moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Language Translation of Knowledge Mobilization Resources: A Randomized Trial
Trial ID: NCT07127887
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-07-28
Completion date: 2027-01-31
Sponsor: University of Alberta
Conditions: Communication Research
Interventions: KM Resource - AI; KM Resource - Professional
Locations: University of Alberta
Countries: Canada
Enrollment: 350
Age range: 18 Years - None
Sex: ALL

Summary:
Knowledge mobilization (KM) resources are tools designed to facilitate the use of research evidence in healthcare decision-making. These resources are created in various formats - including plain language summaries, infographics, and videos - to meet the needs of diverse end-users, such as healthcare professionals, policymakers, patients, and caregivers. They are intended to be easily accessible; however, individuals whose first language is not English may have difficulty understanding them. Thus, translating KM resources into other languages is essential to support health equity and accessibility, but it is often costly and time intensive.

This study aims to explore whether artificial intelligence (AI) tools, specifically ChatGPT - an AI-based large language model developed by OpenAI - can effectively translate KM resources for members of the public whose first language is not English. The resource being evaluated offers guidance on preventing post-COVID-19 condition and has already been translated by a professional (human) translator into seven languages commonly spoken in Canada: French, Spanish, Ukrainian, Tagalog, Arabic, Chinese, and Punjabi. Using ChatGPT, AI-generated translations will be created in those same seven languages.

For this study, participants - adults living in Canada whose first language is one of the selected languages and able to read English - will be randomly assigned to review either an AI-generated or a professionally translated version of a KM resource. They will then complete a questionnaire evaluating their understanding of the resource, as well as the readability and acceptability of the translation.

This study will contribute to the Investigators' understanding of the potential use of AI for translating health information. The goal is to support equitable access to health information and promote citizen-centered care by reducing language barriers using innovative solutions.

Eligibility criteria:
Inclusion Criteria:

* • 18 years of age or older

  * Living in Canada
  * First language is one of the selected languages (i.e. French, Spanish, Ukrainian, Tagalog, Arabic, Chinese, and Punjabi)
  * Able to read and complete a questionnaire in English
  * Access to an electronic device (e.g. computer or tablet), Internet and email

Exclusion Criteria:

* • Under the age of 18 years

  * Does not live in Canada
  * First language is not one of the selected languages (i.e. French, Spanish, Ukrainian, Tagalog, Arabic, Chinese, and Punjabi)
  * Unable to read and complete a questionnaire in English
  * No access to an electronic device (e.g. computer or tablet), Internet or email

Outcomes:
Understanding
The AID Study 2: Artificial Intelligence for Colorectal Adenoma Detection 2
Trial ID: NCT04260321
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2020-02-19
Completion date: 2020-12-31
Sponsor: Istituto Clinico Humanitas
Conditions: Colon Cancer
Interventions: AI
Locations: Fondazione Poliambulanza; Endoscopy Unit, Humanitas Research Hospital; Ospedale Valduce; Digestive Endoscopy Unit, Nuovo Regina Margherita Hospital; Ente Ospedaliero Cantonale, Ospedale Italiano
Countries: Italy; Italy; Italy; Italy; Switzerland
Enrollment: 700
Age range: 40 Years - 80 Years
Sex: ALL

Summary:
Colonoscopy is clinically used as the gold standard for detection of colon cancer (CRC) and removal of adenomatous polyps. Despite the success of colonoscopy in reducing cancer-related deaths, there exists a disappointing level of adenomas missed at colonoscopy. "Back-to-back" colonoscopies have indicated significant miss rates of 27% for small adenomas (\< 5 mm) and 6% for adenomas of more than 10 mm in diameter. Studies performing both CT colonography and colonoscopy estimate that the colonoscopy miss rate for polyps over 10 mm in size may be as high as 12%. The clinical importance of missed lesions should be emphasized because these lesions may ultimately progress to CRC.

Limitations in human visual perception and other human biases such as fatigue, distraction, level of alertness during examination increases such recognition errors and way of mitigating them may be the key to improve polyp detection and further reduction in mortality from CRC. In the past years, a number of CAD systems for detection of polyps from endoscopy images have been described. However, the benefits of traditional CAD technologies in colonoscopy appear to be contradictory, therefore they should be improved to be ultimately considered useful. Recent advances in artificial intelligence (AI), deep learning (DL), and computer vision have shown potential to assist polyp detection during colonoscopy.

Average experienced endoscopists (each having performed \<2000 screening colonoscopies) will perform the endoscopic procedure.

Eligibility criteria:
Inclusion Criteria:

* All 40-80 years-old subjects undergoing a colonoscopy

Exclusion Criteria:

* subjects with personal history of CRC, or IBD.
* patients with inadequate bowel preparation (defined as Boston Bowel Preparation Scale \> 2 in any colonic segment).
* patients with previous colonic resection.
* patients on antithrombotic therapy, precluding polyp resection.
* patients who were not able or refused to give informed written consent.

Outcomes:
Non-inferiority of AI-aided colonoscopy in terms of ADR
The Cohort Study of Pediatric Cataract
Trial ID: NCT05472844
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2022-08-22
Completion date: 2032-02-01
Sponsor: Zhongshan Ophthalmic Center, Sun Yat-sen University
Conditions: Pediatric Cataract
Locations: Zhongshan Ophthalmic Center
Countries: China
Enrollment: 5000
Age range: None - 18 Years
Sex: ALL

Summary:
This retrospective-prospective bidirectional cohort study aims to observe ocular parameter changes, different surgical/vision recovery strategies and postoperative adverse effects among young children with cataract. The influence on psychology and cognitive function will be taken into consideration as well.

Eligibility criteria:
Inclusion Criteria:

* aged \< 18 yo at the 1st visit unilateral or bilateral cataract agree to participant in this study and sign the informed consent

Exclusion Criteria:

* adults

Outcomes:
Best-corrected visual acuity changes; Intraocular pressure changes; Refractive power changes; Ocular biological measurements changes; Incidence changes of postoperative complications
Artificial Intelligence-assisted CT for Risk Stratification in COronary Artery Disease to PreveNt Future Coronary Events and Improve Outcomes
Trial ID: NCT07577609
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2022-12-21
Completion date: 2032-12-21
Sponsor: University of Galway
Collaborators: Science Foundation Ireland
Conditions: Coronary Artery Disease (CAD); Computed Tomography Angiography; Biomarkers; AI (Artificial Intelligence); Lipoprotein(a); Atheroscleroses, Coronary; Myocardial Ischemia
Locations: Clinical Research Facility
Countries: Ireland
Enrollment: 10000
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this observational study is to learn if AI-assisted cardiac CT imaging can improve cardiovascular risk stratification and prediction of future coronary events in an adult population undergoing clinically indicated cardiac CT.

The main questions it aims to answer are:

* Can AI-enhanced cardiac CT accurately assess cardiovascular risk in a real-world adult population?
* How do CT-derived plaque characteristics correlate with clinical, biochemical, and lifestyle risk factors? Researchers will compare subgroups (e.g., patients with different risk profiles, biomarkers, or imaging findings, and a subset undergoing OCT imaging) to see if differences in imaging and clinical parameters are associated with cardiovascular risk and plaque vulnerability.

Participants will:

* Provide informed consent and medical history/demographic information
* Undergo blood sampling for cardiovascular and metabolic biomarkers
* Have a resting ECG performed
* Complete a detailed lifestyle and health questionnaire
* Receive a non-invasive cardiac CT scan interpreted by an expert
* Potentially receive heart rate-lowering medication (e.g., metoprolol) if required for imaging quality
* Be referred for further clinical evaluation if clinically indicated ￼

Eligibility criteria:
Inclusion Criteria:

* Adults aged ≥18 years
* Undergoing clinically indicated cardiac CT
* Able and willing to provide informed consent

Exclusion Criteria:

* History of malignancy
* Abnormal renal function (e.g., eGFR or serum creatinine outside reference range)
* Contraindications to iodinated contrast agents or CT imaging

Outcomes:
Cardiovascular risk stratification using AI-assisted cardiac CT
An Artificial Intelligence System for Multimodal, Multi-class Diagnosing Solid Pancreatic Lesions Based on Endoscopic Ultrasound
Trial ID: NCT07381192
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-09-01
Completion date: 2028-06-30
Sponsor: Qilu Hospital of Shandong University
Collaborators: Liaocheng People's Hospital; Taian City Central Hospital; Qilu Hospital of Shandong University (Qingdao); Binzhou People's Hospital; Shandong Provincial Hospital; The Affiliated Hospital of Qingdao University; Qianfoshan Hospital; Shengli Oilfield Hospital; Binzhou Medical University
Conditions: Endoscopic Ultrasound (EUS); Solid Pancreatic Lesion
Interventions: iEUS-SPL(intelligent endoscopic ultrasound system-pancreatic solid lesion)
Locations: Qilu Hospital of Shandong University
Countries: China
Enrollment: 383
Age range: 18 Years - None
Sex: ALL

Summary:
The aim of this study is to validate an artificial intelligence system named iEUS-SPL(intelligent endoscopic ultrasound system-solid pancreatic lesion) for detecting and multimodal, multi-class diagnosing solid pancreatic lesions during endoscopic ultrasound(EUS) examination.

Eligibility criteria:
Inclusion Criteria:

1. Patients aged ≥18 years scheduled for EUS with suspected solid pancreatic lesions based on clinical symptoms, medical history, laboratory tests or radiological examinations agree to participate in the research and be able to sign informed consent.
2. Patients with no prior history of treatment for pancreatic lesions.

Exclusion Criteria:

1. Patients with absolute contraindications to EUS examination.
2. Pregnancy or lactating.
3. Uncorrectable coagulopathy(PTT\>50 seconds or INR\>1.5) and/or uncorrectable thrombocytopenia(platelet count\<50×109/L).
4. Upper gastrointestinal obstruction.
5. Patients who underwent surgical treatment or anatomical alterations of the pancreas due to lesions in other thoracic and/or abdominal organs, as well as patients with congenital anatomical abnormalities.
6. Patients who have undergone biliary/pancreatic duct stent placement.
7. Patients who refuse to sign the informed consent.

Outcomes:
The accuracy of iEUS-SPL for solid pancreatic lesions; The sensitivity of iEUS-SPL for solid pancreatic lesions; The specificicy of iEUS-SPL for solid pancreatic lesions; The postive predictive value of iEUS-SPL for solid pancreatic lesions; The negative predictive value of iEUS-SPL for solid pancreatic lesions; the lesion detection rate of iEUS-SPL for detecting solid pancreatic lesions
Automatic Real-time Diagnosis of Gastric Mucosal Disease Using Probe-based Confocal Laser Endomicroscopy With Artificial Intelligence
Trial ID: NCT03784209
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2018-07-01
Completion date: 2021-09-29
Sponsor: Shandong University
Conditions: Gastric Diseases; Artificial Intelligence; Confocal Laser Endomicroscopy
Interventions: The diagnosis of Artificial Intelligence and endoscopist
Locations: Endoscopic unit of Qilu Hospital Shandong University
Countries: China
Enrollment: 951
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
Probe-based confocal laser endomicroscopy (pCLE) is an endoscopic technique that enables real-time histological evaluation of gastric mucosal disease during ongoing endoscopy examination. However this requires much experience, which limits the application of pCLE. The investigators designed a computer-aided diagnosis program using deep neural network to make diagnosis automatically in pCLE examination and contrast its performance with endoscopists.

Eligibility criteria:
Inclusion Criteria:

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

Exclusion Criteria:

* Patients under conditions unsuitable for performing CLE including coagulopathy , impaired renal or hepatic function, pregnancy or breastfeeding, and known allergy to fluorescein sodium;
* Inability to provide informed consent

Outcomes:
The diagnosis efficiency of Artificial Intelligence
Lung Nodule Imaging Biobank for Radiomics and AI Research
Trial ID: NCT04270799
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2020-06-01
Completion date: 2021-08
Sponsor: Royal Marsden NHS Foundation Trust
Collaborators: RM Partners West London Cancer Alliance; Royal Brompton & Harefield NHS Foundation Trust; University College London Hospitals; Imperial College Healthcare NHS Trust; Lewisham and Greenwich NHS Trust; King's College Hospital NHS Trust; Epsom and St Helier University Hospitals NHS Trust; Institute of Cancer Research, United Kingdom; Guy's and St Thomas' NHS Foundation Trust; UCLH Biomedical Research Centre
Conditions: Lung Cancer; Pulmonary Nodule, Multiple; Pulmonary Nodule, Solitary; Lung Neoplasms
Interventions: Machine Learning Classification
Locations: Royal Marsden - Surrey; Lewisham and Greenwich NHS Trust; Epsom and St Helier's Hospitals NHS Trust; University College London Hospitals NHS Foundation Trust; The Royal Brompton NHS Foundation Trust
Countries: United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom
Enrollment: 1000
Age range: 18 Years - None
Sex: ALL

Summary:
This study will collect retrospective CT scan images and clinical data from participants with incidental lung nodules seen in hospitals across London. The investigators will research whether machine learning can be used to predict which participants will develop lung cancer, to improve early diagnosis.

Eligibility criteria:
Inclusion Criteria:

* Age \> 18
* Baseline CT thorax imaging reported as having pulmonary nodule(s) between 5 and 30mm in the last 10 years.
* Ground truth known (either scan data showing stability for 2 years (based on diameter) or one year (based on volumetry), complete resolution, or biopsy-proven malignancy.
* Slice thickness \< 2.5mm.

Exclusion Criteria:

* • Absence of at least one technically adequate CT thorax imaging series (defined by visual inspection of presence of imaging data of the thorax in the DICOM record).

  * Slice thickness \> 2.5mm.
  * Imaging \> 10 years old.
  * Ground truth unknown.

Outcomes:
Development of an imaging biobank
Evaluation of Artificial Intelligence in Defining Lung Ultrasound Findings in Anesthesia Management of Preeclampsia
Trial ID: NCT05487014
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2021-07-01
Completion date: 2022-07-26
Sponsor: Gazi University
Conditions: Preeclampsia; Lung Edema
Locations: Selin Bağcaz
Countries: Turkey (Türkiye)
Enrollment: 35
Age range: 18 Years - 45 Years
Sex: FEMALE

Summary:
A total of eight quadrants of standard lung US examination was performed to all pregnant women with preeclampsia within the scope of the study by the same anesthesiologist, dividing each hemithorax into four regions through the parasternal, anterior axillary, posterior axillary vertical lines, and the horizontal line assumed to pass under the nipple. The resulting images were stored in digital media. A Lung US examination was performed once for an average of 5-10 minutes. The presence of B lines was investigated in the examination.

The case was defined as interstitial edema when the B lines, which are defined as vertical linear hyperechoic reverberation artifacts representing the edematous interlobular septa/alveoli, extend posteriorly from below the pleural line and moving in sync with lung movements, are found in two or more lung areas. B-lines were determined for each case and reported in standard form in terms of number and morphology. The diagnostic accuracy of B lines was determined with the artificial intelligence supported SmartAlpha Rievi 1300 software program. B-lines validated by artificial intelligence assisted algorithm in all stored digital images were reported blindly by another anesthesiologist experienced in lung US. The clinical features, laboratory parameters, and intraoperative hemodynamic data of the cases were recorded to be evaluated in terms of relationship with lung US data.

We predict that the application of lung US with artificial intelligence software will provide an opportunity to quickly evaluate the clinic of preeclamptic pregnant women who are frequently operated on in emergency conditions.

Eligibility criteria:
Inclusion Criteria:

* Pregnant women who underwent cesarean section with a diagnosis of preeclampsia

Exclusion Criteria:

* Preeclamptic parturients who have another lung disease
* Preeclamptic parturients whose optimal lung US image could not be obtained

Outcomes:
For comparison, artificial intelligence assisted ultrasound and standard ultrasound in the follow-up of 35 preeclamptic parturients will be performed.
Real-Time Identification of Gastric Lesions and Anatomical Landmarks During Magnetically Controlled Capsule Endoscopy Using Artificial Intelligence
Trial ID: NCT04203264
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2019-12-16
Completion date: 2020-01-01
Sponsor: Changhai Hospital
Conditions: Capsule Endoscopy
Interventions: Real-time artificial intelligence identification system
Locations: Changhai Hospital
Countries: China
Enrollment: 50
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
The aim of this study is to evaluate the performance of real-time auxiliary system based on artificial intelligence algorithm in lesion detection and quality control in magnetically controlled capsule endoscopy.

Eligibility criteria:
Inclusion Criteria:

* With or without gastrointestinal complaints
* Scheduled to undergo a capsule endoscopy for both stomach and small bowel
* Signed the informed consents before joining this study

Exclusion Criteria:

* Dysphagia or symptoms of gastric outlet obstruction, suspected or known intestinal stenosis, overt gastrointestinal bleeding, history of upper gastrointestinal surgery or abdominal surgery altering gastrointestinal anatomy
* Refused abdominal surgery to take out the capsule in case of capsule retention
* Implanted pacemaker, except the pacemaker is compatible with MRI
* Other implanted electromedical devices or magnetic metal foreign bodies
* Pregnancy or suspected pregnancy

Outcomes:
Sensitivity
"Pilot Randomized Prospective Clinical Study of the Effectiveness of the Use of Artificial Intelligence in Determining the "Safe" Clamping Zones in the Surgical Treatment of Abdominal Aortic Aneurysms
Trial ID: NCT05643664
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-01-01
Completion date: 2024-12-31
Sponsor: Meshalkin Research Institute of Pathology of Circulation
Conditions: Frequency of Embolic Complications; Frequency of Ischemic Complications
Interventions: prosthetics of the abdominal aorta; prosthetics of the abdominal aorta after determining the safe zones of clamping
Locations: Alexander A Gostev; Federal State Institution Academician E.N.Meshalkin Novosibirsk State Research Institute Of Circulation Pathology Rusmedtechnology; Novosibirsk Research Institute of Circulation Pathology; E. Meshalkin National Medical Research Center
Countries: Russia; Russia; Russia; Russia
Enrollment: 100
Age range: 45 Years - 75 Years
Sex: ALL

Summary:
"Pilot randomized prospective clinical study of the effectiveness of the use of artificial intelligence in determining "safe" clamping zones in the surgical treatment of abdominal aortic aneurysms."

Eligibility criteria:
Inclusion Criteria:

* Patients with aneurysmal dilation of the abdominal aorta, who are shown surgery.
* Patients who have agreed to participate in this study

Exclusion Criteria:

* Chronic heart failure of functional class III -IV according to NYHA classification;
* Chronic decompensated "pulmonary" heart;
* Severe hepatic or renal insufficiency (bilirubin \>35 mmol/l, glomerular filtration rate \<60 ml/min);
* Polyvalent drug allergy;
* Malignant oncological diseases in the terminal stage with a predicted life span of up to 6 months;
* Acute cerebrovascular accident;

Outcomes:
frequency of intraoperative and early postoperative embolism
Design and Fabrication of an Intelligent Robotic Hand for Grasping Various Objects Using Artificial Intelligence
Trial ID: NCT07437664
Status: ACTIVE_NOT_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-02-12
Completion date: 2026-09-01
Sponsor: Al-Nahrain University
Conditions: Transradial Amputation; Upper Limb Amputation Below Elbow; Upper Limb Amputation
Interventions: AI-Powered Robotic Hand
Locations: Al-Nahrain University, College of Engineering
Countries: Iraq
Enrollment: 1
Age range: 18 Years - 60 Years
Sex: MALE

Summary:
The purpose of this research is to create an intelligent robotic hand for people who have lost a limb below their elbow. By using artificial intelligence to adaptively grasp different types of objects, this will improve both the accuracy and flexibility of robotic prosthetic control. In addition, the project will integrate mechanical design and artificial intelligence based controls in order to produce a more functional and user-friendly prosthetic solution.

Eligibility criteria:
Inclusion Criteria:

* Participants with unilateral transradial amputation.
* Age between 18 and 60 years.
* Sufficient muscle activity in the residual limb to generate detectable EMG signals.
* Stable physical and mental health condition to undergo the testing.

Exclusion Criteria:

* History of severe skin diseases or open wounds at the site of EMG electrode placement.
* Cognitive impairments that prevent the participant from understanding or following instructions.
* Participation in other clinical trials that might interfere with the current study outcomes.

Outcomes:
Feasibility and Technical Performance of the AI-driven Prosthetic System.