moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
A Feasibility Study To Determine The Effect Of An Artificial Intelligence (AI) Based Motion Tracking Exercise Programme (Kemtai) On Amount Of Physiotherapy Contact And Length Of Hospital Stay After Knee Arthroplasty Compared With Exercise As Usual .
Trial ID: NCT07502040
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-03-31
Completion date: 2026-11-28
Sponsor: Hull University Teaching Hospitals NHS Trust
Conditions: Knee Arthroplasty, Total
Interventions: Kemtai Digital App; NHS standard care
Enrollment: 40
Age range: 18 Years - None
Sex: ALL

Summary:
To stay strong, patients should do some strength training at least twice a week. However arthritic pain can limit walking and activity. The wait for knee surgery can be long, which can lead to loss of muscle strength. Patients who need a new knee joint tend to be older. If older people do not exercise and become weak, they tire easily, and may take longer to recover after surgery. Patients recover faster after having a new knee joint if their leg muscles are strong. This is why exercise before surgery can help people to make the most of their new joint. Patients come to a joint school where the investigators show them exercises and advise how to get ready for surgery. Patients are given a sheet of pictures and prompts for each exercise but the investigators know that some people don't do the exercises that they are given. Exercises are more likely to be done if they are fun, if people can see they make a difference, if they know what to do, and if their physiotherapist can check what they are doing.

Eligibility criteria:
Inclusion Criteria:

1. Adults aged ≥18 years.
2. Referred to Hull University Teaching Hospitals NHS Trust t for Knee Arthroplasty at either Castle Hill Hospital or Goole District Hospital and has access to pre operative resources offered by the joint school physiotherapists.
3. Has access to a device which is compatible with the operational requirements of the Kemtai programme.
4. Able to give informed consent.

Exclusion Criteria:

• Significant comorbid physical or mental illness considered by the investigator to either: prevent engagement in modified exercise; impair the participants ability to follow instructions place the participant at undue risk during exercise training adversely affect the recovery or rehabilitation trajectory

* Patients already involved in an exercise or rehabilitation study will be excluded from this study.
* Unwilling or unable to consent or complete study measures.
* Has insufficient understanding of English or the languages that are supported by the Kemtai programme and does not have someone who can translate.
* Is a prisoner
* Has had a total hip replacement within the previous 6 months.
* Is attending for revision of a previous knee replacement
* Is having a knee replacement due to a tumour
* Lacks visual acuity sufficient to follow stimuli on monitor or has someone who can give necessary verbal prompts
* Severe genu varum or genu valgum deformity which precludes the ability to exercise using the Kemtai programme.

Outcomes:
Length of stay in hospital; Number of post operative physiotherapy contacts
A Randomized Trial on the Utilization of Endocuff-assisted Colonoscopy and Computer-aided Detection in Optimizing Colonoscopies in the Elderly
Trial ID: NCT06621225
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-11-22
Completion date: 2023-06-30
Sponsor: Pankaj Patel
Conditions: Colorectal Carcinoma
Interventions: Artificial Intelligence; Mucosal Exposure Device
Locations: The Surgery and Endoscopy Center of Sebring
Countries: United States
Enrollment: 264
Age range: 75 Years - None
Sex: ALL

Summary:
N = 264 patients (50% female) aged 75 years and above undergoing colonoscopy were enrolled. Patients were randomly assigned into one of the three intervention groups: the primary intervention arm (CADe in combination with the MED), the second group with MED alone, and the control group with WLE. All detected lesions were removed and sent to histopathology for diagnosis. The primary outcome was the adenoma detection rate. Secondary outcomes were adenoma detection in the left colon in our cohort of patients.

Eligibility criteria:
Inclusion Criteria

1. Patients aged 75 years and older.
2. Patients who were able to provide informed consent or had a legally authorized representative who can consent on their behalf.
3. Patients who were deemed fit for colonoscopy based on a pre-procedure evaluation.

Exclusion criteria

1. Patients with acute gastrointestinal conditions such as active colitis, acute diverticulitis, or bowel obstruction.
2. Patients with a history of major colorectal surgery that might alter normal colon anatomy (e.g., colectomy).
3. Patients with severe comorbid conditions that would contraindicate colonoscopy, such as severe cardiopulmonary disease or advanced liver disease.
4. Patients with uncorrected coagulopathies or those on anticoagulation therapy that cannot be safely managed around the time of the procedure.
5. Patients who are unable to adequately prepare the bowel for colonoscopy.
6. Patients who refuse to participate in the study or have a legally authorized representative who refuses consent.

Outcomes:
Primary Outcome Measure
Real Time Computer-aided Diagnosis (CADx) of Diminutive Colorectal Polyps Using Artificial Intelligence
Trial ID: NCT05349110
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-08-20
Completion date: 2022-12
Sponsor: Maastricht University Medical Center
Collaborators: Catharina Ziekenhuis Eindhoven; Eindhoven University of Technology
Conditions: Colorectal Neoplasms; Colorectal Polyp
Interventions: Computer-aided diagnosis (CADx) systems
Locations: Maastricht University Medical Center; Catharina Ziekenhuis Eindhoven
Countries: Netherlands; Netherlands
Enrollment: 105
Age range: 18 Years - None
Sex: ALL

Summary:
Correct endoscopic prediction of the histopathology and differentiation between benign, pre-malignant, and malignant colorectal polyps (optical diagnosis) remains difficult. Artificial intelligence has great potential in image analysis in gastrointestinal endoscopy. Aim of this study is to investigate the real-time diagnostic performance of AI4CRP for the classification of diminutive colorectal polyps, and to compare it with the real-time diagnostic performance of commercially available CADx systems.

Eligibility criteria:
Inclusion Criteria:

* Age \>18 years;
* Patients with at least one colorectal polyps encountered during colonoscopy;
* Patients referred for a colonoscopy by the Dutch bowel cancer screening program, patients undergoing a colonoscopy for endoscopic surveillance, or patients undergoing a colonoscopy because of complaints;
* Written informed consent.

Exclusion Criteria:

* Patients with prior history of inflammatory bowel diseases (IBD) or polyposis syndromes;
* Patients with inadequate bowel preparations after adequate washing, suctioning, and cleaning manoeuvres have been performed by the endoscopist;
* Patients undergoing an emergency colonoscopy;
* Written objection in the patient file for participation in scientific research.

Outcomes:
Technical feasibility of real-time use of AI4CRP.; User interface feasibility of real-time use of AI4CRP.; The diagnostic accuracy of AI4CRP per image modality (HDWL, BLI, LCI, i-scan).; The sensitivity of AI4CRP per image modality (HDWL, BLI, LCI, i-scan).; The specificity of AI4CRP per image modality (HDWL, BLI, LCI, i-scan).; The negative predictive value of AI4CRP per image modality (HDWL, BLI, LCI, i-scan).; The positive predictive value of AI4CRP per image modality (HDWL, BLI, LCI, i-scan).; The Area Under ROC Curve (AUC) of AI4CRP per image modality (HDWL, BLI, LCI, i-scan).
Clinical Validation of an Artificial Intelligence Software for Digital Cholangioscopy Diagnosis: an Observational Trial
Trial ID: NCT05147389
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2020-10-01
Completion date: 2022-05-01
Sponsor: Instituto Ecuatoriano de Enfermedades Digestivas
Collaborators: The Methodist Hospital Research Institute; University of Sao Paulo; Vrije Universiteit Brussel; Advanced Endoscopy Research, Robert Wood Johnson Medical School Rutgers University; Baylor St. Luke's Medical Center; Universitair Ziekenhuis Brussel
Conditions: Common Bile Duct Neoplasms; Non-Neoplastic Bile Duct Disorder
Interventions: AI model classification; DSOC endoscopist experts' classification
Locations: Advanced Endoscopy Research, Robert Wood Johnson Medical School Rutgers University; Baylor Saint Luke's Medical Center; Houston Methodist Hospital; Department of Advanced Interventional Endoscopy, Universitair Ziekenhuis Brussel (UZB)/Vrije Universiteit Brussel (VUB); Serviço de Endoscopía Gastrointestinal do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo; Carlos Robles-Medranda
Countries: United States; United States; United States; Belgium; Brazil; Ecuador
Enrollment: 170
Age range: 18 Years - 79 Years
Sex: ALL

Summary:
Digital single-operator cholangioscopy (DSOC) findings achieve high diagnostic accuracy for neoplastic bile duct lesions. To date, there is not a universally accepted DSOC classification. Endoscopists' Intra and interobserver agreements vary widely. Cholangiocarcinoma (CCA) assessment through artificial intelligence (AI) tools is almost exclusively for intrahepatic CCA (iCCA). Therefore, more AI tools are necessary for assessing extrahepatic neoplastic bile duct lesions.

In Ecuador, the investigators have recently proposed an AI model to classify bile duct lesions during real-time DSOC, which accurately detected malignancy patterns. This research pursues a clinical validation of our AI model for distinguishing between neoplastic and non-neoplastic bile duct lesions, compared with high DSOC experienced endoscopists.

Eligibility criteria:
Inclusion Criteria:

* Patients referred to our center with an indication of DSOC due to suspicion of CBD tumor or indeterminate CBD stenosis.
* Patients who authorized for recording DSOC procedure for this study.

Exclusion Criteria:

* Any clinical condition which makes DSOC inviable.
* Patients with more than one DSOC.
* Low quality of recorded DSOC videos, even for AI model as for the expert endoscopists.
* Lost on a one-year follow-up after DSOC.
* Disagreement between DSOC findings vs. one-year follow-up, even after re-assessment of respective DSOC videos.

Outcomes:
Neoplastic bile duct diagnosis confirmation after one year follow-up
Impact of an Artificial Intelligence Platform on Inappropriate Medication Use in Older Adults
Trial ID: NCT04710615
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-10-05
Completion date: 2024-05-05
Sponsor: University Hospital, Bordeaux
Conditions: Drug-drug Interactions; Geriatric Disease
Interventions: Synapse platform
Locations: CHU de Bordeaux, Hôpital Pellegrin; CHU de Bordeaux; Centre Hospitalier de Libourne - Pôle Gériatrie; CHU de Bordeaux, Hôpital Xavier Arnozan; CHU de Bordeaux, Hôpital Xavier Arnozan
Countries: France; France; France; France; France
Enrollment: 200
Age range: 65 Years - None
Sex: ALL

Summary:
Polypharmacy in the older adults is common and promotes the risk of drug interactions. The hypothesis evoked is that a virtual platform with artificial intelligence applied to the health, and in particular to the good use of the drug, could bring aids to the doctors them of the prescription of drugs. The main objective of this study is to evaluate the impact of geriatric use of the Synapse platform on the frequency of inappropriate medication prescriptions (STOPP criteria) in discharge orders for patients 65 years of age or older hospitalized in geriatric department.

Eligibility criteria:
* Inclusion criteria:

  * Male or female age aged more than 65 years
  * Hospitalization in geriatric department at Bordeaux University Hospital or Libourne Hospital
  * Initial drug prescription with at least 2 active pharmaceutical ingredients
  * Affiliation to French social security
  * Written informed consent signed by participant and investigator
* Exclusion criteria:

  * Adults protected by law
  * Subject included in a clinical trial with an experimental treatment
  * Subject during exclusion period relative to another trial

Outcomes:
Number of patients with decrease in STOPP criteria number between initial prescription and exist prescription.
Effectiveness of Artificial Intelligence Based Educational Approach in Developing Clinical Reasoning Skills in Physiotherapy Students
Trial ID: NCT07010991
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-05-14
Completion date: 2025-09-10
Sponsor: Medipol University
Conditions: Student; Student Engagement; Student Education
Interventions: AI based clinical problem solving education; Traditional Low Back Pain Education
Locations: Medipol University İstanbul
Countries: Turkey (Türkiye)
Enrollment: 40
Age range: None - None
Sex: ALL

Summary:
In the context of this study, the 2nd and 3rd grade undergraduate students in Medipol University, Department of Physiotherapy and Rehabilitation, will taken into an education program to increase and assess their theoretical knowledge level, clinical decision-making skills and attitudes towards interactive learning with artificial intelligence by applying the ChatGPT-supported PBL module and the results will be compared with traditional teaching methods.

Eligibility criteria:
Inclusion Criteria:

* Being an undergraduate student
* Being voluntary to attend to the courses

Exclusion Criteria:

* Not attending to the sessions on time
* Being a 4th grade student who already took and learned the process of clinical problem solving
* Want to recruit to study during the study process

Outcomes:
Theoretical knowledge test; Mini Clinical Evaluation Exam (Mini-CEX)
The Accuracy of Computer Aided Detection of Second Mesio-buccal Canal of Maxillary First Molars on CBCT Images Using Deep Learning Model (Artificial Intelligence): Diagnostic Accuracy Study
Trial ID: NCT05340140
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-05
Completion date: 2023-10
Sponsor: Cairo University
Conditions: Artificial Intelligence
Interventions: deep learning model
Locations: Faculty of dentistry cairo university
Countries: Egypt
Enrollment: 50
Age range: 18 Years - None
Sex: ALL

Summary:
CAD systems are computer applications that assist in the detection and/or diagnosis of diseases by providing an unbiased "second opinion" to the image interpreter, aiming at improving accuracy and reducing time for analysis. With the rapid growth of Deep Learning (DL) algorithms in image-based applications, CAD systems can now be trained by DL to provide more advanced capability (ie, the capability of artificial intelligence \[AI\]) to best assist clinicians.

Eligibility criteria:
Inclusion Criteria:

* • CBCT scans showing erupted maxillary 1st molar.

  * Vovel size not exceeding 0.1mm.
  * Maxillary molars showing complete root formation.
  * Carious or Non-carious tooth.

Exclusion Criteria:

* • Maxillary first molars with developmental anomalies, external or internal root resorption, root canal calcification, previous root canal treatment, post restorations, and/or root caries.

  * CBCT images of sub-optimal quality or artifacts / high scatter interfering with proper assessment.

Outcomes:
accuracy of detection of MB2
Assessing the Effectiveness of Large Language Model (LLM)-Enabled Nurse Treatment Planning in 2 Indian Districts: A Pilot Study
Trial ID: NCT07432893
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-01-13
Completion date: 2026-07-31
Sponsor: Sarah Nabia
Collaborators: Liver Foundation, West Bengal; Endless Health
Conditions: Hypertension; Diabete Mellitus; Breathlessness; Fever
Interventions: AI-enabled clinical decision support tool (software) used by nurses; Physician consultation
Locations: Liver Foundation
Countries: India
Enrollment: 672
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this clinical trial is to learn whether AI-enabled, nurse-led treatment planning can improve the quality of clinical reasoning and management compared with standard physician-led care in adult primary care patients (≥18 years) presenting with hypertension, diabetes mellitus, fever, breathlessness, or musculoskeletal pain in rural and semi-urban India.

The main questions it aims to answer are:

* Does a nurse + large language model (LLM) consultation achieve non-inferior clinical quality scores compared with a standard doctor consultation?
* Is AI-assisted nurse-led care acceptable and satisfactory to patients in primary healthcare settings? Researchers will compare nurse + LLM-led consultations with physician-led standard-of-care consultations within the same participant to see if the AI-enabled nurse model delivers comparable or improved clinical reasoning and treatment planning.

Participants will:

* Receive two sequential consultations for the same visit (one with a nurse using an AI tool and one with a physician, order randomized).
* Have both consultations audio recorded for blinded clinical quality assessment.
* Complete a brief exit survey on communication, trust, and satisfaction after the AI-assisted nurse consultation.

Eligibility criteria:
Inclusion Criteria:

1. Adults aged ≥18 years
2. Presenting to participating primary care facilities in study sites
3. Meeting criteria for at least one of the following conditions or symptoms:

   * Hypertension: Known diagnosis
   * Diabetes mellitus: Known diagnosis or laboratory evidence (HbA1c ≥6.5%, fasting blood glucose ≥126 mg/dL, or post-prandial glucose ≥200 mg/dL)
   * Fever: Presenting as chief complaint
   * Breathlessness: Presenting as chief complaint, without evidence of fever
   * Musculoskeletal pain: Presenting as chief complaint, without evidence of fever
4. Able and willing to provide written informed consent
5. Willing to participate in two sequential consultations and complete an exit survey

Exclusion Criteria:

1. Inability to provide informed consent due to cognitive impairment (e.g., dementia or intellectual disability)
2. Medical instability or condition requiring immediate emergency referral
3. Prior participation in the study during an earlier visit

Outcomes:
Clinical Quality of Consultation (Clinical Management and Clinical Reasoning Score)
Effect of a Deep Learning-based Bile Duct Scanning System on the Diagnostic Accuracy of Common Bile Duct Stones During Examination by Novice Ultrasound Endoscopists: a Single-center, Tandem, Randomized Controlled Trial
Trial ID: NCT05381064
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-06-01
Completion date: 2024-01-01
Sponsor: Renmin Hospital of Wuhan University
Conditions: Common Bile Duct Stones
Interventions: artificial intelligence assistance system
Enrollment: 184
Age range: 18 Years - None
Sex: ALL

Summary:
The bile duct scanning system based on deep learning can prompt endoscopists to scan standard stations and identify bile ducts and stones in real time. The purpose of this study is to evaluate the effectiveness and safety of the proposed deep learning-based bile duct scanning system in improving the diagnostic accuracy of common bile duct stones and reducing the rate of missed gallstones during bile duct scanning by novice ultrasound endoscopists in a single-center, tandem, randomized controlled trial

Eligibility criteria:
Inclusion Criteria:

1. Males and females aged 18 years and older who are suspected of having common bile duct stones at intermediate to low risk, where intermediate-risk patients are those with normal liver function but with abdominal ultrasound suggestive of bile duct dilatation, and low-risk patients are those with normal abdominal ultrasound and liver function but whose physicians still suspect common bile duct stones;
2. Able to read, understand and sign an informed consent;
3. The investigator believes that the subjects can understand the process of the clinical study, are willing and able to complete all study procedures and follow-up visits, and cooperate with the study procedures.

Exclusion Criteria:

1. Patients at high risk of common bile duct stones. High-risk patients are those with common bile duct stones detected by abdominal ultrasound, patients with manifestations of cholangitis or hospitalized patients with a history of gallbladder stones with pain, bile duct dilatation and jaundice;
2. Have drug or alcohol abuse or mental disorder in the last 5 years;
3. Pregnant or lactating women;
4. Altered anatomy due to previous history of upper gastrointestinal surgery;
5. Patients with advanced tumors resulting in abnormal upper gastrointestinal anatomy;
6. High-risk diseases or other special conditions that the investigator considers the subject unsuitable for participation in the clinical trial.

Outcomes:
Accuracy of diagnosis of common bile duct stones in patients with low and intermediate risk by novice combined with AI-assisted and expert
Performance Comparison of Large Language Models in TAP Block Ultrasound Interpretation: A Double-Blind Prospective Study
Trial ID: NCT07261436
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-01-15
Completion date: 2026-05-01
Sponsor: Kanuni Sultan Suleyman Training and Research Hospital
Conditions: Artificial Intelegence
Interventions: Gemini 2.5 Pro Evaluation; ChatGPT-5.1 Evaluation
Locations: Health Science University İstanbul Kanuni Sultan Süleyman Education and Training Hospital
Countries: Turkey (Türkiye)
Enrollment: 40
Age range: 18 Years - 85 Years
Sex: ALL

Summary:
The goal of this study is to learn how accurately two artificial intelligence (AI) models, Gemini 2.5 Pro and ChatGPT-5.1, can interpret ultrasound videos of the Transversus Abdominis Plane (TAP) block, a regional anesthesia technique used for pain control after surgery.

The main questions this study aims to answer are:

How accurately can each AI model identify anatomical structures on TAP block ultrasound videos? Can the AI models correctly evaluate the spread of local anesthetic and determine whether the block is successful? How closely do the AI models' answers match the evaluations of expert anesthesiologists? No additional procedures will be performed on patients. TAP blocks will be done as part of routine clinical care, and the ultrasound videos will be recorded and de-identified.

Participants will not need to do anything extra for the study. Experienced anesthesiologists will review the videos and provide expert answers. The AI models will be given the same videos and asked the same questions. A second expert, who does not know which answers came from humans or AI, will compare all responses.

The results will help researchers understand whether advanced AI systems can safely support clinicians in interpreting ultrasound-guided regional anesthesia procedures and improve education and decision-making in anesthesia practice.

Eligibility criteria:
Inclusion Criteria:

* Adults aged 18-85 years
* ASA I-III physical status
* Undergoing elective surgery with a lateral TAP block performed as part of routine anesthesia care
* Complete ultrasound-guided block procedure recorded on video
* Able to provide written informed consent

Exclusion Criteria:

* Unsuccessful or incomplete TAP block procedure
* Poor-quality ultrasound video (needle tip or anesthetic spread not visible)
* Missing demographic or clinical data
* Withdrawal of consent at any time

Outcomes:
Anatomical Interpretation Accuracy