moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Conversational AI in Tactical Casualty Care: Baseline GPT-4o Improves Combat Medic Decision-Making
Trial ID: NCT06796036
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-02-02
Completion date: 2025-04-30
Sponsor: Charles University, Czech Republic
Collaborators: Czech Technical University in Prague
Conditions: Artificial Intelligence
Interventions: Combat Medic Decision-Making with and without artificial intelligence assistance
Locations: Military University Hospital Prague
Countries: Czechia
Enrollment: 42
Age range: 18 Years - None
Sex: ALL

Summary:
The aim of the project is to investigate whether the integration of artificial intelligence (AI) support, specifically through the GPT-4 model, enhances the decision-making processes of military medical first responders within the framework of Tactical Combat Casualty Care (TCCC). The study focuses on AI's ability to assist in ventilator settings for injured individuals in combat scenarios, emphasizing improved accuracy and decision-making speed. The project tests the hypothesis that the use of AI can positively impact outcomes without compromising the autonomy of first responders. The results have the potential to optimize patient care in challenging conditions and contribute to the advancement of combat medicine.

Eligibility criteria:
Inclusion Criteria:

* Combat medics actively serving in the Czech Armed Forces
* Completion of standardized Tactical Combat Casualty Care training modules and e-learning on ventilator settings and blood gas interpretation
* Successful passing of pre-tests to ensure a uniform baseline knowledge level.
* Willingness to participate and provide informed consent.
* Availability to complete the full study protocol, including 10 simulated scenarios.

Exclusion Criteria:

* Failure to pass the pre-tests or complete TCCC and ventilator management training
* Prior advanced training or professional certification in critical care or mechanical ventilation that could bias results
* Refusal to provide informed consent or inability to commit to the study schedule

Outcomes:
Accuracy of ventilator settings
Intelligent Diagnosis of Bladder Cancer Staging and Prediction of New Adjuvant Chemotherapy Efficacy Based on Deep Learning and Transfer Learning in Ultrasound-Magnetic Resonance-Pathology Multimodal Multiscale
Trial ID: NCT07051083
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-01-01
Completion date: 2026-12-31
Sponsor: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Conditions: Bladder Cancer; Staging; Deep Learning; Neoadjuvant Chemotherapy; Contrast Enhanced Ultrasound
Interventions: Risk Stratification
Locations: Sun Yat-sen Memorial Hospital, Sun Yat-sen University
Countries: China
Enrollment: 480
Age range: None - None
Sex: ALL

Summary:
Bladder cancer is the most common malignant tumor of the urinary system. The presence or absence of muscle invasion in early bladder cancer is an independent prognostic factor. The involvement of muscle invasion affects the choice of surgical methods and treatment. Preoperatively, the precise assessment of bladder cancer staging has important practical value. A more accurate preoperative assessment of bladder cancer staging can reduce overtreatment and provide a favorable basis for clinicians to choose more reasonable and effective surgical methods. Clinically, there has been a longstanding desire to diagnose the staging of bladder cancer through a simple, convenient, effective, and non-invasive examination. As relevant research progresses, a multi-omics diagnostic model will be beneficial in improving diagnostic efficiency. This project aims to establish a multi-omics artificial intelligence system based on deep learning and transfer learning to accurately diagnose the staging of bladder cancer and predict the efficacy of neoadjuvant chemotherapy. This system will assist in clinical treatment decision-making.

Eligibility criteria:
Inclusion Criteria:

1. Ultrasound and other imaging examinations (CT, MR, etc.) suggest bladder masses and are suspicious for bladder cancer patients.
2. The bladder is well filled, and no allergic reactions to ultrasound contrast agents are found.
3. No surgery or radiotherapy/chemotherapy has been performed.
4. Patients who meet the indications for surgical resection and are planned for surgical treatment, including one of the following:

   1. Clinical symptoms consistent with suspected bladder cancer (such as gross hematuria, etc.);
   2. Patients with confirmed primary or recurrent bladder cancer by cystoscopic biopsy;
   3. Rapid urine cytology and urine cytology FISH testing suggest malignancy.

Exclusion Criteria:

1. Individuals unable to tolerate surgery;
2. Individuals allergic to ultrasound contrast agents, unable to undergo ultrasound contrast examination;
3. Unsuccessful preoperative ultrasound contrast examination or non-compliant patients;
4. Postoperative pathology does not indicate bladder cancer;
5. Patients who have undergone chemotherapy or radiation therapy.

Outcomes:
Detection of Muscle Invasion in Bladder Cancer
Precision Risk Prediction of Symptom Clusters and Frailty With AI-Guided Emotion Detection: A Three-Year Study in Adults With Congenital Heart Disease
Trial ID: NCT07479654
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-03-01
Completion date: 2028-12-31
Sponsor: Mackay Memorial Hospital
Conditions: Adult Congenital Heart Disease; Symptom Clusters; Frailty; Risk Prediction Model; Artificial Intelligence-Based Voice Emotion Detection
Interventions: AI-Based Voice Emotion Detection-Guided Counselling; Usual Care (Control)
Enrollment: 410
Age range: 20 Years - None
Sex: ALL

Summary:
The goal of this three-year mixed-methods observational study with an embedded randomized controlled trial is to develop and validate a frailty risk prediction model and evaluate an artificial intelligence-based voice emotion detection-guided counselling intervention in adults with congenital heart disease (ACHD).

The main questions it aims to answer are:

Are symptom clusters associated with frailty and psychological outcomes in adults with congenital heart disease?

Can symptom clusters and psychosocial factors be used to predict frailty risk over time in ACHD patients?

Does an AI-based voice emotion detection-guided counselling intervention improve psychological outcomes, fatigue, and quality of life among high-risk ACHD patients?

Researchers will compare ACHD patients receiving AI-based voice emotion detection-guided counselling with those receiving usual care to determine whether the intervention reduces depression, anxiety, sleep disturbance, fatigue, and frailty risk, and improves grit and quality of life.

Participants will:

Complete longitudinal assessments of symptom clusters, frailty, and psychological status at baseline and follow-up time points

Participate in qualitative interviews to explore lived experiences related to symptoms and frailty

Receive AI-based voice emotion detection-guided counselling (intervention group only in Year 3)

Eligibility criteria:
Inclusion Criteria:

1. Adults aged 20 years or older.
2. Diagnosed with congenital heart disease by a physician.
3. Able to communicate in Mandarin or Taiwanese, with normal cognitive function.

Exclusion Criteria:

1. Presence of other congenital anomalies.
2. Currently hospitalized for acute decompensation, or having severe cognitive impairment or psychiatric symptoms that preclude completion of questionnaires.
3. Pregnant individuals.

Outcomes:
Depression and Anxiety assessed using the Patient-Reported Outcomes Measurement Information System (PROMIS) Short Form Scales
Accessible and Inclusive Diabetes Telecoaching Self-Management Program: Protocol for a Feasibility Study
Trial ID: NCT04927377
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-10-31
Completion date: 2025-09-30
Sponsor: University of Alabama at Birmingham
Conditions: Diabete Type 2; Disability Physical
Interventions: AI4DM; Attention-control
Locations: University of Alabama at Birmingham
Countries: United States
Enrollment: 90
Age range: 18 Years - 65 Years
Sex: ALL

Summary:
The purpose of this study is to develop and pilot test an accessible and inclusive Artificial Intelligence (AI)-assisted, individualized, family-focused lifestyle modification intervention (AI4DM) for glycemic control in people with disabilities.

Eligibility criteria:
Inclusion Criteria:

* Diagnosis of Type 2 Diabetes Mellitus,
* Living with a permanent physical disability such as spinal cord injury (SCI), spina bifida, multiple sclerosis, stroke, etc.
* Ability to converse and read in English

Exclusion Criteria:

* Current enrollment in any diabetes-related intervention
* Severe untreated depression in the past six months
* Major cardiac event in the past twelve months
* Uncontrolled blood pressure
* Resting tachycardia
* Renal failure
* Severe peripheral neuropathy
* Unavailability of a smartphone

Outcomes:
Glycemic Management
A Retrospective, Observational, Multi-centered Study to Validate a D3-D5 Embryo Prediction Tool and Assess Its Performance in a Clinical Setting.
Trial ID: NCT07464275
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-04-22
Completion date: 2026-06
Sponsor: Future Fertility Inc.
Conditions: Fertility Disorders; Infertility
Interventions: MAGENTA Blast
Locations: Future Fertility
Countries: Canada
Enrollment: 80
Age range: 18 Years - None
Sex: FEMALE

Summary:
This is a retrospective, observational, single-arm, multi-center study to evaluate the predictive performance of MAGENTA BLAST, an artificial intelligence (AI)-enabled software application, when used as analytical, adjunctive information to support Day 3 cleavage stage embryo predictions of blastocyst formation by Day 5.

Eligibility criteria:
Inclusion Criteria:

* Women undergoing fresh IVF treatment using their own eggs
* At least 18 years of age
* Total antral follicle count (AFC) of at least 12 as measured by ultrasound prior to stimulation (in cases where AFC is performed)
* Basal day 3 follicle-stimulating hormone levels (FSH) \< 10 IU (in cases where basal day 3 FSH is performed)
* At least 8 normally fertilized eggs at the pronuclear (2PN) stage
* Fertilization using only ejaculated sperm (fresh or frozen) - no surgically removed sperm
* At least one two-cell embryo on Day 3
* At least part of the embryo cohort is cultured until day 5 (i.e. 114-116 HPI)

Exclusion Criteria:

* Use of re-inseminated eggs (rescue ICSI)
* Gestational carriers
* Concurrent participation in another clinical study
* Previous enrollment in this clinical study (i.e., one cycle data per patient)
* History of cancer
* Planned Day 3 laser-assisted hatching or embryos that underwent Day 3 biopsy for preimplantation genetic testing (PGT)
* Low quality images
* Lack of correlating reproductive outcomes (IVF lab results not available)

Outcomes:
The association between using MAGENTA Blast as an informative adjunctive aid and the prediction of blastocyst formation by Day 5 in embryos graded as Good/Fair
The Artificial Intelligent Prognostic Assessment and Pathological Basis Research of Early Primary Hepatocellular Carcinoma After Minimally Invasive Treatment Based on Multimodal MRI and Clinical Big Data
Trial ID: NCT04299919
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2007-04-01
Completion date: 2024-06-30
Sponsor: The First Affiliated Hospital of Dalian Medical University
Conditions: Hepatocellular Carcinoma
Interventions: Minimally invasive treatment; Hepatectomy
Locations: The First Affiliated Hospital of Dalian Medical University
Countries: China
Enrollment: 1200
Age range: None - None
Sex: ALL

Summary:
The study evaluates artificial intelligence method based on multimodal magnetic resonance imaging (MRI) images and clinical data in preoperative prediction of prognosis in early hepatocellular carcinoma (HCC) patients treated with minimally invasive treatment. The correlation between prognosis-related MRI features and pathological features was studied through artificial intelligence method, so as to provide the interpretability of image features for predicting the prognosis of HCC patients treated with minimally invasive treatment.

Eligibility criteria:
Inclusion Criteria:

* Hepatocellular carcinoma patients received minimally invasive treatment (transcatheter arterial chemoembolization, radiofrequency ablation, or combined) or hepatectomy;
* Patients received MRI examination within 1 month before treatment;
* Complete post-treatment prognosis information.

Exclusion Criteria:

* local or systemic treatment before MR examination;
* Incomplete clinical and pathological data;
* Heavy image artifacts.

Outcomes:
Three-month recurrence; Six-month recurrence; One-year recurrence; Two-year recurrence; Three-year recurrence
Prospective, Multicenter, Randomized Evaluation of the Performance and Clinical Applicability of AI-Assisted Radiotherapy Contouring Software for Thoracic Organs at Risk
Trial ID: NCT05787522
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2022-09-30
Completion date: 2024-03-06
Sponsor: Tianjin Medical University Cancer Institute and Hospital
Collaborators: Guangzhou Perception Vision Medical Technology Co. Ltd; People's Hospital of Guangxi Zhuang Autonomous Region; Shanxi Province Cancer Hospital; Fifth Affiliated Hospital, Sun Yat-Sen University; Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
Conditions: Lung Cancer; Breast Cancer; Esophageal Cancer
Locations: Tianjin Medical University Cancer Institute and Hospital, Tianjin Key Laboratory of Cancer Prevention and Therapy
Countries: China
Enrollment: 500
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this clinical trial is to evaluate performance and clinical applicability of AI-assisted radiotherapy contouring software (iCurveE) for thoracic organs at risk. The main question it aims to answer is:

• Does AI-assisted contouring (AI contouring with manual modification) offer greater accuracy and time efficiency compared to manual contouring? After screening, the qualified participants' thoracic CT images will be anonymized and segmented using three methods: manual, AI (AI-only), and AI-assisted contouring. The researchers will compare the results generated by the three different contouring methods with the ground truth established by expert consensus, in order to evaluate both accuracy and time-related parameters

Eligibility criteria:
Inclusion Criteria:

1. ≥18 years old, no gender limit.
2. Patients diagnosed with breast cancer, lung cancer, or esophageal cancer, who are scheduled for chest CT scanning followed by thoracic radiotherapy.
3. CT slice thickness ≤5mm.
4. Patients understand the goal of the trial, are willing to attend the trial and sign the informed consent.

Exclusion Criteria:

1. Congenital malformations or abnormal anatomical structures resulting from non-tumor factors in the scan area.
2. Artifact, prosthesis or implantation causing images undistinguishable.
3. CT images not conforming to DICOM standards.
4. Investigators consider not suitable.

Outcomes:
volumetric DICE similarity coefficient, vDSC; Contouring time (min)
Acceptability Assessment of an "Organization of Care Integrating Artificial Intelligence and a Solution of Telemedicine" on Care of the Nursing Home Residents Located in a Medical Desert: Pilot Study INTEL@MED-POC
Trial ID: NCT04679181
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2020-12-15
Completion date: 2023-03-25
Sponsor: University Hospital, Limoges
Conditions: Homes for the Aged
Interventions: Telemedecine
Locations: EHPAD Le Bois Joli; EHPAD Le Chant des Rivières; EHPAD La Porte d'Aquitaine; Ehpad Le Chavanon; EHPAD Ernest Coulaud; EHPAD J et M Colaud
Countries: France; France; France; France; France; France
Enrollment: 308
Age range: 65 Years - None
Sex: ALL

Summary:
While depend elderly people needs are important in Nursing Home, the medical resources are weak from both inside and outside residences located in a medical desert. The goal of this project is to measure the acceptability of Artificial Intelligence and Telemedicine in Nursing Home located in medical desert for patients, nurses, and medical doctor

Eligibility criteria:
Inclusion Criteria:

* Patient older than 65 years old.
* Patient living in Nursing Home located in medical desert.
* Patient presenting symptoms involved medical doctor call.
* Patient or legal representative having signed consent form
* Patient covered by social security

Exclusion Criteria:

* End of life patient.
* Chronic aphasic patient.

Outcomes:
Acceptability for nurse to use Artificial Intelligence / Telemedecine device analyzed by distant medical doctor; Number of non-programmed hospitalization for both distant medical doctor and usual care recommendations; delay to obtain the medical expertise
Effectiveness of Using the Speedometer During Colonoscopy: a Prospective, Comparative Trial
Trial ID: NCT04710251
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-03-01
Completion date: 2022-12-31
Sponsor: Norwegian Department of Health and Social Affairs
Collaborators: Beth Israel Deaconess Medical Center; Oslo University Hospital; University of Oslo; Showa University
Conditions: Colonic Polyp; Colonic Adenoma; Adenoma Colon
Interventions: Speedometer
Enrollment: 332
Age range: 18 Years - None
Sex: ALL

Summary:
PURPOSE OF PROTOCOL

Objective:

To evaluate if the speedometer based on the real-time image analysis can help endoscopists increase their withdrawal time, which is defined as time spent examining the colon during withdrawal of the colonoscope, during colonoscopy.

Hypothesis:

The trial hypothesis is that use of the speedometer during colonoscopy will increase the average withdrawal time, which is defined as time spent examining the colon during withdrawal of the colonoscope, by 1.6 minutes, possibly increasing the performance of the participating endoscopists. Our objective is to clarify the clinical benefits of this digital tool in colonoscopy.

Endpoint: Withdrawal time difference between colonoscopies done without the speedometer (control period) and colonoscopies done with the speedometer (intervention period).

Eligibility criteria:
Inclusion Criteria:

* Both women and men aged 18 years or older undergoing colonoscopy for any indication at Beth Israel Deaconess Medical Center (BIDMC), Boston, USA.

Exclusion Criteria:

* Known colorectal cancer, hereditary colorectal polyposis, inflammatory bowel disease, or past history of colectomy.

Outcomes:
Withdrawal time
Validation of the Utility of Ophthalmology Intelligent Diagnostic System: A Clinical Trial
Trial ID: NCT03499145
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2018-04-01
Completion date: 2019-08-31
Sponsor: Sun Yat-sen University
Collaborators: Ministry of Health, China; Xidian University
Conditions: Ophthalmopathy; Artificial Intelligence
Interventions: Ophthalmology diagnostic system.
Locations: Zhongshan Ophthalmic Center, Sun Yat-sen University
Countries: China
Enrollment: 615
Age range: None - None
Sex: ALL

Summary:
The prevention and treatment of diseases via artificial intelligence represents an ultimate goal in computational medicine. Application scenarios of the current medical algorithms are too simple to be generally applied to real-world complex clinical settings. Here, the investigators use "deep learning" and "visionome technique", an novel annotation method for artificial intelligence in medical, to create an automatic detection and classification system for four key clinical scenarios: 1) mass screening, 2) comprehensive clinical triage, 3) hyperfine diagnostic assessment, and 4) multi-path treatment planning. The investigator also establish a telemedicine system and conduct clinical trial and website-based study to validate its versatility.

Eligibility criteria:
Inclusion Criteria:

* Patients and residents who underwent ophthalmic examination of the eye and recorded their ocular information in the outpatient clinic and community.

Outcomes:
The proportion of accurate, mistaken and miss detection of the ophthalmology diagnostic system.
The Application of Large Language Model in Emergency Chest Pain Triage
Trial ID: NCT06493175
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-12-20
Completion date: 2024-12-20
Sponsor: Peking University Third Hospital
Collaborators: Jinan Central Hospital; Qingdao Municipal Hospital; Tianjin Medical University General Hospital; The First Hospital of Hebei Medical University
Conditions: Chest Pain
Interventions: Application of large language model in emergency chest pain triage.; According to the normal procedures to receive medical treatment
Locations: Peking University Third Hospital
Countries: China
Enrollment: 2000
Age range: 18 Years - None
Sex: ALL

Summary:
This study will evaluate the accuracy and efficiency of large language model in emergency triage.

Eligibility criteria:
Inclusion Criteria:

1. All patients with chest pain entered the emergency triage procedure.
2. patients aged 18 and above.

Exclusion Criteria:

1. Patients with severe cognitive impairment or inability to communicate.
2. There are patients who have been explicitly referred to specific departments (for example, some of the 120 transfer patients, who may go directly to the green channel) .
3. Patients with unstable vital signs .
4. Patients with potential medical problems.
5. Is participating in other clinical trials.
6. Failure to follow test procedures.
7. Those who refuse to sign the informed consent form.

Outcomes:
The Diagnostic Accuracy Rate of MedGuide-V5