moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Improving the Accuracy of Artificial Intelligence Triage in Primary Care
Trial ID: NCT07237919
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-04-01
Completion date: 2029-07
Sponsor: University of Manchester
Collaborators: University of Cambridge
Conditions: Primary Care; Artificial Intelligence (AI)
Interventions: AI triage; No AI triage
Locations: NHS GP practices
Countries: United Kingdom
Enrollment: 226821
Age range: None - None
Sex: ALL

Summary:
WHY ARE WE DOING THIS? When patients contact their GP practice, the first step is to work out what kind of help they need and how quickly it's needed. This is called 'triage' and is important for patient safety.

Artificial Intelligence (AI) can help make triage faster. While AI is already being used in the NHS, we don't know how accurate it is or if it treats all patients fairly.

WHAT WILL WE DO?

We will collect anonymised data from patients that use an AI triage system called Patchs in GP practices in England. The project will last four years. We will analyse the data in four steps:

1. Look at data from GP practices using Patchs without AI triage to see how they currently triage patients and what problems they face.
2. Use data from GP practices using Patchs (both with AI on and off) to make the AI triage more accurate.
3. Check data from GP practices using Patchs with AI triage off to measure how well the updated AI system works.
4. Give the improved AI triage system to GP practices already using AI.

At each step, we will check whether patients from different backgrounds are treated fairly.

HOW WILL WE ANALYSE THE DATA? We will use statistical methods to compare the triage decisions made by the AI with those made by clinical staff. This analysis will also be used to check that the AI works fairly for patients from different backgrounds.

WHAT DIFFERENCE WILL WE MAKE? Our research will show the problems with triage and explain how an improved AI system could help patients get the care they need more quickly.

Eligibility criteria:
Inclusion Criteria:

* GP practices using the Patchs system

Exclusion Criteria:

* N/A

Outcomes:
F1 score
Using Artificial Intelligence Models for Predicting The Need for Intubation and Successful Weaning From Mechanical Ventilation in ICU Patients
Trial ID: NCT07065838
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-10-01
Completion date: 2027-11-01
Sponsor: Assiut University
Conditions: Respiratory Distress Syndrome (RDS)
Enrollment: 600
Age range: 18 Years - 90 Years
Sex: ALL

Summary:
Using artificial intelligence (AI) approach to build a model to determine the optimal timing of intubation and optimal timing of weaning from MV for ICU patients, and compare outcomes with these which depend on clinicians dicision.

And also assess whether AI-assisted decisions improve patient outcomes (e.g., reduced intubation delays, shorter ventilation duration).

Eligibility criteria:
Inclusion Criteria

* Adult patients (≥18 years) in ICUs at risk of respiratory failure.
* Adults (≥18 years) receiving mechanical ventilation for \>24 hours and deemed ready for weaning.

Exclusion Criteria:

* \- Patients with a pre-existing "Do Not Intubate" (DNI) order.
* \- Patients with irreversible neurological conditions affecting weaning

Outcomes:
Perfect time to intubate and to Wean
Remote Breast Cancer Screening Based on Automated Breast Ultrasound Among Chinese Women: a Multi-center, Prospective, Cohort Study
Trial ID: NCT04527510
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-01-01
Completion date: 2025-08
Sponsor: The First Affiliated Hospital of the Fourth Military Medical University
Conditions: Breast Cancer; Mass Screening; Cancer Screening; Ultrasound Imaging; AI (Artificial Intelligence)
Interventions: Conventional-reading; Second-reading; Concurrent-reading; Tow views-reading; Handheld US-screening
Locations: The First Affiliated Hospital of Fourth Military Medical University; The First Affiliated Hospital of the Fourth Military Medical University
Countries: China; China
Enrollment: 6333
Age range: 35 Years - None
Sex: FEMALE

Summary:
A multi-center, prospective, cohort study to evaluate the efficiency of breast cancer screening based on Automated Breast Ultrasound (AB US) with remote reading mode.

Eligibility criteria:
Inclusion Criteria:

1. Any age at 35 and over
2. Screening breast cancer with AB US
3. Obtaining written informed consent

Exclusion Criteria:

1. symptoms of breast cancer
2. surgical of breast within 12 months prior to the study
3. known diagnosis breast cancer
4. poor image quality
5. follow-up was less than 12 months

Outcomes:
The performance of breast cancer screening based on AB US with remote reading mode
SAFE-AI ONCO-TRACK: Multimodal GenAI for Early Detection of Minimal Residual Disease and Recurrence in Gastrointestinal Oncology
Trial ID: NCT07189520
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-06-01
Completion date: 2030-06-01
Sponsor: Università Politecnica delle Marche
Conditions: Rectal Cancer
Interventions: Artificial Intelligence
Enrollment: 700
Age range: 18 Years - None
Sex: ALL

Summary:
Current decision tools (TNM, MRI/PET, CEA, and other serum markers, as well as single-marker genomics) are insufficiently predictive of responders, fail to detect early MRD in many cases, and rarely connect molecular biology to dynamic perioperative data. SAFE-AI will build and validate multimodal, explainable GenAI models that fuse liquid/tissue multi-omics with radiology and clinical trajectories to:

(i) detect MRD earlier, (ii) improve recurrence-risk calibration, and (iii) support non-invasive "virtual biopsy"-inferring tissue-level features from blood profiles, and vice-versa, to mitigate missing-modality gaps. This is grounded in the strong mechanistic premise that integrating heterogeneous molecular signals with imaging captures tumour-host biology more completely than single-modality assays, enabling actionable, calibrated risk estimates for rectal and oesophageal cancer.

The clinical hypothesis is that such integrated models can improve recurrence prediction by at least 20% over guideline baselines, with transparent uncertainty and bias monitoring to meet EU AI Act/MDR expectations.

Eligibility criteria:
Inclusion Criteria (Justification in parenthesis):

* Age ≥18 years (RC and EC are primarily adult-onset cancers, and adult inclusion aligns with ethical biospecimen collection and consent processes.)
* Histologically confirmed diagnosis of rectal or esophageal cancer (Confirms clinical relevance and eligibility for standard treatment pathways.)
* Treatment plan includes surgical resection with curative intent (Ensures applicability to MRD and outcome prediction tasks.)
* Undergoing standard-of-care neo-adjuvant or perioperative therapy (Ensures data consistency and relevance to response modelling.)
* Ability and willingness to provide informed consent for biospecimen and clinical data use (Meets ethical requirements for participation.)
* Availability for longitudinal blood sampling at T0 (baseline), T1 (3 months post-treatment), and T2 (6 months post-treatment) (Critical for temporal biomarker analysis.)
* Optional Inclusion: Access to tumor tissue (archival or fresh) for multi-omic profiling (Supports deep integrative biomarker discovery.)

Exclusion Criteria:

* Diagnosis of non-resectable or metastatic disease at enrollment (Excludes non-curative settings where the longitudinal biomarker protocol may not be feasible.)
* Emergency surgeries or treatment plans that deviate from standard protocols (To maintain data comparability.)
* Inability or refusal to provide informed consent (Essential for ethical compliance.)
* Failure to complete biospecimen donation or key follow-up timepoints (Maintains data integrity and model reliability.)

Outcomes:
Primary outcomes
Understanding Artificial Intelligence Anxiety Within Different Physical Activity Profiles: Comparing Athletes and Sedentary Individuals
Trial ID: NCT07102420
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2025-08-20
Completion date: 2025-10-20
Sponsor: KTO Karatay University
Conditions: Athletes; Sedanter
Interventions: Questionnaire and Physical Exam
Locations: Kto Karatay University
Countries: Turkey (Türkiye)
Enrollment: 663
Age range: 18 Years - 45 Years
Sex: ALL

Summary:
The aim of this study is to compare artificial intelligence anxiety (AIA) in athletes and sedentary individuals, evaluate its relationship with exercise frequency, and identify the subdimensions that cause high anxiety at the level of AIA scale items.

Eligibility criteria:
Inclusion Criteria:

* Being between 18 and 45 years of age
* Having engaged in regular exercise or sports at least twice a week over the past six months (for athlete group)
* Not having engaged in regular exercise during the past six months (for sedentary group)
* Being exposed to basic concepts of artificial intelligence (via media, news, education, etc.)
* Having sufficient cognitive ability to complete the questionnaires

Exclusion Criteria:

* Having a chronic neurological, orthopedic, cardiovascular, psychological, or similar medical condition
* Having a serious physical disability that prevents participation in exercise
* Incomplete or inconsistent responses in the questionnaires
* Having undergone a recent surgical operation
* Lack of voluntary consent to participate in the study

Outcomes:
Artificial Intelligence Anxiety Scale
Assessment of Accuracy and Safety of LabTest Checker - Clinical Evaluation of a Patient-centered Clinical Decision Support System Based on AI
Trial ID: NCT05813938
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-12-22
Completion date: 2023-03-31
Sponsor: Labplus Sp. z o.o.
Conditions: Abnormalities in the Blood Test Results
Interventions: LabTest Checker
Locations: Oddział Chorób Wewnętrznych i Chemioterapii Onkologicznej, Samodzielny Publiczny Szpital Kliniczny im. Andrzeja Mielęckiego Śląskiego Uniwersytetu Medycznego w Katowicach
Countries: Poland
Enrollment: 101
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this study is to assess performance of a new clinical decision support system named LabTest Checker in a cohort of adult patients reporting to the internal ward's emergency department. The main question it aims to answer is:

• how accurate and safe is LabTest Checker.

A set of blood laboratory tests will be ordered for each participant. Participants will use LabTest Checker to obtain an interpretation of their test results. The interpretation provided by LabTest Checker will then be compared with the interpretation given by an internal medicine specialist to assess the accuracy and safety of the tool.

Eligibility criteria:
Inclusion Criteria:

* adult
* requires laboratory test investigation

Exclusion Criteria:

* under 18 years old
* pregnant
* no need for laboratory test investigation

Outcomes:
accuracy; sensivity to emergency; sensivity to urgency; safety
Positive Predictive Value of Machine Learning Tools (Audiogene v4.0) for Diagnosing DFNA9 in a Large Series of p.Pro51Ser Variant Carriers in COCH.
Trial ID: NCT04331015
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2020-02-01
Completion date: 2020-03-28
Sponsor: Jessa Hospital
Conditions: DFNA9; Sensorineural Hearing Loss
Interventions: Pure tone audiometry
Locations: Jessa Hospital; University of Antwerp
Countries: Belgium; Belgium
Enrollment: 111
Age range: 18 Years - None
Sex: ALL

Summary:
To study the positive predictive value of Audiogene v.4.0 open source online machine learning tool in accurately predicting DFNA9 (DeaFNess autosomal dominant ninth) as top 3 gene loci in a large series of genetically confirmed c.151C\>T,p.Pro51Ser (p.P51S) variant carriers in COCH (coagulation factor C Homology).

Eligibility criteria:
Inclusion Criteria:

* at least 18 years
* genetically confirmed c.151 C\>T, p.Pro51Ser variant carrier in COCH gene
* not contra-indication for audiometric testing

Exclusion Criteria:

* \<18 years
* no carrier status for c.151C\>T, p.Pro51Ser
* no auditory data available

Outcomes:
hearing threshold; age; prediction gene locus
Tuberculosis Screening With AeoNose and CAD4TB in Paraguayan Prisons
Trial ID: NCT04407325
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-10-18
Completion date: 2023-12-01
Sponsor: Radboud University Medical Center
Collaborators: European Union; Instituto de Saude Publica da Universidade do Porto; VHIR; University Medical Center Groningen; Laboratorio central de salud publica, Paraguay; Instituto Nacional de Enfermedades Respiratorias y del Ambiente Juan Max Boetner; National TB program Paraguay; IICS Paraguay
Conditions: Volatile Organic Compounds; Radiography
Interventions: electronic nose device; dig chest Xray with automated readings
Locations: Buen Pastor; UPI
Countries: Paraguay; Paraguay
Enrollment: 5000
Age range: 16 Years - None
Sex: ALL

Summary:
Two diagnostic tools for TB screening in high risk groups need additional assessment: the AeoNose™, an 'electronic nose device' for breath sampling, and digital chest X-ray (CXR) with computer aided detection with CAD4TB® software. This study will systematically screen prisoners and its' employees for TB, test the diagnostic performance of AeoNose™ and CAD4TB (both individually and together) as a TB screening tool and and establish Mycobacterium tuberculosis epidemiology in Paraguayan prisons.

Eligibility criteria:
Inclusion Criteria:

* All persons, either being a prisoner or employee, of the involved penal institutions after providing informed consent, including those with known active TB disease and/or currently on TB treatment

Exclusion Criteria:

* A potential participant who meets any of the following criteria will be excluded from participation in this study:

  1. Unable to exhale breath through the AeoNose™ during five minutes due to respiratory insufficiency
  2. Unable to stand in an upright position for the CXR
  3. Unable to communicate and comply with the instructions of the study team

Outcomes:
Volatile organic compound signal as measured with the AeoNose™; Tuberculosis probablity score using Computed Automated detection software CAD4TB® on chest Xrays; Positive mycobacterial sputum test
Application of Artificial Intelligence on the Diagnosis of Helicobacter Pylori Infection and Premalignant Gastric Lesion: A Multihospital Study
Trial ID: NCT05762991
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2021-12-24
Completion date: 2026-12-31
Sponsor: National Taiwan University Hospital
Conditions: Helicobacter Pylori Infection; Premalignant Lesion
Locations: Yi-Chia Lee
Countries: Taiwan
Enrollment: 2000
Age range: 20 Years - 80 Years
Sex: ALL

Summary:
The aim of this diagnostic accuracy study is to evaluate the application of artificial intelligence on the diagnosis of Helicobacter pylori infection and premalignant gastric lesions based on upper endoscopic images. We use techniques of artificial intelligence to analyze the correlation between endoscopic images and urea breath test results/histopathological results.

Eligibility criteria:
Inclusion Criteria:

1. Age 20-80
2. Scheduled urea breath test and endoscopy

Exclusion Criteria:

1\. History of gastric surgery

Outcomes:
Sensitivity to detect premalignant gastric lesions
Pulmonary Arterial Hypertension and Associated Cardiovascular Disease Detection Using Artificial Intelligence
Trial ID: NCT07147725
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-10-01
Completion date: 2027-08-01
Sponsor: Imperial College London
Collaborators: Imperial College Healthcare NHS Trust
Conditions: Pulmonary Hypertension; Cardiovascular Diseases (CVD)
Interventions: AI Stethoscope
Locations: Imperial College Healthcare NHS Trust
Countries: United Kingdom
Enrollment: 1000
Age range: 18 Years - None
Sex: ALL

Summary:
Cardiovascular disease (CVD) is a leading global cause of morbidity and mortality and excessive healthcare expenditures. Pulmonary hypertension (PH) represents an insidious and progressive subset of CVD affecting an estimated 1% of the general population, increasing to up to 10% in the population over the age of 65. Recent advancements in artificial intelligence (AI) have shown promise in transforming PH diagnosis by enabling the analysis of complex physiological data. Specifically, AI algorithms applied to electrocardiography (ECG) and phonocardiography (PCG) waveforms captured through novel medical devices, such as smart stethoscopes, have demonstrated potential in detecting PH and other cardiovascular conditions with high sensitivity and specificity.

Despite the promising capabilities of AI algorithms, a significant barrier to their clinical implementation is the lack of high-quality, prospectively collected datasets for validation. Many existing AI algorithms have been trained on retrospective data, which may not capture the variability and complexity of real-world clinical scenarios. This limitation raises concerns about the generalisability and reliability of AI predictions across diverse patient populations.

Therefore, there is a critical need for prospective validation studies to assess the performance of AI algorithms in realworld settings, ensuring their accuracy and applicability before widespread clinical deployment. Imperial College London's Health Impact Lab (Hi Lab) and collaborators continue to develop artificial intelligence (AI) algorithms that use cardiac waveforms to predict cardiovascular disease (CVD), including pulmonary hypertension (PH). The performance of these algorithms requires validation on prospectively collected patient data (waveforms) - where the ground truth for the algorithms under investigation is recorded during routine echocardiography as part of clinical care. This study aims to prospectively collect a large dataset of cardiovascular ECG and PCG data, along with corresponding gold-standard echocardiography findings. This dataset will be used to validate AI algorithms for important CVD, such as pulmonary hypertension enhancing their reliability and clinical applicability.

Eligibility criteria:
Inclusion Criteria:

* Age 18 years or above
* Able to give informed consent
* Attending for echocardiography at Imperial College NHS Trust

Exclusion Criteria:

• Any chest wound, skin pathology or other feature that would prohibit routine stethoscope examination

Outcomes:
Performance characteristics for developed algorithms
Detection of Systemic Diseases Such as Hepatobiliary Diseases From Ocular Images Via Deep Learning
Trial ID: NCT07581925
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2020-04-10
Completion date: 2024-07-30
Sponsor: Zhongshan Ophthalmic Center, Sun Yat-sen University
Collaborators: Third Affiliated Hospital, Sun Yat-Sen University; Aikang Health Care
Conditions: Ophthalmology; Artificial Intelligence (AI); Hepatobiliary Diseases; Systemic Diseases
Interventions: Systemic diseases such as Hepatobiliary Disorders
Locations: Zhongshan Ophthalmic Center, Sun Yat-sen Univerisity
Countries: China
Enrollment: 775
Age range: 18 Years - None
Sex: ALL

Summary:
Oculomics is an emerging interdisciplinary field that deciphers multi-dimensional, high-throughput ocular data to predict, diagnose, and monitor systemic diseases and health span.In recent years, artificial Intelligence may provide insight into exploring the potential covert association behind and reveal some early ocular architecture changes in individuals with systemic diseases. The investigators conducted a survey to explore the association between the eye and systemic diseases via deep learning, to develop and evaluate different deep learning models to predict the systemic diseases such as hepatobiliary disease by using ocular images.

Eligibility criteria:
Inclusion Criteria:

* The quality of ocular images should clinical acceptable.
* Complete clinical information such as baseline demographic characteristics, the history of systematic diseases and so on.

Exclusion Criteria:

* Individuals diagnosed with severe eye diseases or acute systematic diseases.
* Incompatible with ocular examinations.

Outcomes:
area under the receiver operating characteristic curve of the deep learning system
ROMIAE 2 Trial: Randomized Controlled Trial for Managing Suspicious Acute Myocardial Infarction in ED Using AI-ECG
Trial ID: NCT07027891
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-07-01
Completion date: 2028-02-28
Sponsor: CHA University
Conditions: Myocardial Infarction (MI); Chest Pain Rule Out Myocardial Infarction
Interventions: AI-ECG
Locations: Asan Medical Center; CHA Bundang Medical Center; Ewha Womans University; Gangnam Severance Hospital; Hanyang University; Jeonbuk National University Hospital; Korea University Ansan Hospital; Korea University Guro Hospital; Kyung Hee University Hospital; Samsung Medical Center; Soonchunhyang University Bucheon Hospital; Yonsei University Wonju College of Medicine
Countries: South Korea; South Korea; South Korea; South Korea; South Korea; South Korea; South Korea; South Korea; South Korea; South Korea; South Korea; South Korea
Enrollment: 4670
Age range: 18 Years - None
Sex: ALL

Summary:
This study is to see whether the AI ECG assisted protocol is as safe and efficacious as conventional protocol in early triage of suspected myocardial infarction.

Eligibility criteria:
Inclusion Criteria:

* chest pain
* suspicious of acute myocardial infarction

Exclusion Criteria:

* STEMI
* Revisit of same symptoms within 1 week
* traumatic chest pain
* Pneumothorax
* Transferred from other hospital diagnosed of AMI
* Cardiac arrest
* Chest pain of clearly non-cardiac etiology
* declined to participate in the study

Outcomes:
30 Days MACE(Major adverse cardiac events)
Diabetes Prevention Combining CGM and Artificial Intelligence Health Education
Trial ID: NCT06472297
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-06-01
Completion date: 2024-09-30
Sponsor: University of Southern California
Collaborators: National Institute on Minority Health and Health Disparities (NIMHD)
Conditions: Pre-diabetes
Interventions: The continuous glucose monitoring system
Locations: Southern California Center for Latino Health SCCLH
Countries: United States
Enrollment: 23
Age range: 18 Years - None
Sex: ALL

Summary:
The objective of this project is to develop a behavioral intervention that combines wearable continuous glucose monitoring (CGM) with smartphone feedback and educational video clips generated by artificial intelligence (AI) software to improve glycemic control among individuals with pre-diabetes. The goal is to prevent transition to type 2 diabetes.

Eligibility criteria:
Inclusion Criteria:

* Prediabetes by finger prick blood A1C%
* Centers for Disease Control and Prevention (CDC) prediabetes risk test score of 5 or higher
* Willingness to wear CGM sensor
* Latino community health worker

Exclusion Criteria:

* Currently pregnant
* Less than 18 years of age, which is adult in California
* Diagnosed with any disorder that interferes with glucose
* Influential medical disorder/event affecting ability to participate in study
* Incompatible smartphone device not pairing with Dexcom G6 app

Outcomes:
Change in Mean Glucose (mg/dL) From Baseline