moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
A Single-Center, Open-Label, Randomized, Parallel Controlled Trial Evaluating the Effectiveness of Artificial Intelligence-Enhanced Management for Coronary Heart Disease (AIM-CHD) Delivered Via Mobile Application
Trial ID: NCT06686056
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-11-23
Completion date: 2025-06-30
Sponsor: China National Center for Cardiovascular Diseases
Conditions: Secondary Prevention of Coronary Heart Disease
Interventions: AIM-CHD Mobile Health Intervention; Usual post-discharge care
Locations: Fuwai Hospital, Chinese Academy of Medical Sciences/National Center for Cardiovascular Diseases
Countries: China
Enrollment: 1100
Age range: 18 Years - 85 Years
Sex: ALL

Summary:
The goal of this clinical trial is to find out if an artificial intelligence (AI)-enhanced mobile app can help people with coronary heart disease (CHD) better manage their health after being discharged from the hospital. The main questions it aims to answer are:

1. Does the AI-enhanced app help lower bad cholesterol (LDL-C) levels within 3 months after leaving the hospital?
2. Does the app improve other health measures, like blood pressure, blood sugar control, weight, medication adherence and cardiac events?

Researchers will compare the AI-enhanced app to usual care, where participants receive usual health advice without using the app.

Participants will:

1. Be randomly assigned to use either the AI-enhanced app or receive usual care.
2. Use the app to track and manage their health, receive reminders, and get educational tips.
3. Attend checkups at 3 months to measure cholesterol levels and other health outcomes.

The study hopes to show that using an AI-enhanced app can make it easier for people with CHD to stay healthy and avoid future heart problems.

Eligibility criteria:
Inclusion Criteria:

* CHD patients aged 18-85 years
* ability of the patient or close relatives to use smartphones and applications
* willingness to participate and sign the informed consent form

Exclusion Criteria:

* severe cognitive impairment
* advanced malignant tumors
* expected survival of less than 3 months
* severe multi-organ failure
* refusal to sign the informed consent form

Outcomes:
LDL-C levels
Application of Spectral Methods to Assess the Potential of Gametes and Embryos, as Well as Human Reproductive Capabilities
Trial ID: NCT06266169
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-09-20
Completion date: 2025-12-31
Sponsor: Family Planning Center of Women's Welfare Clinic #44 of Pushkin District
Conditions: Infertility
Interventions: making artificial intelligence based decisions of gamete, embryo and endometrial potential
Locations: Family planning center
Countries: Russia
Enrollment: 1064
Age range: 20 Years - 44 Years
Sex: ALL

Summary:
Relevance of the research topic: At present, in the world, a kind of "plateau" in the efficiency of assisted reproductive technologies has been achieved, which ensures a birth rate of 30% per embryo transfer. At the same time, a relatively high (15-20%) and stable rate of miscarriages is preserved. Until now, no effective methods for assessing the potential of gametes and embryos, as well as human reproductive capabilities, have been offered. In these conditions, to increase the rate of births after IVF, clinicians have to increase the number of transferred embryos at a time, however, this leads to a sharp increase in complications of IVF, such as multiple pregnancy. In addition, until today, the clinical effectiveness of assessing the potential of endometrium using gene expression determination methods has not been shown. Therefore, to ensure the effectiveness and safety of infertility treatment, it is necessary to develop methods for predicting the potential of gametes and embryos, as well as human reproductive capabilities. For this purpose, the investigators assume to use Raman spectroscopy of the environment obtained from the objects of research, as well as fluorescent spectroscopy of endometrium. The objects of the research are gametes (spermatozoa) and embryos, used culture medium, endometrium. The subject of the study is the set of factors, that exists in the objects of research and their ability to determine the outcomes of infertility treatment.

Eligibility criteria:
Inclusion Criteria:

* Embryos: embryos that have reached the blastocyst stage
* Sperm: samples used for IVF during infertility treatment
* Endometrium: endometrial spectra in cases where an embryo transfer was performed into the uterus

Exclusion Criteria:

* For all groups: ectopic pregnancy
* Embryos: presence of only one blastocyst, and that embryo prognosed by the model as negative
* Sperm: a cycle where less than 3 oocytes suitable for fertilization were obtained; total pathological fertilization; 60% or more immature oocytes at the time of fertilization registration

Outcomes:
Embryonic model performance; Gamete model performance; Endometrial model performance; Embryonic model clinical efficiency; Endometrial model clinical efficiency
A Multi-Centre Observational Study to Evaluate the Clinical Utility of Returning Genomic Aberration Results Using the Oncomine Precision Assay in Advanced or Metastatic Non-Small Cell Lung Cancer Within the Exactis Network
Trial ID: NCT04564079
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2021-06-01
Completion date: 2023-11-21
Sponsor: Exactis Innovation
Conditions: NSCLC
Locations: Centre hospitalier universitaire Dr-Georges-L.-Dumont; The Moncton Hospital; The Ottawa Hospital; Princess Margaret Cancer Centre; Jewish General Hospital; Centre hospitalier universitaire de Sherbrooke (CHUS)
Countries: Canada; Canada; Canada; Canada; Canada; Canada
Enrollment: 100
Age range: 18 Years - None
Sex: ALL

Summary:
A Multi-centre Observational Study to Evaluate the Clinical Utility of Returning Genomic Aberration results Using the Oncomine Precision Assay in Advanced or Metastatic Non-Small Cell Lung Cancer Patients within the Exactis Network

Eligibility criteria:
Inclusion Criteria:

* Patients with NSCLC, which is (i) advanced or metastatic (stage IIIb/IV), (ii) non- squamous histology NSCLC as confirmed by local histopathology (mixed squamous and adenocarcinoma is allowed), and has (iii) radiographically measurable disease with at least one bidimensionally measurable lesion of \> 1 cm by CT scan
* No prior treatment for advanced or metastatic NSCLC, except for palliative radiation therapy to non-lung or non-thorax metastases. Patients who received palliative radiation therapy to metastases located within or approximately to the lung or thorax must be evaluated for eligibility.
* Willing and able to provide adequate blood sample prior to starting treatment.
* Willing to provide primary or metastatic tissue, if available.
* Signed and dated Research Ethics Board (REB)-approved informed consent form for Exactis-01 or PMT.
* Not pregnant or breastfeeding

Exclusion Criteria:

• Patients who have a history of another active cancer within the past 2 years from date of consent except cervical cancer in situ, basal cell carcinoma of the skin or another in situ carcinoma that is considered cured by the investigator.

Outcomes:
To compare the number of genomic aberrations detected by the Oncomine Precision Assay vs Standard of Care
The Effectiveness of NeuroBot in Alleviating Anxiety of Patients Undergoing Neurovascular Surgeries
Trial ID: NCT07144540
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-10-01
Completion date: 2027-05-30
Sponsor: Chinese University of Hong Kong
Collaborators: The Hong Kong Polytechnic University; Hospital Authority, Hong Kong
Conditions: Anxiety; Patient Compliance; Knowledge
Interventions: NeuroBot; Standard Care (in control arm)
Locations: The Chinese University of Hong Kong; The Chinese University of Hong Kong
Countries: Hong Kong; Hong Kong
Enrollment: 146
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this randomized controlled trial is to investigate the effectiveness of NeuroBot in alleviating anxiety of patients undergoing neurovascular procedures. The main questions it aims to answer are:

Is NeuroBot effective in alleviating anxiety? Is NeuroBot effective in enhancing patients' procedure and disease-related knowledge? Is NeuroBot effective in enhancing patients' compliance to treatment? If there is a comparison group: Researchers will compare with standard perioperative education (control group) to see if there is any difference in results.

Participants will be invited to chat with the artificial intelligence based chatbot "NeuroBot" for 6 weeks, from the date of preoperative clinic visit till 4 weeks after the surgery. The participants are allowed to access the NeuroBot at anytime anywhere, to ask surgery- related questions at their discretion.

Eligibility criteria:
Inclusion Criteria:

1. aged above 18 years;
2. scheduled for elective neuroendovascular surgery;
3. able to understand Chinese and read Chinese;
4. mobile device with internet capability.

Exclusion Criteria:

1. identified cognitive impairment or have difficulty in understanding study procedures, following instructions, or providing informed consent;
2. prior exposure to health chatbot to acquire health-related information in previous 3 months;
3. have neurovascular surgeries before

Outcomes:
Anxiety
Anonymization of Clinical Data From Pseudonymized Databases Collected as Part of Previus Clinical Trials on Multiple Myeloma
Trial ID: NCT07283224
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-06-30
Completion date: 2028-01-01
Sponsor: IRCCS Azienda Ospedaliero-Universitaria di Bologna
Conditions: Myeloma Multiple
Locations: IRCCS AOU Policlinico Sant'Orsola
Countries: Italy
Enrollment: 600
Age range: 18 Years - None
Sex: ALL

Summary:
This is a retrospective, observational, single-center study conducted at the Hematology Unit of the IRCCS Azienda Ospedaliero-Universitaria di Bologna. The study aims to integrate genomic, clinical, and imaging data-all previously collected and pseudonymized-from earlier spontaneous studies carried out by the Hematology Unit. The main goal is to explore how genetic, clinical, and imaging features relate to disease outcomes and to develop predictive models using artificial intelligence (AI) and machine learning (ML). These models may help forecast disease progression and treatment response in the future. By combining different types of data, the research team hopes to develop new methods to predict disease progression and treatment outcomes. No new procedures, tests, or patient visits are required for this study. All the information analyzed will come from existing data collected as part of past clinical practice or previous research projects. Once anonymized, it will no longer be possible to identify individual patients. The anonymized dataset will be securely developed and stored by the bioinformatics team at the Hematology Unit of IRCCS AOUBO.

Eligibility criteria:
Inclusion Criteria:

* Age ≥18 years
* Signed Informed Consent
* Patients with confirmed diagnosis of MM, previously enrolled in spontaneous trial conducted at the UOC Ematologia, IRCCS AOU di Bologna, from 2021, for whom pseudonymized datasets are already available at the start of this study.

Exclusion Criteria:

* None

Outcomes:
Detailed description of the genomic profiles of the BM-CD138+ compartment(s), both from BM (gDNA profile) and PB (cfDNA and CMMCs) at baseline. Description of the disease distribution with imaging techniques.
Diabetes in Asians at Risk in Youth
Trial ID: NCT07452159
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-03-18
Completion date: 2029-01-02
Sponsor: Singapore General Hospital
Collaborators: SingHealth Duke-NUS Academic Medical Centre; Duke-NUS Centre of Quantitative Medicine; Data Science and Artificial intelligence Laboratory, SGH; Translational Research and Innovation Lab, SingHealth
Conditions: Young Onset Asian Diabetes (Under 40 Years)
Interventions: Oral glucose tolerance test and HbA1c; Anthropometric measurements; Body impedance Analysis; Handgrip measurement; Continuous glucose monitoring; Food diary recording over days 2 and 3 of continuous glucose monitoring; Survey; Skin reaction information; Download of social media data; Whole genome sequencing
Enrollment: 3000
Age range: 21 Years - 39 Years
Sex: ALL

Summary:
This observational study aims to develop novel contemporary risk factor tools for detecting young onset diabetes (under 40y) in young Asians in Singapore. The investigators aim to recruit 3000 people not currently known to have diabetes, selected across age and Body Mass Index categories, and perform formal diagnostic tests for diabetes (oral glucose tolerance test and HbA1c). The participants will also undergo continuous glucose monitoring sensor, keep a food diary, and undergo body composition analysis using bioimpedance analysis, handgrip measurements for muscle strength, and sociobehavioural profiling through download of a curated list of social media interactions. The participants will also have a blood sample retrieved and stored for subsequent comparison between people with normal glucose versus abnormal glucose levels. The whole group will also have another blood sample stored for whole gene analysis.

Eligibility criteria:
Inclusion Criteria:

* Asian/Part Asian ethnicity living in Singapore, not known to have diabetes

Exclusion Criteria:

* Pregnancy women
* Non-Asian ethnicity
* On active cancer/steroid therapy
* Haemoglobinopathies or haemolytic anaemia
* Previous bariatric surgery
* Previous partial/total pancreatectomy

Outcomes:
Diagnosis of diabetes
Ambient Scribe in General Practice: a Multi-perspective Before-after Longitudinal Mixed-methods Study
Trial ID: NCT06691724
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-12-09
Completion date: 2025-07-02
Sponsor: Erasmus Medical Center
Conditions: Workload
Interventions: LLM-based transcription and reporting tool
Locations: Erasmus MC
Countries: Netherlands
Enrollment: 800
Age range: 18 Years - None
Sex: ALL

Summary:
General practitioners (GPs) in the Netherlands are under unsustainable pressure. Recent surveys show that 68% of general practitioners find the workload too high and 18% find their work extremely or very stressful. The pressure on GPs significantly harms patient care, as reduced physician well-being can negatively impact patient experiences, treatment adherence, patient-provider communication, healthcare costs, care quality, and patient safety.

A key contributor to the stress is the increasing time commitment associated with clinical documentation. The documentation process has evolved into a time-intensive task, which is a significant obstacle to efficient patient care. Large language models (LLMs) are promising artificial intelligence (AI) solutions to reduce the documentation in general practice. In this project, the investigators aim to study an AI-based transcription and reporting tool in general practice.

Eligibility criteria:
General practitioners:

Inclusion:

* Planning to implement the AI-based transcription and reporting tool
* Give informed consent for observation and/or interview and/or questionnaire

Exclusion:

\- Insufficient knowledge of the Dutch language to be interviewed

Patients:

Inclusion:

* Give informed consent for questionnaire and/or interview
* For questionnaire: had a consultation with the GP
* For interview: had a consultation with the GP in which the tool was used

Exclusion:

\- Insufficient knowledge of the Dutch language to be interviewed

Outcomes:
Time spent on documentation
Detection of Coronary Artery Calcifications by Whole Blood Transcriptome Analyzed by Artificial InTelligence Algorithms. (CAC-TRAIT Study)
Trial ID: NCT05619042
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2022-11-07
Completion date: 2024-01-24
Sponsor: Santiago Gabriel Miriuka
Collaborators: Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia
Conditions: Atherosclerosis; Atheroscleroses, Coronary; Atherosclerotic Plaque
Locations: Clinica Sagrada Familia
Countries: Argentina
Enrollment: 800
Age range: 30 Years - 75 Years
Sex: ALL

Summary:
The goal of this observational study is to determine the diagnostic accuracy of peripheral whole blood transcriptomes analyzed by an artificial intelligence algorithm to detect the presence and extent of coronary calcification in individuals without a history of known cardiovascular disease.

The main question it aims to answer is:

1\. Will the proposed method predict the presence and extent of coronary artery calcification from the peripheral whole-blood transcriptomes?

Eligibility criteria:
Inclusion Criteria:

* Men and women between 30 and 75 years attending the institution for a non-contrast cardiac CT scan (coronary calcium score) either by medical indication or voluntary for cardiovascular risk assessment.
* Signature of the informed consent form

Exclusion Criteria:

* Previously known chronic renal or hepatic insufficiency.
* Active chronic lung disease, defined as exacerbated asthma, exacerbated chronic obstructive pulmonary disease (COPD), or pulmonary fibrosis.
* Active and/or previous cardiovascular disease, defined as previous acute myocardial infarction, stable or unstable angina, cerebrovascular accident, history of vascular interventions (coronary or another territory), heart failure, severe cardiomyopathies, or valvulopathies.
* Uncontrolled hyper or hypothyroidism.
* Suprarenal insufficiency.
* Previous surgeries in the last 3 months.
* Severe trauma in the last 6 months, defined as one that involved bone fractures and/or surgical interventions.
* Known active cancer disease or under treatment (acute or preventive), or history of cancer disease without criteria for a cure.
* Known autoimmune disease active or in treatment.
* Ongoing pregnancy, postpartum period of fewer than 12 months, or breastfeeding.
* Other serious illnesses with an estimated life expectancy of fewer than 12 months (according to the investigators).
* Temperature greater than 37.5°C recorded by a thermometer or any acute infection caused by viruses or bacteria confirmed by a health professional in the previous 30 days.
* Pathologies under immunosuppressive treatment

Outcomes:
Coronary artery calcium score
Interventional Study Focused on Providing Personalised Health Recommendations to the General Population Through an Integrated AI Guided App as a Strategy for Gastric Cancer Prevention (AIDA)
Trial ID: NCT06421324
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-06-01
Completion date: 2026-12-31
Sponsor: Fundación para la Investigación del Hospital Clínico de Valencia
Conditions: Gastric Cancer; Cancer Prevention; Helicobacter Pylori Infection; Artificial Intelligence
Interventions: Health reccommendations
Locations: Hospital Clínico Universitario de Valencia
Countries: Spain
Enrollment: 450
Age range: 18 Years - None
Sex: ALL

Summary:
This clinical study aims to be used to implement and validate the AIDA tool in two phases:

* Phase 1: Risk stratification and personalised recommendations \& Model development
* Phase 2: Mechanistic Model (Bioresource) development \& testing

Eligibility criteria:
Inclusion Criteria:

* Subjects ≥ 18 years old with a diagnosis of GIM or previous or current H. pylori infection, to whom a gastroscopy is indicated within clinical care
* Availability of a signed informed consent form to participate in the study

Exclusion Criteria:

* Patients to whom an endoscopy is performed for the follow-up of another illness such as oesophageal varices and/or for therapy such as endoscopic dilation, feeding tube placement or endoscopic resection
* Subjects with a clinical diagnosis of gastric diseases other than GIM or GC
* Patients who have received antimicrobials during the four weeks prior to the endoscopy
* Patients who have received proton pump inhibitors and/or bismuth-based treatments at least two weeks prior to the endoscopy
* Subjects for whom clinical data are not available: H. pylori status, eradication treatment, sex, age, tobacco smoking, and first-degree family history of gastric cancer
* Subjects who lack the mental capacity to understand the nature and requirements of the study and who lack the ability to give informed consent

Outcomes:
Risk of developing Gastric Cancer based on medical records; H. pylori Eradication Therapy Recommendation; GIM risk score assessment using imaging modalities
Validation of an AI Tool for Improving MASLD Advanced Liver Fibrosis Diagnosis in Primary Care: A Provider-Level Crossover Randomized Controlled Trial Pilot
Trial ID: NCT07305324
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-06-15
Completion date: 2027-06-15
Sponsor: Yale University
Conditions: MASLD; Fibrosis of Liver
Interventions: FibroX; Usual Care
Enrollment: 40
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this clinical trial is to learn whether an artificial intelligence (AI) tool called FibroX can help primary care providers better diagnose significant liver fibrosis (≥F2) and clinically significant portal hypertension in adults with metabolic dysfunction-associated steatotic liver disease (MASLD).

The main questions it aims to answer are:

* Can FibroX improve the accuracy of diagnosing significant liver fibrosis (≥F2) and clinically significant portal hypertension compared to usual care?
* Is FibroX easy to use and acceptable to primary care providers in simulated clinical settings?
* Do providers trust FibroX as a decision-support tool?

Researchers will compare FibroX-assisted care to usual care to see if FibroX improves diagnostic accuracy, provider trust, and supports better decision-making.

Participants will:

* Be primary care providers (MDs, DOs, NPs, PAs) from diverse clinics
* Review simulated patient cases with MASLD risk factors
* Use either usual care tools (standard labs and optional FIB-4 calculator) or FibroX (AI-generated risk score, triage band, and explainability panel)
* Make diagnostic and referral decisions for each case
* Complete surveys on usability, trust in AI, confidence, and cognitive workload

This study will help determine whether FibroX can be integrated into real-world primary care workflows to support earlier and more accurate detection of liver fibrosis and portal hypertension, potentially reducing missed diagnoses, unnecessary referrals, and improving patient outcomes.

Eligibility criteria:
Inclusion Criteria:

* Licensed primary care providers (MD, DO, NP, or PA)
* Currently practicing in adult primary care (≥0.5 Full-Time Equivalent)
* Affiliated with one of the participating clinics (academic, community, or Federally Qualified Health Center)
* Willing and able to participate in simulated electronic health record (EHR)-based case reviews
* Able to provide informed consent

Exclusion Criteria:

* Providers not actively practicing in adult primary care
* Providers with less than 0.5 FTE in clinical practice
* Prior involvement in the development or validation of the FibroX tool
* Inability to complete both simulation periods due to scheduling or other constraints

Outcomes:
Diagnostic Accuracy for Significant Liver Fibrosis (≥F2) and Clinically Significant Portal Hypertension Using FibroX Compared to Usual Care; System Usability Scale (SUS) Score for FibroX Integration; Provider Trust in AI Tool (FibroX); Median Decision Time per Case