes:
Development of an algorithm to help interpret colposcopy images
Implementing an App-based Remote Blood Pressure Monitoring Program to Reduce Health Disparities Among Women With Hypertension During Pregnancy
Trial ID: NCT05595629
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-11-07
Completion date: 2023-08-30
Sponsor: Women and Infants Hospital of Rhode Island
Conditions: Gestational Hypertension; Preeclampsia; Chronic Hypertension Complicating Pregnancy
Interventions: App-based remote blood pressure monitoring; text-message based remote blood pressure monitoring
Locations: Women & Infants Hospital of Rhode Island
Countries: United States
Enrollment: 120
Age range: 18 Years - 55 Years
Sex: FEMALE

Summary:
Remote postpartum blood pressure monitoring program with text messages has been shown to increase adherence to recommended postpartum blood pressure checks among those with hypertension at discharge from birth hospitalization, but these programs require medically trained professionals to respond to each individual text message. A bluetooth-enabled blood pressure cuff that synchs automatically a smartphone application that leverages Artificial Intelligence to provide tailored recommendations based on recorded blood pressure measurements--and can also provide on-demand education on hypertension--may be less costly way to provide similar support.

Eligibility criteria:
Inclusion Criteria:

* a) English- or Spanish-speaking
* b) maternal age \>18 years old
* c) smartphone ownership for remote blood pressure ascertainment
* d) diagnosis of gestational hypertension, preeclampsia, or chronic hypertension at hospital discharge
* e) plan to receive postpartum care at a Women \& Infants Hospital (WIH)-affiliated clinic

Exclusion Criteria:

* a) fetal anomaly
* b) prisoners
* c) lack of smartphone
* d) inability to consent.

Outcomes:
Blood Pressure Ascertainment
Reasoning Enhancement With Feedback From a Generative AI in Nephrology (REFINe): A Randomized Evaluation of Generative AI Support in Nephrology Diagnosis
Trial ID: NCT07352475
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-11-20
Completion date: 2026-12-31
Sponsor: University Hospital, Lille
Collaborators: Institut Pasteur de Lille; Lille University
Conditions: Diagnosis; Clinical Decision-making; Artificial Intelligence (AI) in Diagnosis; Decision Support Systems, Clinical
Interventions: AI suggestion
Locations: Lille University Hospital (online study)
Countries: France
Enrollment: 100
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this clinical trial is to learn how artificial intelligence (AI) may help doctors make diagnoses in kidney medicine. The researchers want to know whether an AI tool called a large language model (LLM) can help doctors choose the correct diagnosis more often and feel more confident in their answers.

Before starting the study, the research team tested several AI models and chose one of the best performers, a GPT-5-class model set to use high reasoning effort.

The main questions this study aims to answer are:

1. Do doctors make more correct diagnoses when they can see AI suggestions?
2. Does seeing AI suggestions change how confident doctors feel about their diagnosis?

Researchers will compare doctors who receive AI suggestions with doctors who do not receive AI suggestions to see how the AI affects accuracy, confidence, and decision-making.

Participants will complete up to 10 online clinical cases. For each case, they will:

1. Read a short medical scenario
2. Suggest up to three possible diagnoses

(If in the AI group) Review the AI's suggestions and decide whether to change their answer

The study will also look at how long participants take to answer each case and how the AI's performance compares to the human answers.

Eligibility criteria:
Inclusion Criteria:

Adults aged 18 years or older.

Able to read and answer clinical vignettes in English or French.

Access to a computer or smartphone with an internet connection.

Provides informed consent online.

Participants are expected to have at least basic medical training (e.g., medical students, residents, fellows, or practicing clinicians), although no formal verification is required.

Exclusion Criteria:

Individuals under 18 years of age.

Inability to complete online study procedures.

Prior involvement in the design, development, or evaluation of the AI system used in this study.

Outcomes:
Final diagnostic accuracy (top-3) with vs without AI support
Artificial Intelligence Confocal Laser Endomicroscopy for Real-time Assessment of Gastric Cancer Risk: a Prospective Exploratory Study
Trial ID: NCT05459428
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-05-20
Completion date: 2024-06-30
Sponsor: Shandong University
Conditions: Intestinal Metaplasia of Gastric Mucosa; Confocal Laser Endomicroscopy
Locations: Department of Gastroenterology, QiLu Hospital, Shandong University
Countries: China
Enrollment: 250
Age range: 40 Years - 80 Years
Sex: ALL

Summary:
Current national guidelines recommend the use of OLGIM score to evaluate the risk of gastric cancer in patients, and close gastroscopic monitoring should be performed after OLGIM grade III/IV. However, it would not be clinically feasible to require at least four biopsies. Confocal laser endomicroscopy is able to magnify the tissue by 1000X, which can be seen at the cellular level. Observing goblet cells under Confocal laser endomicroscopy is simpler and diagnostic accuracy is comparable to pathology. Confocal laser endomicroscopy enables better assessment of intestinal metaplasia in gastric mucosa, thus quantifying the risk of gastric cancer and avoiding multiple biopsies.

Eligibility criteria:
Inclusion Criteria:

* Patients aged 40-80 years undergoing the confocal laser endomicroscopy examination

Exclusion Criteria:

* Patients with severe cardiac, cerebral, pulmonary or renal dysfunction or psychiatric disorders who cannot participate in gastroscopy
* Patients with previous surgical procedures on the stomach
* Patients with contraindications to biopsy
* Patients who refuse to sign the informed consent form

Outcomes:
Endoscopists concluded the sensitivity of CGGIM score in the diagnosis of OLGIM III/IV; Endoscopists derive the specificity of CGGIM score in the diagnosis of OLGIM III/IV; Accuracy of CGGIM Score for Diagnosing OLGIM III/IV by Endoscopists; AI yielded the sensitivity of CGGIM score in the diagnosis of OLGIM III/IV; AI yielded the specificity of CGGIM score in the diagnosis of OLGIM III/IV; AI yields accuracy of CGGIM score for diagnosis of OLGIM III/IV
Machine Learning Modeling the Risk of Refractory Pituitary Adenoma Using Radiomic and Pathomic Data
Trial ID: NCT05108064
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2019-01-01
Completion date: 2024-12-31
Sponsor: Huashan Hospital
Conditions: Pituitary Neoplasms
Interventions: Artificial intelligence model
Locations: Huashan Hospital
Countries: China
Enrollment: 1000
Age range: 18 Years - None
Sex: ALL

Summary:
Refractory pituitary adenoma is characterized by invasive tumor growth, continuous growth and/or hormone hypersecretion in spite of standardized multi-modal treatment such as surgeries, medications or radiations. Quality of life or even lives are threatened by these tumors. According to the 2017 World Health Organization's new classification guideline of pituitary adenoma, patients have to suffer from symptoms or complications caused by these tumors, to bear a heavy financial burden, and to accept additional therapeutic side effects when the diagnosis of "refractory pituitary adenoma" is made. If refractory pituitary adenoma could be predicted at early stage, these patients would be able to have a more frequent clinical follow-up, receive multiple effective treatment as early as possible, or even be enrolled in clinical trials of investigational medications, so as to prevent or delay the recurrence or persistent of the tumor growth. Therefore, the unmet clinical need falls into an early prediction system for refractory pituitary adenomas, which could provide accurate guidance for subsequent treatment in the early stage. The investigators have constructed a pituitary adenoma database including clinical data, radiological images, pathological images and genetic information. The investigators are proposing a study using machine learning to extract features from these multi-dimensional, multi-omics data, which could be further used to train a prediction model for the risk of refractory pituitary adenoma. The proposed model would also be validated in another prospectively collected database. The established model would be able to identify potential medication targets and provide guidance for personalized therapy of refractory pituitary adenoma.

Eligibility criteria:
Inclusion Criteria:

* All patients with pituitary adenoma

Exclusion Criteria:

* Patients who were not able to sign the informed consent

Outcomes:
The risk of refractory pituitary adenoma
A Health Promotion Intervention for Vulnerable School - Children and Families: a Cluster-randomized Trial
Trial ID: NCT05395364
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-03-01
Completion date: 2023-07-31
Sponsor: University of Minho
Conditions: Obesity; Health Promotion; Noncommunicable Diseases; Child Development; Life Style
Interventions: Intervention program BeE-school
Locations: Ana Duarte
Countries: Portugal
Enrollment: 735
Age range: 5 Years - 12 Years
Sex: ALL

Summary:
The BeE-school (Be Empowered in school) is a cluster-randomised trial that addresses the complexity of the social challenge (vulnerable school-age children). It aims to analyse the effectiveness of the intervention program, based on the promotion of health literacy and lifestyles, specifically on children's: 1-health literacy and infodemic resilience (Aim 1); 2- lifestyles (e.g. dietary intake, 24hmovement behaviour) (Aim 2); 3-overweight and obesity (Aim 3); 4-blood pressure (Aim 4). The project converges multiple disciplines (e.g. public health, informatics, law) and researchers with proven expertise in these fields to provide comprehensive and innovative answers. 478 children (6 schools) aged 6-12years old will participate in this cluster-randomised trial, having schools as the unit of randomisation, assigned into intervention (239-3schools) and the control arm (239 - 3 schools). This project will perform a social listening (online and offline) and bottom-up approach to tackling NCDs, focusing on health literacy and health promotion and recognising children's systems in daily life (e.g. family, teachers). Stakeholders' involvement goes far beyond a merely consultative approach; the researchers are committed to a genuine codevelopment process. Data collection includes sociodemographics, health literacy and infodemic resilience, dietary intake and children's 24-h movement behaviour (e.g. accelerometry), anthropometry (e.g. weight, height and waist circumference) and blood pressure. Data collection will occur at baseline and after the intervention (follow-up, 6 months after the beginning of the intervention). Expected outputs and outcomes include the creation of a model for characterising NCDs and health topics based on artificial intelligence techniques (e.g. deep learning, and social network analysis methods); improved health literacy and infodemic resilience of children, families and teachers; enhanced children's lifestyles (e.g. dietary intake, 24-h movement behaviour); reduction of NCDs' physical risk factors (e.g. overweight, raised blood pressure); feasible intervention program about health promotion and NCDs' prevention for school-aged children with vulnerabilities, and advocacy- policies about health promotion and NCDs' prevention.

Eligibility criteria:
Inclusion Criteria:

* Children from TEIP schools
* Ages between 5 to 12 years old

Exclusion Criteria:

* Children/families who don't speak/understand Portuguese

Outcomes:
Overweight and obesity; Risk of high blood pressure; Health literacy; Dietary intake; Physical activity; Sedentary behavior; Sleep time; Sleep quality
Development and Prospective Validation of a Pathology-Based Artificial Intelligence Model for Predicting the Time to Castration Resistance of Prostate Cancer
Trial ID: NCT07376057
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-01-01
Completion date: 2028-12-31
Sponsor: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Conditions: Prostatic Neoplasms, Castration-Resistant
Interventions: Artificial intelligence (AI)-based predictive model (developed)
Locations: Sun Yat-sen Memorial Hospital of Sun Yat-sen University
Countries: China
Enrollment: 150
Age range: 18 Years - None
Sex: MALE

Summary:
The goal of this predictive test is to prospectively test the performance of pre-developed artificial intelligence (AI) predictive model for predicting the time to castration resistance of prostate cancer. Investigators had developed this AI model based on deep learning algorithms in preliminary research, and it performed well in retrospective tests.

Eligibility criteria:
Inclusion Criteria:

1. Patients are diagnosed with intermediate- to high-risk prostate cancer; undergo prostate biopsy
2. Patients only received endocrine therapy for prostate cancer;
3. Patients with complete clinical and pathological information.
4. Patients agree to participate in this diagnostic test.

Exclusion Criteria:

1. Patients with other tumors and undergo systemic therapy .
2. The patient refused to participate in this diagnostic test.

Outcomes:
C-index (Concordance Index)
The Effects of an Artificial Intelligence-Based Clinical Orientation Program on Nursing Students Orientation Process in Pediatric Nursing Practice
Trial ID: NCT07564544
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-04-30
Completion date: 2026-06-11
Sponsor: Selcuk University
Conditions: Orientation; Clinical
Interventions: Artificial Intelligence-Based Clinical Orientation Program
Locations: Selcuk University; Selcuk University
Countries: Turkey (Türkiye); Turkey (Türkiye)
Enrollment: 90
Age range: 19 Years - None
Sex: ALL

Summary:
Aim: This experimental study aims to investigate the impact of an Artificial Intelligence (AI)-based orientation program on the clinical orientation process of nursing students during their pediatric nursing clinical practice.Materials and Methods: The study population will consist of 186 third-year nursing students enrolled in the spring semester at a state university. The sample will include 90 students (45 intervention, 45 control) who meet the study criteria and volunteer to participate. Students in the intervention group will receive an AI-based clinical orientation program, while the control group will receive no additional intervention beyond the standard faculty and hospital orientation. Data will be collected between February and March 2026 using a "Descriptive Information Form," the "Clinical Adaptation Scale for Student Nurses," and the "Therapeutic Communication Skills Scale for Nursing Students" in a pre-test/post-test design.Statistical Analysis: Data will be analyzed using SPSS 25.0. Descriptive statistics (frequency, percentage, mean, and standard deviation) will be used. Normality will be assessed via Kolmogorov-Smirnov/Shapiro-Wilk tests. For group comparisons, Chi-square, ANOVA, independent samples t-test, Mann-Whitney U, and Wilcoxon tests will be utilized. The statistical significance level will be set at $p \< 0.05$.Ethical Considerations: Ethical committee approval and necessary permissions from scale authors will be obtained before the study begins. Informed consent will be collected from all participants after explaining the study objectives.Keywords: Orientation training, nursing, student, artificial intelligence, clinical practice.

Eligibility criteria:
Inclusion Criteria:

* Being enrolled in the Pediatric Nursing course for the first time.
* Being a third-year nursing student.
* Providing voluntary informed consent to participate in the study.

Exclusion Criteria:

* Being absent during the course of the study.
* Currently being employed as a healthcare professional.
* Having a frozen enrollment status.

Outcomes:
Introductory Information Form