moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Effect of AI Chatbot-Assisted Case-Based Learning on Clinical Reasoning in Occupational Therapy Students: A Post-Test Only Controlled Trial in Parkinson's Disease Education
Trial ID: NCT07045077
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-07-01
Completion date: 2025-07-03
Sponsor: Ceyhun Türkmen
Conditions: Parkinson Disease; Occupational Therapy Students; Clinical Reasoning; Neurologic Rehabilitation
Interventions: AI Chatbot-Assisted Case-Based Learning; Traditional Case-Based Learning with Standard Materials
Locations: Faculty of Health Sciences
Countries: Turkey (Türkiye)
Enrollment: 25
Age range: 20 Years - 23 Years
Sex: ALL

Summary:
This study aims to examine whether using an artificial intelligence (AI) chatbot can enhance occupational therapy students' learning during a case-based activity focused on Parkinson's disease. The research compares two groups of students: one using traditional learning materials, and another using both traditional resources and a conversational AI chatbot. Students in both groups work in teams to analyze the same clinical case and propose assessment and treatment strategies for a hypothetical patient. The main purpose of the study is to evaluate whether the AI chatbot helps improve students' performance in three learning domains: cognitive (knowledge and understanding), affective (empathy and attitudes), and psychomotor (planning and action skills). Students' performance is assessed through a structured written examination. The hypothesis is that students who use the AI chatbot will achieve higher scores, especially in the cognitive and psychomotor domains, compared to those who rely on traditional methods only. The study also examines how students interact with the chatbot and whether they use it to support deeper clinical reasoning. By exploring the role of AI in occupational therapy education, this research seeks to inform future teaching strategies and support the thoughtful integration of digital tools in health professions training.

Eligibility criteria:
Inclusion Criteria:

* Currently enrolled as an undergraduate student in an occupational therapy program
* Registered in the "Neurological Rehabilitation" course during the study semester
* Aged between 20 and 23 years
* Provided written informed consent to participate in the study

Exclusion Criteria:

* Previously completed the "Neurological Rehabilitation" course in a prior semester
* Refused or failed to provide informed consent
* Participated in a similar case-based learning study within the past 6 months

Outcomes:
Total Post-Test Score (0-24 points)
Prospective Validation of an AI-Driven Ultrasound-Based Method for Estimating Hepatic Steatosis Using MRI-Derived Fat Fraction as Reference in Pediatric Metabolic Dysfunction-Associated Steatotic Liver Disease
Trial ID: NCT07265011
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-12-31
Completion date: 2025-09-23
Sponsor: Jae Won Choi
Conditions: MASLD; Pediatric
Interventions: Ultrasound and MRI
Locations: Seoul National University Hospital
Countries: South Korea
Enrollment: 50
Age range: 8 Years - 18 Years
Sex: ALL

Summary:
The purpose of this study is to validate an artificial intelligence (AI)-based algorithm that estimates hepatic steatosis using ultrasound (US) B-mode images in pediatric participants with metabolic dysfunction-associated steatotic liver disease (MASLD). The MRI proton density fat fraction (MRI-PDFF) serves as the reference standard for hepatic fat quantification.

Eligibility criteria:
Inclusion Criteria:

* participants aged 8 to 18 years clinically indicated for liver ultrasound examination to evaluate hepatic steatosis
* participants with suspected or known metabolic dysfunction-associated steatotic liver disease (MASLD)
* able to understand the study purpose and provide written informed consent (from both participant and legal guardian).
* agree to undergo same-day liver MRI examination in addition to the ultrasound

Exclusion Criteria:

* unable to cooperate with imaging procedures
* parent or legal guardian unable to understand the study explanation
* contraindications to MRI
* determined by the investigator to be otherwise unsuitable for participation after consultation

Outcomes:
Agreement Between AI-Predicted Ultrasound Fat Fraction (AI-USFF) and MRI Proton Density Fat Fraction (MRI-PDFF)
Acceptability and Efficiency of Screening for Diabetic Retinopathy in Pharmacies With Artificial Intelligence Enhanced Retinophotography : a French Pilot Study
Trial ID: NCT05452993
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-12-16
Completion date: 2024-12-31
Sponsor: University Hospital, Caen
Conditions: Diabetic Retinopathy; Artificial Intelligence; Diabetes Mellitus
Interventions: Analysed Retinophotography
Locations: Caen University Hospital
Countries: France
Enrollment: 64
Age range: 18 Years - None
Sex: ALL

Summary:
Diabetic retinopathy is frequent, potentially severe with visual threat, health costly and represents a major public health problem. However, screening compliance for retinopathy remains too low in France, approximately 40% patients with diabetes laking diabetic retinopathy screening for at least 2 years.

DIABeyeIA is a prospective pilot study evaluating the effectiveness and acceptability of diabetic retinopathy screening in 11 pharmacies in Normandy (north of France) using a non-mydriatic portable retinophotograph enhanced by artificial intelligence software. The main goal of this work is to evaluate a potential increase rate of diabetic retinopathy screening, compared to the actual rate (64% in France). Secondary goals are faisability, satisfaction and economical considerations for implementation of such a new screening program.

Eligibility criteria:
Inclusion Criteria:

* Adult patients with diabetes
* Ongoing diabetes treatment (oral hypoglycaemic agents, injectable GLP-1 receptor agonists and insulin)
* Regular customer of the pharmacy (at least 3 previous visits)
* Informed consent to participate to the study

Exclusion Criteria:

* Patient unable to read, write or give consent
* Patient refusing to share results with their general practitioner or ophthalmologist

Outcomes:
diabetic retinopathy screening rate
AI Evaluation of COVID-19 Sounds
Trial ID: NCT05115097
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-09-20
Completion date: 2022-12
Sponsor: IRCCS San Raffaele
Conditions: COVID-19; COVID-19 Respiratory Infection; Cough; Breathing Sound; Breathing, Mouth
Locations: San Raffaele Hospital and Scientific Institute
Countries: Italy
Enrollment: 100
Age range: 18 Years - None
Sex: ALL

Summary:
In this study the investigators record sounds of voice, breaths and cough of subjects who tested positive for COVID19. The investigators then feed these sounds into an artificial intelligence and see if it can learn to recognise features to make COVID19 diagnosis from these sounds in order to avoid to use swabs to test the general population.

Eligibility criteria:
Inclusion Criteria:

* Adults who tested positive for SARS-CoV-2 currently admitted to hospital at the study site
* Adults who tested negative for SARS-CoV-2 and are admitted for any respiratory condition (eg COPD or asthma flare-up, pneumonia..)
* Healthy volunteers

Exclusion Criteria:

* Patients under the age of 18.
* Patients unable to read in Italian.
* Patients unable to give informed consent to participate.
* Patients requiring life support (including, but not limited to, mechanical cardiac support, ventilation, etc.)
* Patients who are pregnant

Outcomes:
Accuracy evaluation
Voice Analysis Using Artificial Intelligence to Detect Changes in Pulmonary Arterial Pressure in Patients With Heart Failure and an Implanted Pressure Sensor
Trial ID: NCT07443670
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-12-12
Completion date: 2026-09
Sponsor: Noah Labs
Conditions: Heart Failure
Interventions: Daily Voice Recording
Locations: University of California, San Francisco (UCSF); BG Klinikum Unfallkrankenhaus Berlin, Dept. of Cardiology; University Hospital Frankfurt, Dept. of Cardiology and Angiology
Countries: United States; Germany; Germany
Enrollment: 60
Age range: 18 Years - None
Sex: ALL

Summary:
VAPP-HF is a prospective, multi-center, observational study assessing whether daily voice recordings analyzed by a machine learning algorithm can detect changes in pulmonary arterial (PA) pressure in heart failure patients with implanted PA pressure sensors (e.g., CardioMEMS, Cordella). Patients across three sites in Germany and the United States provide daily voice recordings via a mobile app for 12 weeks while continuing standard PA pressure monitoring and heart failure care. Voice data is analyzed retrospectively after study completion; no clinical decisions are based on voice analysis during the study. The primary endpoint is the sensitivity and specificity of the AI-based voice analysis in detecting PA pressure changes at defined thresholds.

Eligibility criteria:
Inclusion Criteria:

* Age 18 years or older
* Successful implantation of a PA pressure sensor and monitored by a participating study center
* Willingness to record a short predefined text daily for 3 months using a smartphone or tablet
* Ability to comfortably read aloud the study passage in English or German
* Written informed consent obtained

Exclusion Criteria:

* Pregnant, breastfeeding, or unwilling to practice birth control during participation
* Condition that in the opinion of the investigator would compromise patient safety or data quality
* Pathological voice changes due to surgery or injury
* Planned invasive cardiac procedures during the study period
* COPD requiring home oxygen therapy
* Chronic kidney disease requiring dialysis
* Cognitive dysfunction limiting ability to perform daily voice recording
* Inability to read English or German
* Physical inability to use the recording device

Outcomes:
Sensitivity of AI Voice Analysis in Detecting PA Pressure Changes
USE OF ARTIFICIAL INTELLIGENCE IN THE BACK-OFFICE OF A SPECIALIZED MEDICAL CLINIC FOR PULMONARY FIBROSIS (LLM-ILD)
Trial ID: NCT06852794
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-05-01
Completion date: 2026-02-01
Sponsor: Fondazione Policlinico Universitario Agostino Gemelli IRCCS
Conditions: Interstitial Disease
Interventions: Survey
Enrollment: 100
Age range: 18 Years - None
Sex: ALL

Summary:
The present study aims to evaluate the usefulness of artificial intelligence (AI) in the back-office of an ILD (Interstitial Lung Disease) clinic.

Eligibility criteria:
Inclusion Criteria:

\- Patients attending ILD clinic of the IRCCS Fondazione Policlinico A. Gemelli.

Exclusion Criteria:

* Patients unable or unwilling to sign the informed consent
* Patients who are senders of one of the emails chosen for the questionnaire
* Patients with manifest inability to fill out the questionnaire

Outcomes:
Variation of VAS scores of clarity of the message in questionnaires administered at T1 and at T2; Variation of VAS scores of empathy conveyed in questionnaires administered at T1 and at T2
White Matter Hyperintensities Subtypes in Cerebral Small Vessel Disease : 7 Tesla Ultra-high Resolution Imaging MRI
Trial ID: NCT04298866
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-03-04
Completion date: 2023-06-04
Sponsor: Assistance Publique - Hôpitaux de Paris
Conditions: Cerebral Small Vessel Diseases
Interventions: Experimental Arm
Locations: Hopital Lariboisière
Countries: France
Enrollment: 100
Age range: 18 Years - None
Sex: ALL

Summary:
Cerebral small vessel diseases (SVD) are a very frequent group of disorders all characterized by alterations of the structure and/or function of small arteries, veins and capillaries. In these disorders, brain tissue lesions accumulate years before the occurrence of clinical symptoms which can be devastating such as stroke, cognitive disturbances and gait disorders. So far, chronic hypoperfusion was considered to be responsible for the accumulation of such lesions. However, recent results have suggested that the lesions underlying white matter hyperintensities (WMH), the most common MRI marker of SVD visible on conventional MRI in quite every subject with SVD long before the occurrence of clinical events, may depend on the considered brain area and may correspond to various mechanisms. Some WMH may even be associated with less severe clinical manifestations.The aim of the present study is to identify different types of WMH by studying 100 patients with different forms of SVD with the most advanced MRI (including ultra-high-resolution imaging at 7 Tesla, new diffusion protocol, sodium MRI, contrast-enhanced angiography and relaxometry and post-processing techniques), and post-processing techniques (machine learning, deep learning, artificial intelligence).

Eligibility criteria:
Inclusion Criteria:

* Subjects or patients with MRI defined cerebral small vessel disease including different extents of white matter hyperintensities, presumably related to hypertension (30 patients), cerebral amyloid angiopathy (30 patients), CADASIL (30 patients) or any other monogenic form of cerebral small vessel disease (HTRA1 AD, COLIVA1… 10 patients)
* Age ≥ 18 years
* No dementia (MMSE \> 24 and absence of dependence in daily activities)
* No disability (modified Rankin's scale \< 2)
* No history of severe allergic reaction, in particular to gadolinium infusion
* No history of severe asthma
* No renal insufficiency (clearance \< 60 ml/mn/1.73 m2)

Exclusion Criteria:

* Contraindications to MRI
* Standard MRI of bad quality due to movement artefacts
* Dementia or disability
* Patient without affiliation to the French social security

Outcomes:
Percentage of patients with a different form of white matter hyperintensities (WMH)
Zesty Exercise System for Therapeutic Engagement
Trial ID: NCT07382674
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-02
Completion date: 2028-02
Sponsor: Emory University
Collaborators: Georgia Clinical & Translational Science Alliance
Conditions: Parkinson Disease
Interventions: ZEST-E Robot-Assisted Exercise Program
Locations: Emory University, 57 Executive Park S NE, Ste 200
Countries: United States
Enrollment: 46
Age range: 40 Years - None
Sex: ALL

Summary:
This study will evaluate exercise delivered through the Zesty Exercise System for Therapeutic Engagement (ZEST-E) for people with Parkinson's disease when sessions are monitored either in person or remotely. Participants will complete robot-guided exercise three times per week for three weeks. The study will assess feasibility, retention, safety, acceptability, and tolerability of remotely monitored ZEST-E and will measure changes in functional performance using the Standing Forward Reach test and the 30-Second Chair Stand test. These outcomes reflect range of motion and lower-body strength targeted by the exercise program.

Eligibility criteria:
Inclusion Criteria:

-Older adults with mild to moderate PD.

Exclusion Criteria:

* Acute medical illness requiring hospitalization;
* Under 40 years old;
* Uncontrolled congestive heart failure;
* History of stroke in the past three years;
* Inability to perform study procedures;
* Medical or physical conditions that would preclude participation (e.g., severe arthritis or mobility problems, uncontrolled hypertension or diabetes, renal failure, history of angina with activity);
* On medications that could adversely affect cognition, e.g.: antipsychotics, opioids, stimulants, chemotherapy;
* Psychotic disorders;
* Confounding neurologic conditions (e.g., active central nervous system opportunistic infections, seizure disorders, head injury with loss of consciousness \>30 minutes, intracranial neoplasms, stroke with neurological or neuropsychiatric sequelae);
* Substance Use Disorder, Major Depressive and Generalized Anxiety Disorders within six months of evaluation;
* Hohn \& Yahr \> 3
* MoCA \<17

Outcomes:
Standing Forward Reach (SFR) Test; 30-Second Chair Stand Test
The Development and Study of a 3D-Printed Model for Surgical Planning of Lung Cancer with Realistic Subsegmental Boundaries and Complete Tissue Structures
Trial ID: NCT06507774
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-01-01
Completion date: 2024-01-01
Sponsor: Ke Zhang
Conditions: Lung Cancer
Interventions: 3D Reconstruction Technology; No 3D reconstruction is used
Locations: Affiliated Hospital of Hebei University
Countries: China
Enrollment: 150
Age range: 18 Years - None
Sex: ALL

Summary:
This project focuses on the development of a precision multi-organ surgical planning model aided by artificial intelligence (AI), computer-aided design (CAD), multi-material 3D printing, and multimodal 3D modeling. The integration of these advanced technologies aims to enhance the accuracy and efficiency of preoperative planning for complex surgical procedures involving multiple organs. By leveraging AI and CAD, the project seeks to create detailed, patient-specific 3D models that incorporate various imaging modalities. These models will be used to guide multi-material 3D printing, producing highly accurate replicas of anatomical structures. The ultimate goal is to improve surgical outcomes by providing surgeons with precise and comprehensive tools for planning and executing intricate surgeries.

Eligibility criteria:
Inclusion Criteria:

* Patients diagnosed with lung cancer
* Complete clinical case data;
* All patients underwent surgical treatment;
* Voluntarily participate in the study and sign the informed consent.

Exclusion Criteria:

* Severe comorbidities, such as uncontrolled heart disease or diabetes
* History of major surgeries or other interventions
* Unwillingness to participate in the study

Outcomes:
Operation Time; Blood Loss; Postoperative Complications
Fibrotic Interstitial Lung Disease Early Recognition and Strategic Therapy Study in China
Trial ID: NCT06655090
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2021-12-01
Completion date: 2027-11-30
Sponsor: Dai Huaping
Collaborators: The First Affiliated Hospital of Guangzhou Medical University; Nankai University; Jilin University; Shanghai Pulmonary Hospital, Shanghai, China; Shanghai Chest Hospital; Institute of Basic Medical Sciences CAMS; Tongji Hospital; National Institute for Occupational Health and Poison Control; Infervision Medical Technology Company Limited
Conditions: Interstitial Lung Disease (ILD)
Locations: China-Japan Friendship Hospital
Countries: China
Enrollment: 10000
Age range: None - None
Sex: ALL

Summary:
This project aimed to: 1) construct a cohort of no less than 10000 cases of f-ILD (including pneumoconiosis ≥3000 cases) with continuous regular follow-up to reveal the clinical phenotypes closely related to the development, progression and prognosis of pulmonary fibrosis; 2) systematically evaluate the safety and effectiveness of frozen lung biopsy, surgical lung biopsy/thoracoscopic lung biopsy and other techniques, and to optimize the histological diagnosis method of f-ILD; 3) construct a set of artificial intelligence (AI) evaluation system for quantitative evaluation of pulmonary fibrosis and its severity, and develop application software; 4) excavate and verify important molecular targets for the formation of pulmonary fibrosis and identify biomarkers; 5) combined with clinical phenotype, imaging, pathology and biomarkers to establish f-ILD early recognition and progress model, intervention strategies, guidelines and consensus, and applicated nationwide.

Eligibility criteria:
Inclusion Criteria:

* Diagnosed as ILD

Exclusion Criteria:

* Lack of chest CT
* Patients refused to participant

Outcomes:
Clinical diagnostic protocol of ILD tissue biopsy; Predict model; Severity of fibrosis in HRCT assessed by AI system in patients with ILD; F-ILD cohort; Important molecular targets and biomarkers identified by multi-omics
Development of Machine Learning Models for the Prediction of BMI and Complications After Bariatric Surgery (CABS-Study)
Trial ID: NCT05710913
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2023-01-05
Completion date: 2025-12-31
Sponsor: Dr. Med Anas Taha
Collaborators: University of Basel; GZO Hospital, Zürich, Switzerland
Conditions: Body Weight; Post-Op Complication; Anastomosis, Leaking; Hiatal Hernia; GERD; Internal Hernia; Mesenteric Hernia; Intussusception; Diabetes Mellitus, Type 2; Sleep Apnea, Obstructive
Locations: University of Basel - Department of Biomedical Engineering
Countries: Switzerland
Enrollment: 10000
Age range: 18 Years - None
Sex: ALL

Summary:
This Study aims to develop machine learning models with the ability to predict patients' BMI and complications after Bariatric Surgery (CABS-Score).

This Study also aims to develop machine learning models with the ability to predict diabetic (DM II)patients' remission rate after Bariatric Surgery.

The service mentioned above will be publicly available as a web-based application

Eligibility criteria:
Inclusion Criteria:

* Patients undergoing bariatric surgery
* Patients \>18 years

Exclusion Criteria:

* Patients \<18 years
* Patients who cannot be followed up on for more than 6 month after surgery
* Patients who are unable to provide informed approval to participate according to each centre's rules will be excluded.

Outcomes:
Complication after surgery; Body mass Index (KG/m2) after surgery; Diabetes mellitus type II remission rate postoperatively
Revolutionizing Clinical Education for University Students: The Impact of AI-Driven Smart Learning Platforms on Reflective Thinking, Emotional Competence, and Clinical Embeddedness: An RCT Study
Trial ID: NCT06848803
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-02-13
Completion date: 2025-05-30
Sponsor: Alexandria University
Conditions: Artificial Intelligent
Interventions: AI- driven simulation platform
Locations: Faculty of Nursing, Alexandria university
Countries: Egypt
Enrollment: 140
Age range: 19 Years - 23 Years
Sex: ALL

Summary:
The rapid advancement and integration of Artificial Intelligence (AI) into various facets of modern life have ushered in a new era of technological possibilities, particularly within the realm of education. AI-driven smart learning platforms (SLPs) are emerging as powerful tools with the potential to revolutionize how individuals learn and develop crucial skills. These platforms, characterized by adaptive learning algorithms, personalized feedback mechanisms, and intelligent tutoring systems, offer a dynamic and interactive learning experience that traditional methods often struggle to replicate. This exploration delves into the multifaceted impact of AI-driven SLPs on three key dimensions of professional development: reflective thinking, emotional competence, and clinical embeddedness. Understanding the complex interplay between these elements and the influence of AI is crucial for shaping the future of education and professional training (Ali et al., 2023).

Eligibility criteria:
Inclusion Criteria:

students enrolled in psychiatric mental health nursing department and Currently participating in clinical rotations.

Willingness to provide informed consent.

Exclusion Criteria:

Students with significant cognitive impairments that may affect their ability to participate in the study.

Outcomes:
Tool I: Reflective Thinking Scale