moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Strategy for EArly Recognition of Cancer, COPD & Heart Failure in the Emergency Department
Trial ID: NCT07391280
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-02-16
Completion date: 2026-12-14
Sponsor: NHS Greater Glasgow and Clyde
Collaborators: University of Glasgow
Conditions: Cancer; Cardiovascular; Respiratory
Interventions: Annalise.AI Chest X-Ray Solution
Enrollment: 25000
Age range: 18 Years - None
Sex: ALL

Summary:
SEARCH-ED is a research study which is running in Emergency Department (ED) of the Queen Elizabeth University Hospital. The aim of the study is to find out if using a computer programme can help doctors diagnose heart and lung problems from chest x-rays.

We want to compare how many people are diagnosed with heart or lung problems for the first time when doctors have access to the computer programme results, in comparison to when they don't.

Eligibility criteria:
Inclusion Criteria:

Unconsented Use of AE Algorithm in Emergency Department (ED):

* Frontal Chest X-Ray (CXR) (AP or PA) acquired in the Queen Elizabeth University Hospital (QEUH) ED
* Patients aged 18 or over
* Appropriate meta data (DICOM) to allow for Annalise Enterprise processing and secondary capture report provision.

Patient Focus Groups:

* Aged 18 or over
* Able to provide written, informed consent in English.

Clinician Focus Groups:

* Aged 18 or over
* Able to provide written, informed consent in English.
* Working as a doctor, advanced nurse practitioner or advanced clinical practitioner in ED, radiology or downstream medical specialties
* For post-implementation focus groups only, must have at least 4 months experience of working with AE algorithm.

Diagnostic Clinic:

* Patients without terminal illness or advanced frailty
* Usual healthcare provider based in NHS GGC

Exclusion Criteria:

Applies to use of unconsented CXRs:

\- Patient has requested that they are removed from the study, or has objected to the use of AI in their routine clinical care and this has been subsequently upheld by the health board.

Applies to invitation to combined diagnostic clinic:

* Patients not available to follow up, including patients i.e. whose the patient's usual care (or onward care following index admission) is out-with NHS GGC.
* Patients who have been referred to palliative care for end-stage disease, or patients with severe frailty (i.e.

bedbound) will not be invited to the combined diagnostic clinic

For Patient and Clinician Focus Groups:

* Unable to provide informed written consent in English
* Aged \<18

Outcomes:
Proportion of patients identified with a confirmed new diagnosis of heart failure, based on subsequent clinical assessment and guideline-based investigation.
The Construction of Precision Medicine in Chinese Medicine
Trial ID: NCT04434924
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2020-06-24
Completion date: 2022-05-31
Sponsor: China Medical University Hospital
Conditions: Hypertension
Locations: China Medical University Hospital
Countries: Taiwan
Enrollment: 600
Age range: 20 Years - None
Sex: ALL

Summary:
"Precise medicine" is also called "personal medicine". With the development of big data database, genetic testing and artificial intelligence, the concept of precision medicine has been applied to the diagnosis, treatment and prevention of different diseases. Traditional Chinese medicine (TCM) has traditionally emphasis on personalized medicine, according to each person's body constitution, through discriminatory treatment and personalized treatment methods. Chinese medicine doctors collect patients' information to distinguish the type of TCM pattern, and then according to the different types of diagnosis of traditional Chinese medicine to decide of using herbs, acupuncture, massage and other treatment strategies. However, lacking of objective data of traditional Chinese medicine, the current research on precision medicine in Chinese medicine is still limited. The aim of this study is to establish a big database of traditional Chinese precision medicine, and use the patients with hypertension as the population to conduct research on precision medicine in Chinese medicine.This study will combine the TCM body constitutional questionnaire, tongue diagnosis, pulse diagnosis, Chinese medicine immunoassay and genetic testing. In addition, the information providing by the medical electronic medical record system of China Medical University Hospital will be connected, which include modern medical diagnosis, treatment, blood and biochemical analysis and imaging. We will combine the big data analysis and artificial intelligence interpretation technology to explore the correlation between different TCM syndrome types and gene variations in patients with hypertension and establish a database of TCM precision medicine. We will also conduct the education training and symposium to train TCM precision medical talents and promote public understanding of TCM precision medicine. This project will establish a database of traditional Chinese medicine precision medicine to provide the correlation between different TCM pattern types of patients with hypertension, the reaction to different herbal medicine, and gene mutation, and provide reference for TCM clinicians.

Eligibility criteria:
Inclusion Criteria:

1. Primary hypertension
2. Older than 20 year-old

Exclusion Criteria:

1. Past history of cancer
2. Hemodialysis
3. Hypertension caused by renal diseases or endocrine disorders
4. Pregnancy
5. Patients can not received investigation of body constitution, TCM tongue examination and pulse examination

Outcomes:
The correlation between TCM body constitution and genetic variation
Development of an Artificial Intelligence System for Assessment of Tumor Risk and Diagnosis and Treatment Based on Multimodal Data Fusion Using Deep Learning Technology
Trial ID: NCT05426135
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2022-06-01
Completion date: 2026-10
Sponsor: Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
Conditions: Artificial Intelligence; Deep Learning; Lung Cancer; Lung; Node; Stomach Cancer; Colon Cancer; Cancer Risk; Cancer Screening; Cancer, Treatment-Related
Locations: Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
Countries: China
Enrollment: 3000
Age range: 18 Years - 75 Years
Sex: ALL

Summary:
To improve the accuracy of risk prediction, screening and treatment outcome of cancer, we aim to establish a medical database that includes standardized and structured clinical diagnosis and treatment information, image features, pathological features, and multi-omics information and to develop a multi-modal data fusion-based technology system using artificial intelligence technology based on database.

Eligibility criteria:
Inclusion Criteria:

1. Participants with the suspected of lung cancer/node, or stomach cancer/lesion, or colorectal cancer/leision
2. Participants that have signed informed consent.
3. Participants with detailed electronic medical records, image records, pathological records, multi-omics information, and other important clinical diagnostic information.
4. Healthy participants with no clinical diagnosis of lung cancer/node, or stomach cancer/lesion, or colorectal cancer/leision.

Exclusion Criteria:

1. Participants with primary clinical and pathological data missing.
2. Participants lost to follow-up.
3. Participants with too poor medical image quality to perform segment and mark ROI accurately

Outcomes:
The outcome of clinical diagnosis of suspected patients with lung cancer/pulmonary nodular (Benign/Malignant nodule); The outcome of clinical diagnosis of suspected patients with stomach cancer or lesion (Benign/Malignant).; The outcome of clinical diagnosis of suspected patients with colorectal cancer or lesion (Benign/Malignant).; Treatment response of anti-cancer therapy at first evaluation in patients with lung/stomach/colorectal cancer (CR, PR, PD, SD).
A Deep-learning-based Multi-modal Phonocardiogram(PCG) and Electrocardiogram(ECG) Processing Framework for Screening Depressed Left Ventricular Ejection Fraction (dLVEF) Using a Wearable Cardiac Patch
Trial ID: NCT06009718
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2023-08-25
Completion date: 2028-06-01
Sponsor: Ruijin Hospital
Conditions: Heart Failure
Locations: Ruijin Hospital, Shanghai Jiaotong School of Medicine; Shanghai Chest Hospital, Shanghai Jiao Tong University School Of Medicine; Shanghai East Hospital
Countries: China; China; China
Enrollment: 3000
Age range: 18 Years - 100 Years
Sex: ALL

Summary:
The diagnosis of depressed left ventricular ejection fraction (dLVEF) (EF\<50%) depends on golden standard ultrasound cardiography (UCG). A wearable synchronized phonocardiography (PCG) and electrocardiogram (ECG) device can assist in the diagnosis of dLVEF, which can both expedite access to life-saving therapies and reduce the need for costly testing.

Eligibility criteria:
Inclusion Criteria:

* Attendance at RuiJin hospital for UCG
* Signed dated informed consent
* Commit to follow the research procedures and cooperate in the implementation of the whole process research
* UCG has been completed
* Age ≥ 18
* At least 8 consecutive cycles of sinus rhythm can be recorded

Exclusion Criteria:

* Patients with pacemakers
* Complete left bundle branch block or block or QRS wave widening\>120ms
* Left chest skin damaged or allergic to patch
* Refusal to participate

Outcomes:
Determination of Heart Failure Disease
Exploring the Feasibility of Digital Intervention Games as a Diagnostic and Management Tool for Delirium at SGH Inpatient Wards
Trial ID: NCT06240897
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-02
Completion date: 2026-04
Sponsor: Institute for Digital Medicine (WisDM)
Collaborators: Singapore General Hospital; National University of Singapore
Conditions: Delirium
Interventions: CURATE.Dtx; Optional games
Locations: Singapore General Hospital
Countries: Singapore
Enrollment: 20
Age range: 65 Years - 99 Years
Sex: ALL

Summary:
Inpatient delirium incidence is widely documented in international and local studies, however, there is no viable and labour-free delirium detection and management tool. CURATE.DTx is our tablet ready, multi-tasking serious game, that leverage a small data artificial intelligence-derived platform that can dynamically personalise cognitive training by modifying the game intensity. The main aims of this study are to explore the feasibility and usability of this new digital tool to aid in the delirium screening and management of at-risk delirium hospitalized patients.

Eligibility criteria:
Inclusion Criteria:

* Admitted to the medical ward in SGH
* Age \>=65 years old
* Patients with a life expectancy of at least 1 month

Exclusion Criteria:

* Pregnant or breastfeeding women
* Patients physically incapable of using computer tablets (either due to vision loss or dominant hand weakness)
* Patients who cannot understand spoken the English language.
* Patients who are unable to give informed consent.

Outcomes:
Patient Acceptability; Trial Team Members Acceptability; Demand; Implementation; Practicality
Predicting Atrial Fibrillation in Patients With Post-implantable Cardiac Monitor Implementation : A Prospective, Long-term Follow-up Study Using Comprehensive AI ECG Analysis : Multicenter Prospective Study
Trial ID: NCT07347691
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-11-19
Completion date: 2028-05
Sponsor: Inha University Hospital
Collaborators: DeepCardio Co., Ltd.
Conditions: Embolic Stroke of Undetermined Source
Locations: Korea University Ansan Hospital; Inha University Hospital; Jeju National University Hospital; Korea University Guro Hospital; Ajou University Hospital
Countries: South Korea; South Korea; South Korea; South Korea; South Korea
Enrollment: 92
Age range: 30 Years - None
Sex: ALL

Summary:
This study investigates patients with Embolic Stroke of Undetermined Source (ESUS) who have received an Implantable Cardiac Monitor (ICM). The main purpose is to evaluate the predictive value of an Artificial Intelligence ECG analysis tool, named SmartECG-AF.

Participants will be classified into two groups based on the AI analysis: a "High Risk" group and a "Low to Intermediate Risk" (control) group. The study aims to compare the incidence rate of atrial fibrillation (AF) events over time between these two groups. Additionally, the study will analyze the relationship between the AI-predicted risk levels and the occurrence of major cardiovascular events during the follow-up period.

Eligibility criteria:
Inclusion Criteria:

* Patients aged 30 years or older.
* Patients diagnosed with Embolic Stroke of Undetermined Source (ESUS) who have undergone or are scheduled for Implantable Cardiac Monitor (ICM) implantation.
* Patients who have undergone at least one 12-lead ECG examination within 2 weeks before or after the date of ICM implantation.
* Patients maintaining Sinus Rhythm on ECG at the time of enrollment.
* Patients who have voluntarily signed the informed consent form.

Exclusion Criteria:

* Patients diagnosed with Atrial Fibrillation (AF) at least once prior to the date of enrollment.
* Patients whose ICM battery status is at Elective Replacement Interval (ERI), making recording impossible.
* Patients whose ECGs cannot be analyzed by the AI algorithm (SmartECG-AF) due to severe artifacts or noise, or are incompatible with digital analysis.

Outcomes:
Incidence of Atrial Fibrillation (Time-to-Event)
The Clinical Application of Artificial Intelligence Assisted Renal Biopsy Image Diagnosis System.
Trial ID: NCT07330362
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-07-01
Completion date: 2026-12-31
Sponsor: Zhujiang Hospital
Conditions: Chronic Glomerulonephritis; Chronic Kidney Disease
Locations: Nanfang Hospital; Zhujiang Hospital of Southern Medical University
Countries: China; China
Enrollment: 2900
Age range: 15 Years - 79 Years
Sex: ALL

Summary:
This research project aims to collect images from patients with chronic glomerulonephritis. For each subject, the images obtained from renal biopsy will undergo evaluation by both the diagnostic model and the 'gold standard' diagnosis by pathologists. The researchers will test the subjects using the diagnostic model and compare the results with the known 'gold standard' diagnosis, in order to evaluate the AUC, specificity, and sensitivity of the diagnostic model.

Eligibility criteria:
Inclusion Criteria:

1. Voluntary signing of informed consent form;
2. Patients clinically diagnosed or suspected of having chronic kidney disease according to the 2023 KDIGO Clinical Practice Guideline for the Evaluation and Management of Kidney Disease;
3. Undergoing renal biopsy and pathological specimen preparation.

Exclusion Criteria:

1. Biopsy tissue from donor kidney or transplanted kidney;
2. Poor quality of pathological specimen, unable to conduct pathological diagnosis.

Outcomes:
The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of the TEM-AID artificial intelligence model
Validation and Added Clinical Value of Rapid On-site Diagnostics in Early Stage Lung Cancer Sampling With a Higher Harmonic Generation Microscope
Trial ID: NCT06007261
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-01-19
Completion date: 2027-04
Sponsor: Radboud University Medical Center
Collaborators: Flash Pathology BV; VU University of Amsterdam; ETH Zurich
Conditions: Lung Cancer
Locations: Radboudumc
Countries: Netherlands
Enrollment: 250
Age range: 18 Years - None
Sex: ALL

Summary:
The aim of this study is to verify a new microscopic technique in diagnosing patients suspected of lung cancer. Patients will undergo navigational bronchoscopy or surgical resection as a part of standard of care. Biopsies taken during this procedure will be imaged for a few minutes with a higher harmonic generation (HHG) microscope, after which the material is taken to the pathology department for histopathology assessment, which is currently the golden standard for diagnosis.

Eligibility criteria:
Inclusion Criteria:

* ASA physical status 1-3.
* Age 18 years or older.
* A pulmonary lesion with an indication for diagnostic or therapeutic evaluation following current clinical guidelines and/or as decided by multi-disciplinary team consultation.

Exclusion Criteria:

* Bleeding disorders.
* Less than 18 years old.
* Inability to consent.
* Unfit for navigation bronchoscopy and/or unfit for surgical resection as per guideline recommended care and multi-disciplinary tumor board decision making.

Outcomes:
The ability to obtain a diagnosis from HHG microscopy images as judged by the pathologist; The ability to obtain a diagnosis from HHG microscopy images as autonomously assessed by a developed AI algorithm.
AI-Driven Integration of Muscle Mass and Muscle Function: A Novel Approach to Sarcopenia Risk Assessment and Intervention
Trial ID: NCT07426159
Status: ACTIVE_NOT_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-02-01
Completion date: 2027-12-31
Sponsor: Tel Aviv University
Conditions: Sarcopenia; Age-related Muscle Loss
Interventions: Personalized AI-Guided Exercise and Nutrition
Locations: Sylvan Adams Sport Institute
Countries: Israel
Enrollment: 120
Age range: 50 Years - 70 Years
Sex: ALL

Summary:
Sarcopenia, the age-related decline in muscle mass and function, is a major contributor to frailty, disability, and mortality in older adults. Current diagnostic tools assess muscle quantity or function separately and lack predictive biomarkers, limiting early detection and personalized management. This study proposes an AI-driven framework that integrates multimodal physiological, metabolic, and functional data with wearable sensor monitoring to improve sarcopenia risk assessment and guide individualized interventions.

In Phase 1, we will analyze a large retrospective dataset of 3,500 adults to identify early predictors of sarcopenia and develop a machine learning-based risk stratification model. Phase 2 will test a 12-week personalized exercise and nutrition intervention in 120 participants, using real-time sensor data and AI-guided adjustments to optimize outcomes. This integrative approach aims to advance early detection, precision intervention, and long-term muscle health in aging populations.

Eligibility criteria:
Inclusion Criteria:

* Men and women aged 50-70 years
* At risk for sarcopenia based on muscle mass and/or muscle function screening
* Able to participate in supervised exercise training
* Willing to comply with study procedures and provide written informed consent

Exclusion Criteria:

* Participation in structured exercise or weight loss programs within the past 6 months
* Unstable body weight (\>±5%) in the past 6 months
* Current smoking or smoking within the past 6 months
* Pregnancy, breastfeeding, or post-menopause
* Contraindications to MRI (e.g., implanted devices, tattoos, permanent makeup)
* Severe cardiopulmonary disease (e.g., recent myocardial infarction, unstable angina)
* Musculoskeletal or neuromuscular conditions limiting exercise participation
* Cognitive impairment
* Chronic diseases including cancer, diabetes, thyroid disease, hypertension, or chronic renal failure
* Use of medications affecting metabolism
* Secondary liver disease (viral, autoimmune, alcoholic, or drug-induced)
* Alcohol intake \>20 g/day (women) or \>30 g/day (men)

Outcomes:
Accuracy of AI-Based Sarcopenia Risk Prediction Model; Change in MRI-Derived Thigh Muscle Volume; Change in Handgrip Strength (kg)
Effectiveness of Artificial Intelligence Integrated Mixed Reality-based High-Alert Medications Management Simulation Program
Trial ID: NCT07390461
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-03-04
Completion date: 2026-03-06
Sponsor: Chonnam National University Hospital
Conditions: Healhty
Interventions: Artificial Intelligence integrated Mixed Reality-based Simulation Program; Standard medication-management education
Enrollment: 60
Age range: None - None
Sex: ALL

Summary:
The goal of this clinical trial is to learn if a Artificial Intelligence integrated Mixed Reality-based High-Alert Medications Management Simulation Program (AIMR-HAM) helps hospital nurses manage high-alert medicines (HAMs) more safely. MR mixes real and virtual elements to let nurses practice in realistic scenarios.

The main questions are:

Does the AIMR-HAM improve nurses' medication safety skills? Does the AIMR-HAM lower medication errors and improve clinical performance?

Researchers will compare two groups to answer these questions:

Intervention group: AIMR-HAM Control group: standard education only

Who can take part:

Nurses who work at large hospitals and have 1 to 6 years of clinical experience.

About 60 nurses will join the study.

What participants will do:

Attend the assigned training (AIMR-HAM or standard education only). Complete short tests and surveys before and after training to measure skills, communication, and clinical reasoning.

Report any medication errors that occur during the study. Why this matters: The study will show whether AIMR-HAM training can improve how nurses handle HAMs and make patient care safer.

Eligibility criteria:
Inclusion Criteria:

* Nurses with at least one to six years of clinical experience.

  * Those who understand the purpose and procedures of this study and have given written consent to participate.

    * Those who have no physical or cognitive limitations in using mixed reality devices.

      ④ Those who are able to communicate in Korean and understand and respond to questions.

Exclusion Criteria:

* Those who do not wish to participate in the study. ② Those who have participated in education related to high-alert medications within the past six months.

  * Those who are unable or have difficulty participating in the mixed reality education program due to visual, hearing, or neurological impairments, or adverse effects such as dizziness or motion sickness.

    * Those who voluntarily withdraw from the study midway through.

Outcomes:
High-Alert Medication clinical performance
Study Using Deep Learning-based Artificial Intelligence for the Diagnosis of Small Bowel Obstruction
Trial ID: NCT06481358
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2022-09-01
Completion date: 2024-10-31
Sponsor: Nagoya University
Conditions: Bowel Obstruction; Artificial Intelligence
Interventions: Artificial intelligence
Locations: Nagoya University Graduate School of Medicine
Countries: Japan
Enrollment: 17
Age range: None - None
Sex: ALL

Summary:
The study will compare the diagnostic accuracy and time to diagnosis of computed tomography images of patients with suspected intestinal obstruction seen in the emergency room by residents and surgeons, with and without artificial intelligence.

Eligibility criteria:
Inclusion Criteria:

* Persons with documented consent

Exclusion Criteria:

* Persons without documented consent

Outcomes:
The diagnosis of the obstruction site