moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Exploring the Application Efficacy of Artificial Intelligence (AI) Diagnostic Tools in Medical Imaging (MI) of Respiratory(R) Infectious (I) Disease (D)
Trial ID: NCT06553911
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-04-01
Completion date: 2026-12-01
Sponsor: Huashan Hospital
Conditions: Respiratory Infectious Diseases; Artificial Intelligence; Medical Imaging
Interventions: Artificial Intelligence-based medical imaging interpretation
Locations: Huashan Hospital
Countries: China
Enrollment: 2000
Age range: 1 Year - 90 Years
Sex: ALL

Summary:
The early identification and severe warning of acute respiratory infectious diseases are of paramount importance. Utilizing effective means to make correct diagnoses of the source of infection at an early stage is the premise of all effective measures. AI-MID is a research initiative that uses artificial intelligence tools to assist in the clinical medical imaging diagnosis of respiratory diseases, aiming to reduce the time doctors spend reviewing images, increase work efficiency, and enhance the sensitivity and specificity of pneumonia detection, thereby improving the detection rate of pneumonia at the grassroots level. This approach facilitates precise prevention, accurate diagnosis, and precise treatment.

Eligibility criteria:
Inclusion Criteria:

1. 1-90 years old, gender not specified.
2. Exhibits symptoms of respiratory tract infection
3. Must have etiological examination results
4. Must have imaging data;

Exclusion Criteria:

1. Severe artifacts in medical images
2. Clinical diagnosis indicates concurrent pulmonary edema
3. Dual review results in unclear diagnosis or potential misdiagnosis
4. Other situations that may cause difficulties in reading the films, or as determined by the researcher, the study participant is deemed unsuitable for enrollment.

Outcomes:
Evaluating the Diagnostic Efficacy of Artificial Intelligence Diagnostic Tools in Medical Imaging of Respiratory Infectious Diseases
Artificial Intelligence in Endoscopic Diagnosis of Colorectal Polyps: A Prospective Randomized Study.
Trial ID: NCT06786793
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-11-01
Completion date: 2025-12-31
Sponsor: Jagiellonian University
Conditions: Quality Indicators, Health Care; Artificial Intelligence (AI); Colonoscopy Diagnostic Techniques and Procedures
Interventions: Computer-aided detection (CADe)
Locations: MEDICINA Medical Center; Brothers Hospitallers Medical Center, Hospital of St John of god in Krakow
Countries: Poland; Poland
Enrollment: 630
Age range: 50 Years - 65 Years
Sex: ALL

Summary:
Colorectal cancer is the second most common malignancy in the countries of the European Union. Colonoscopy is the primary method for detecting and preventing the development of colorectal cancer is endoscopic examination. This study aims to evaluate the impact of artificial intelligence on the detection rate of polyps and early stages of colorectal cancer.

Eligibility criteria:
Inclusion Criteria:

* Consent to participate in the study,
* Age between 50 and 65 years,
* Scheduled outpatient colonoscopy.

Exclusion Criteria:

* Previous colonoscopy,
* History of colorectal surgery,
* Ongoing biological therapy for any indication,
* Primary sclerosing cholangitis,
* Familial polyposis syndrome,
* Chronic diarrhea,
* Ulcerative colitis,
* Crohn's disease.

Outcomes:
Adenoma detection rate (ADR)
The Relationship Between Hormone Sensitivity and Imaging of Idiopathic Interstitial Pneumonia by Artificial Intelligence
Trial ID: NCT05146934
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2019-12-30
Completion date: 2021-06-30
Sponsor: Peking University Third Hospital
Conditions: Idiopathic Interstitial Pneumonia
Interventions: high resolution CT
Locations: Peking University Third Hospital
Countries: China
Enrollment: 150
Age range: 18 Years - 90 Years
Sex: ALL

Summary:
Application of artificial intelligence deep learning algorithm to analyze the relationship between hormone sensitivity of idiopathic interstitial pneumonia and imaging features of high resolution CT.

Eligibility criteria:
Inclusion Criteria:

Clinical-pathological-radiology diagnosis of idiopathic interstitial pneumonia Hormone therapy was used; The follow-up data were complete, and the effect of hormone use could be judged.

Exclusion Criteria:

Lung infection disease; Heart failure; Connective tissue disease; IIP Without hormone therapy ; IIP but the follow-up data were incomplete, and the effect of hormone use could not be judged.

Outcomes:
clinical data and imaging feature ratios in both groups
Accuracy of Artificial Intelligence Technology in Detecting the Quality of Endodontic Treatment Diagnostic Accuracy Experimental Study.
Trial ID: NCT07056998
Status: ENROLLING_BY_INVITATION
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-04-01
Completion date: 2025-10-30
Sponsor: Tala Ghassan Hassan Odeh
Conditions: Root Canal Treatment
Interventions: root canal retreatment
Locations: Faculty of Oral and Dental Medicine future university
Countries: Egypt
Enrollment: 3
Age range: 18 Years - 50 Years
Sex: ALL

Summary:
aims to evaluate the accuracy of clinicians (conventional radiograph) compared to cone beam computed tomography (CBCT) and artificial intelligence software's ability to identify the quality of root canal obturation. Will the use of artificial intelligence software be compared to conventional software in accuracy?

Eligibility criteria:
Inclusion Criteria:

1. Medically free patients with no systemic disease: (American Society of Anesthesiologists / (ASA Class I or II).
2. Age range is between18 to 50 years
3. No sex predilection.
4. All patients must have good oral hygiene. 5 -patients with root canal-treated teeth.

6- restorable teeth. 7- Positive patient acceptance for participating in the study. 8- Patients can sign informed consent.

Exclusion Criteria:

* 1-Patients with very poor oral hygiene. 2-Patients above 50 years and below 18 years. 3-Pregnant and lactating women 4-Psychologically disturbed patients. 5-nonrestorable teeth.

Outcomes:
root canal quality Length of Root Filling
Development and Validation of a Deep Learning System for Multiple Ocular Fundus Diseases Using Retinal Images: a Multi-center Prospective Study
Trial ID: NCT04213430
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2014-01
Completion date: 2020-05
Sponsor: Sun Yat-sen University
Conditions: Ophthalmological Disorder
Interventions: diagnostic
Locations: Zhongshan Ophthalmic Center, Sun Yat-sen Univerisity
Countries: China
Enrollment: 300000
Age range: None - None
Sex: ALL

Summary:
Retinal images can reflect both fundus and systemic conditions (diabetes and cardiovascular disease) and firstly to be used for medical artificial intelligence (AI) algorithm training due to its advantages of clinical significance and easy to obtain. Here, the investigators developed a single network model that can mine the characteristics among multiple fundus diseases, which was trained by plenty of fundus images with one or several disease labels (if they have) in each of them. The model performance was compared with those of both native and international ophthalmologists. The model was further tested by datasets with different camera types and validated by three external datasets prospectively collected from the clinical sites where the model would be applied.

Eligibility criteria:
Inclusion Criteria:

* The quality of fundus images should clinical acceptable. More than 80% of the fundus image area including four main regions (optic disk, macular, upper and lower retinal vessel archs) are easy to read and discriminate.

Exclusion Criteria:

* Images with light leakage (\>30% of area), spots from lens flares or stains, and overexposure were excluded from further analysis.

Outcomes:
Area under the receiver operating characteristic curve of the deep learning system
Collecting Orthopedic Safety and Performance Data Using Surgeons Clinical Outcomes Registry (SCOR)
Trial ID: NCT04032444
Status: TERMINATED
Study type: OBSERVATIONAL
Start date: 2019-07-30
Completion date: 2022-01-21
Sponsor: Stryker Trauma and Extremities
Conditions: Orthopedics
Locations: Orthopedic Surgeon; Orthopedic Surgeon; Orthopedic Surgeon; Orthopedic Surgeon; Orthopedic Surgeon; Orthopedic Surgeon; Orthopedic Surgeon
Countries: United States; United States; United States; United States; United States; United States; United States
Enrollment: 723
Age range: 18 Years - None
Sex: ALL

Summary:
The Surgeons Clinical Outcomes Registry (SCOR) is a global registry designed to collect safety and performance data on commercially available Wright Medical products. Registry data may be used for publications, education and marketing materials, post-market surveillance, future regulatory submissions, research and development for product design, software improvements, and development of artificial intelligence learning and tools.

Eligibility criteria:
Inclusion criteria:

• Patients 18 years of age or older that have or will be receiving a Wright Medical product

Exclusion criteria:

• Member of a vulnerable population, including minors, those unable to decide for themselves to participate or needing a Legally Authorized Representative (LAR), or others who could be subject to coercion

Outcomes:
Number of device associated and procedure associated adverse events.
Deployment and Evaluation of Artificial Intelligence Software for Electrocardiogram Analysis and Management in Primary Care
Trial ID: NCT06637293
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-10-06
Completion date: 2027-03
Sponsor: Montreal Heart Institute
Conditions: Primary Care Provider; Structural Heart Disease
Interventions: DeepECG plateform diagnosis & recommendations
Locations: Montreal Heart Institute
Countries: Canada
Enrollment: 2000
Age range: 18 Years - None
Sex: ALL

Summary:
The DAISEA-ECG project aims to improve the diagnosis of heart diseases in primary care through the DeepECG platform, which combines ECG-AI and ECHONeXT algorithms. This study uses a stepped wedge design, where each Family Medicine Group acts as its own control. The FMGs will gradually transition from the control period (without AI recommendations) to the intervention period (with AI recommendations activated) in a randomized sequence.

The primary objective is to compare the sensitivity of family physicians in detecting cardiac pathologies, with and without the assistance of the DeepECG platform. Sensitivity is defined as the proportion of patients correctly referred to cardiology or for transthoracic echocardiography (TTE) among those who indeed required cardiovascular evaluation, as confirmed by an independent adjudication committee.

Eligibility criteria:
Inclusion Criteria:

Family Physicians or Nurse Practitioners

Family physicians or nurse practitioners (NPs) practicing in one of the participating FMGs.

Family physicians who have given their free and informed consent. Patients

Adult patients (18 years or older). Patients without follow-up in cardiology or internal medicine for cardiovascular issues (arrhythmia, heart failure, myocardial infarction, atherosclerotic coronary artery disease, valvular heart disease) or those who had a negative investigation in the past with no additional follow-up.

ECG

Any 12-lead ECG performed with the MUSE GE 360 machine. ECG of adequate technical quality for interpretation (otherwise, it will be automatically rejected by the platform).

\-

Exclusion Criteria:

* Family Physicians or Nurse Practitioners

Family physicians practicing exclusively in pediatrics (patients under 18 years old).

Family physicians unable to follow the project guidelines.

Outcomes:
sensitivity of cardiology referrals
LOCATOR - Locally Optimised Contouring With AI Technology for Radiotherapy
Trial ID: NCT06546592
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-02-11
Completion date: 2030-04-30
Sponsor: Royal North Shore Hospital
Conditions: Contouring; Segmentation; Radiation Therapy; Artificial Intelligence; Deep Learning
Interventions: AI assisted contouring
Locations: Western Cancer Centre Dubbo; Central West Cancer Centre; Department of Radiation Oncology, Royal North Shore Hospital
Countries: Australia; Australia; Australia
Enrollment: 444
Age range: 18 Years - None
Sex: ALL

Summary:
LOCATOR is a multicentre phase II randomised clinical trial that is looking at the process of contouring in radiation treatment for breast cancer patients. This study looks at whether contouring aided by artificial intelligence (AI) is comparable in quality to that of contouring done completely manually by a radiation oncologist. We are also looking at whether AI assisted contouring saves radiation oncologists time when compared to fully manual contouring.

LOCATOR uses the LOCATOR software which is an in-house software developed locally and trained on local data.

Eligibility criteria:
Inclusion Criteria:

* 18 years and older who are planned for primary breast malignancy
* ECOG performance 0-2
* Ability to understand and willingness to sign a written informed consent document
* The target volume must be able to be objectively reviewed by current published national or international clinical guidelines

Exclusion Criteria:

* Patients under 18 years of age
* Patients unable to understand consent documents

Outcomes:
Assessment of differences in Contour Quality
Artificial Intelligence as a Decision Making Tool in Emergency Medicine
Trial ID: NCT06902675
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2000-01-01
Completion date: 2026-09-01
Sponsor: Rambam Health Care Campus
Collaborators: Technion, Israel Institute of Technology
Conditions: Clinical Decision-making; Medical Reporting; Emergency Department Visit; Information Systems; Electronic Health Records; Artificial Intelligence in Medicine
Locations: Rambam healthcare campus
Countries: Israel
Enrollment: 20000
Age range: 18 Years - 120 Years
Sex: ALL

Summary:
This study will evaluate the performance of a large language model (LLM)-based clinical decision support system in the emergency department at Rambam Health Care Campus. The system analyzes structured patient data from the electronic health record and generates diagnostic and treatment recommendations for physicians.

The study will assess the system's ability to support diagnostic reasoning, its impact on diagnostic accuracy when used by physicians, and its perceived clinical usefulness. In addition, a retrospective analysis of de-identified patient records will be conducted to compare LLM-generated recommendations with actual clinical outcomes, including diagnosis, disposition decisions, and length of stay.

The study will also examine the performance of the system in a multilingual clinical environment where both Hebrew and English are used in medical documentation and communication.

Eligibility criteria:
Inclusion Criteria:

Adults ≥ 18 presented to the ER

Exclusion Criteria:

None

Outcomes:
Length of Stay in Emergency Department
Agreement Between Multidisciplinary Cancer Board Decisions and ChatGPT-4O in Clinical Decision-Making: A Cross-Sectional Observational Study
Trial ID: NCT06986564
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-11-01
Completion date: 2025-05-01
Sponsor: Sebahattin Celik MD
Conditions: Cancer; Artificial Intelligence
Locations: Van state hospital, general surgery department
Countries: Turkey (Türkiye)
Enrollment: 100
Age range: 18 Years - None
Sex: ALL

Summary:
This study aims to compare the decisions of a multidisciplinary cancer board at Van research and educational Hospital with those generated by ChatGPT-4O. Patient data, including age, gender, comorbidities, clinical findings, laboratory results, radiology, and pathology reports, will be collected and recorded in patient files. These records will then be converted into a textual format appropriate for input into the ChatGPT-4O model. Statistical analyses will be conducted to assess the level of agreement between the board's decisions and those of ChatGPT-4O, providing insights into their compatibility and potential utility in clinical practice.

Eligibility criteria:
Inclusion Criteria:

* above 18 y/o
* patients diagnosed with malignancy

Exclusion Criteria:

* patients who refused to consent

Outcomes:
to evaluate the agreement between decisions made by a multidisciplinary cancer board and those generated by the ChatGPT-4O model using kappa statistics.
Assessment of the AI-assisted Registration Versus Conventional Point-based Registration on CBCTs With Heavy Metal Artifacts
Trial ID: NCT06273332
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-12-20
Completion date: 2024-02-25
Sponsor: Ain Shams University
Conditions: Registration Accuracy
Interventions: AI-assisted registration; Point-based registration
Locations: Private maxillofacial digital lab
Countries: Egypt
Enrollment: 16
Age range: 15 Years - None
Sex: ALL

Summary:
Our study investigates the accuracy and duration needed for 3D model registration using artifical intelligence (AI) assistance compared to conventional point-based registration. Manual segmentation of all cone beam computed tomography (CBCT) scans will be performed before the registration procedure.

Eligibility criteria:
Inclusion Criteria:

CBCT scans for either the maxilla or mandible or both and intraoral scans or manual impressions with metal restorations.

Exclusion Criteria:

Scans without metal restorations.

Outcomes:
Registration accuracy
AI-Enhanced Educational Intervention to Support Contextual Well-being in Young Adults (ContextWell)
Trial ID: NCT07009964
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-11-01
Completion date: 2026-02-01
Sponsor: University College Dublin
Collaborators: UCD Clinical Research Centre, Ireland; University College Cork; Imperial College London
Conditions: Well-Being; Lifestyle, Healthy; Young Adult; Behavior and Behavior Mechanisms
Interventions: ContextWell Educational Programme
Locations: University College Dublin
Countries: Ireland
Enrollment: 12
Age range: 18 Years - 30 Years
Sex: ALL

Summary:
This study explores the feasibility of ContextWell, a well-being education programme that combines an online webinar with an AI-powered behavioural nudging system. The programme aims to support university students, particularly those in healthcare disciplines, in enhancing well-being and adopting healthier lifestyle habits.

The primary objective is to determine whether the programme can be feasibly delivered within university settings. A secondary objective is to assess its potential to improve student well-being and encourage behavioural changes aligned with lifestyle medicine principles.

The intervention builds on the understanding that healthcare professionals' lifestyle habits and well-being have a direct impact on patient care. Focusing on healthcare students helps advance the integration of lifestyle medicine within health education and clinical practice.

This single-group feasibility study delivers the full intervention to all participants. Students participate in an online well-being webinar and receive AI-generated behavioural prompts designed to promote self-awareness and healthier daily choices.

Key evaluation measures will indicated the feasibility of the programme among young adults in academic environments. Participant feedback and initial outcomes will support future development and inform larger-scale research.

Eligibility criteria:
Inclusion Criteria:

Primarily healthcare students, young adults enrolled in higher education (18-30 years old)

Access to a computer or mobile device with an internet connection;

Willingness to participate in the educational webinar;

Willingness to complete AI-driven assignments;

Able to read and understand English.

Exclusion Criteria:

Are under the age of 18, or above 30;

Inability to access a computer or mobile device with internet connection;

Inability to read or understand English;

Unwillingness to participate in the webinar or complete assignments.

Outcomes:
Recruitment Rate; Retention Rate
Effect of a Machine Learning Ventilator Decision System Versus Standard Controlled Ventilation on in Critical Care: a Randomized Trial
Trial ID: NCT05132751
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-01-01
Completion date: 2024-12-01
Sponsor: Hu Anmin
Conditions: Mechanical Ventilation; Critically Ill Patients
Interventions: Machine Learning Ventilator Decision System
Enrollment: 300
Age range: 18 Years - None
Sex: ALL

Summary:
Ventilator-induced lung injury is associated with increased morbidity and mortality. Despite intense efforts in basic and clinical research, an individualized ventilation strategy for critically ill patients remains a major challenge. However, an individualized mechanical ventilation approach remains a challenging task: A multitude of factors, e.g., lab values, vitals, comorbidities, disease progression, and other clinical data must be taken into consideration when choosing a patient's specific optimal ventilation regime. The aim of this work was to evaluate the machine learning ventilator decision system, which is able to suggest a dynamically optimized mechanical ventilation regime for critically-ill patients. Compare with standard controlled ventilation, to test whether the clinical application of the machine learning ventilator decision system reduces mechanical ventilation time and mortality.

Eligibility criteria:
Inclusion Criteria:

1. only the first ICU stay was eligible;
2. adults ≥ 18 years of age on ICU admission;
3. estimate mechanical ventilation time ≥24 hours;

Outcomes:
Mechanical ventilation time
Artificial INtelligence eNabled 3D Facial Scanner for Airway Assessment (AINFAS)
Trial ID: NCT07411495
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2022-05-17
Completion date: 2026-12
Sponsor: National University Hospital, Singapore
Collaborators: National University of Singapore
Conditions: Anaesthesia
Interventions: AI-Assisted Preoperative Airway Risk Assessment
Locations: National University Hospital Singapore
Countries: Singapore
Enrollment: 1000
Age range: 21 Years - 100 Years
Sex: ALL

Summary:
We're developing a new AI, which uses advanced computer technology to help doctors identify patients who might have a difficult airway before surgery or emergency procedures. Sometimes, when a person needs help breathing, doctors have to insert a tube into their airway. This can be challenging for some people due to the shape of their mouth, throat, or neck. We hope that AI will look at a patient's facial features to predict if there might be any difficulties.

Eligibility criteria:
Inclusion Criteria:

* Undergoing surgery under general anaesthesia requiring endotracheal intubation or supraglottic airway
* 21-100 years old

Exclusion Criteria:

* Age less than 21 years
* Patients with prior surgery with altered facial appearance
* Patients with tracheostomy
* Patients with any oropharyngeal pathology
* Patients with nasopharyngeal carcinoma post radiotherapy or chemotherapy
* Pregnant females
* Patients whose physicians did not use a laryngoscope or supraglottic airway

Outcomes:
Accuracy of AINFAS in Predicting Difficult Airways