moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
The Effects of Group Music Therapy on the Emotional Health of Residents in Long-Term Care Facilities: A Pilot Study
Trial ID: NCT07536360
Status: ENROLLING_BY_INVITATION
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-02-18
Completion date: 2027-01-31
Sponsor: Yu Chia Chang
Conditions: Loneliness; Negative Affect; Music Therapy; Long-term Care Facility; Emotion Recognition
Interventions: Group Music Therapy
Locations: China Medical University
Countries: Taiwan
Enrollment: 20
Age range: 65 Years - None
Sex: ALL

Summary:
The goal of this pilot study is to learn whether group music therapy improves the emotional health of residents living in long-term care facilities. It will also examine the feasibility of integrating an artificial intelligence (AI)-based emotion recognition model into routine psychosocial interventions.

The main questions it aims to answer are:

Does group music therapy improve positive affect and reduce negative affect, depression, and loneliness among long-term care residents? Are AI-based facial emotion recognition results consistent with residents' self-reported emotional assessments?

Researchers will use a one-group pretest-posttest quasi-experimental design to evaluate changes before and after a 6-week group music therapy program. The study will also compare subjective questionnaire results with objective facial emotion recognition outputs generated by the PaLI Gemma 2 multimodal model.

Participants will:

Attend one 60-minute group music therapy session per week for 6 weeks Complete emotional health questionnaires before the first session and after the sixth session Be recorded during sessions using a non-invasive camera system for facial emotion analysis Have their questionnaire results compared with AI-based emotion recognition outputs to evaluate consistency and feasibility

This pilot study will provide preliminary evidence regarding both the psychological benefits of group music therapy and the feasibility of applying AI-supported multimodal emotion assessment in long-term care settings.

Eligibility criteria:
Inclusion Criteria:

Aged 65 years or older

Resident of the long-term care facility for at least 3 months

SPMSQ scale error 0-2 questions

Medically stable and able to participate in group activities

Able to communicate and complete questionnaires (with assistance if needed)

Willing to provide written informed consent, including consent for facial image collection

Exclusion Criteria:

Diagnosed with severe dementia

Diagnosed with major psychiatric disorders

Diagnosed with neurodegenerative diseases that significantly impair participation

Severe hearing or visual impairment that prevents engagement in music therapy activities

Participation in other psychological therapy or music therapy programs within the past 3 months

Acute medical condition requiring hospitalization or intensive treatment

Outcomes:
Change in Positive Affect Score on the I-PANAS-SF; Change in Negative Affect Score on the I-PANAS-SF
Evaluation of the Potential Impact of an Artificial Intelligence Solution in Second Reading of Organized Screening.
Trial ID: NCT06479772
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-10-10
Completion date: 2024-03-31
Sponsor: GCS Ramsay Santé pour l'Enseignement et la Recherche
Collaborators: Vivactis M2Research
Conditions: Breast Cancer Female
Locations: Hôpital Privé Jean Mermoz
Countries: France
Enrollment: 55589
Age range: 50 Years - 74 Years
Sex: FEMALE

Summary:
Based on data from a series of patients who underwent screening mammography with a "normal" or "benign" first-reading result. The main objective of the study is to evaluate the possibilities of subtracting from the second reading of breast cancer screening a population of examinations for which the risk of discovering cancer is close to zero, using an AI system (here the MammoScreen™ system).

Eligibility criteria:
Inclusion Criteria:

* Women aged 50 to 74 participating in the organized screening campaign.
* Having had a screening mammogram with a final L1 result of "normal" (ACR 1/ BI-RADS 1) or "benign" (ACR 2/ BI-RADS 2).
* For which a second reading interpretation is available
* Screening mammograms performed during the years 2015 to2019 (In order to have follow-up data (subsequent mammogram or information on the person's future)

Exclusion Criteria:

\- Mammography not performed as part of organized screening

Outcomes:
Percentage of real negatives
Harnessing Health Information Technology to Promote Equitable Care for Patients with Limited English Proficiency and Complex Care Needs
Trial ID: NCT05860777
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-05-01
Completion date: 2024-09-30
Sponsor: Mayo Clinic
Conditions: Artificial Intelligence
Interventions: Complexity score and implementation
Locations: Mayo Clinic Minnesota
Countries: United States
Enrollment: 1923
Age range: 18 Years - None
Sex: ALL

Summary:
This is a pragmatic trial that will measure if the use of AI to identify patients with complex care needs and language barriers, as well as active reaching out to clinicians to offer the use of interpreter services will improve the frequency of interpreter use and reduce the time to first interpreter use

Eligibility criteria:
Inclusion Criteria:

* Adults with limited English proficiency and requiring an interpreter and having complex medical care needs (includes admitted to the ICU, prolonged hospital stay, requiring palliative care)
* Have research authorization

Exclusion Criteria:

\- Do not require an interpreter

Outcomes:
Number of interpreters
Smart Technology Facilitated Patient-centered Venous Thromboembolism Management: A Multicenter Cohort Study
Trial ID: NCT06350344
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-11-01
Completion date: 2027-10-31
Sponsor: Navy General Hospital, Beijing
Conditions: Venous Thromboembolism; Digital Health; Health Education
Interventions: mobile venous thromboembolism application (mVTEA)
Locations: Sixth Medical Center of Chinese PLA General Hospital
Countries: China
Enrollment: 2353
Age range: 18 Years - None
Sex: ALL

Summary:
Smart technologies, such as wearable devices, mobile technologies, and artificial intelligence, are being investigated for use in health management. These technologies have the potential to be applied in disease pre-warning, decision-making support, health education, and healthcare maintenance. They are expected to address the challenges in managing thrombosis, improve access to high-quality medical resources in various regions, and enhance the development of a network for thrombosis rescue and treatment prevention.

The objective of this study is to observe the long-term effect of mobile venous thromboembolism application (mVTEA) based patient-centered management of venous thromboembolism (VTE) on thromboprophylaxis, and establish a foundation of evidence for managing patients with high-risk VTE.

Eligibility criteria:
Inclusion Criteria:

* Inpatients ≥18 years of age at admission;
* At high-risk of VTE at discharge: Padua score ≥4 for medical patients and Caprini score ≥5 for surgical patients;
* Signed informed consent.

Exclusion Criteria:

* Diagnosis of VTE at discharge;
* Mental disorder or combination of other serious diseases leading to incapacity for independent living;
* Inability to use smartphones, computer tablets and other smart devices;
* Being pregnant or breastfeeding;
* Have participated in similar trials or are undergoing other clinical trials.

Outcomes:
VTE-related composite event
Development of a Scoring and Prediction Model for Weaning Success in ARDS Patients Using Ventilation Parameters Combined with Artificial Intelligence and Deep Learning Techniques
Trial ID: NCT06751693
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-12-10
Completion date: 2024-12-24
Sponsor: Bakirkoy Dr. Sadi Konuk Research and Training Hospital
Conditions: Deep Learning; Artificial Intelegence; ARDS (Acute Respiratory Distress Syndrome)
Locations: Bakirkoy Dr Sadi Konuk Research and Training Hospital
Countries: Turkey (Türkiye)
Enrollment: 25000
Age range: 18 Years - None
Sex: ALL

Summary:
This study aims to develop an AI-supported scoring model to optimize the weaning processes of ARDS patients from mechanical ventilation. Retrospective analysis will be conducted on the data of 25,000 patients, focusing on ventilator parameters and hemodynamic variables. The model will be designed to contribute to clinical decision support systems.

Eligibility criteria:
Inclusion Criteria:

* ARDS diagnosis
* Aged 18 years and older
* Intubated and followed by Mechanical ventilation
* Admission on Intensive care unit
* Complete data on clinical support and desicion system

Exclusion Criteria:

* Missing data
* Under 18 years of age
* Followed by non-ARDS conditions
* Terminal status

Outcomes:
Successful Weaning
iToBoS: Intelligent Total Body Scanner for Early Detection of Melanoma - Clinical Data Acquisition Study
Trial ID: NCT05075356
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-09-01
Completion date: 2025-03-30
Sponsor: The University of Queensland
Collaborators: Universitat de Girona; Fundacion Clinic per a la Recerca Biomédica; University of Trieste; Trilateral Research Limited; Robert Bosch Espana Fabrica; IBM Israel - Science and Technology Ltd
Conditions: Melanoma
Interventions: The intelligent total body scanner (iToBoS)
Enrollment: 600
Age range: 18 Years - None
Sex: ALL

Summary:
The (overarching) iToBoS Project involves 18 research partners spanning the European Union (including UK and Israel), and 1 Australian partner. The overall aim is to develop an AI assisted diagnostic platform for the early detection of melanoma.

The Clinical Data Acquisition Study (this study) will recruit 600 participants across 3 international sites (Brisbane, Italy, and Spain). The primary objective is to compare the quality and resolution of conventional dermoscopic images of skin lesions with the full-body images captured by the iToBoS imaging system. Secondary objectives are to collect imaging, clinical and genetic data across the three sites, to create labelled datasets for use in training the iToBoS AI component. Also, to refine and develop a holistic melanoma risk score method to be used for the iToBoS system. Lastly, to assess safety of the iToBoS system.

At study site we will aim to recruit 200 participants stratified by risk (of melanoma) categories (low/normal, high, and ultra-high). Participants will be required to attend 3 study visits (months 0, 6 and 12), for total body imaging with the iToBoS system, and dermoscopic images of individual moles. Genetic research and clinical testing are an optional part of the study.

Eligibility criteria:
Inclusion Criteria:

1. Aged 18 years or older
2. Able to provide written informed consent
3. Willing to attend 3 clinical visits over a 12-month period
4. Willing to provide a genetic sample (optional)
5. Willing to follow the clinical procedures (e.g. no physical restrictions from imaging process)
6. Have a BMI between 18.5 to 40 kg/m2
7. Have a height between 140 - 190 cm
8. Have a thorax height (participant lying face up) of 20 - 45 cm
9. Have an elbow to elbow width (breadth) of 40-50cm

Exclusion Criteria:

1. Have a pacemaker
2. Are pregnant, or planning to become pregnant
3. Any condition in which the investigator's opinion may adversely affect the participant's ability to complete the study, or its measures, or which may pose significant risk to the participant.

Outcomes:
iToBoS image quality assessment
Construction and Evaluation of CDSS Based on AI of Traditional Chinese Medicine Intervention for Dry Eye Syndrome
Trial ID: NCT04806256
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2020-07-11
Completion date: 2022-12
Sponsor: Peking University Third Hospital
Conditions: Dry Eye Syndromes
Interventions: Clinical Decision Support System; Routine intervention
Locations: Peking University Third Hospital
Countries: China
Enrollment: 120
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
This project is based on artificial intelligence (AI) algorithms to screen the effective methods of traditional Chinese medicine intervention for dry eye and its applicable conditions, and optimize the traditional Chinese medicine intervention plan for dry eye through the evaluation of evidence-based medicine and expert consensus, and construct the Clinical Decision support system of traditional Chinese medicine intervention for dry eye, secondly, a real-world prospective cohort study design is adopted, the CDSS system is used in the ophthalmology clinic, and its practical application effect on patients with dry eye is evaluated.

Eligibility criteria:
Inclusion Criteria:

1. Ages 18 to 80 year-old.
2. Meet the diagnostic criteria for dry eye, including ocular surface symptoms and signs.

Exclusion Criteria:

1. There are contradictions in diagnostic information, and the real condition of the disease is doubtful;
2. The absence of necessary observation indicators;
3. Lack of necessary prognostic information;
4. The informed consent is not approved.

Outcomes:
Ocular Surface Disease Index; Meibomain Gland Scale
LLM-Generated Lay Summaries for Brain MRI Reports
Trial ID: NCT07310394
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-03-25
Completion date: 2026-04-30
Sponsor: University Hospital, Lille
Collaborators: Direction Générale de l'Offre de Soins
Conditions: Headache
Interventions: LLM-generated lay summary
Locations: Lille University Hospital
Countries: France
Enrollment: 2727
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this clinical trial is to learn if a summary written by artificial intelligence (AI) helps adults understand brain MRI reports for headaches. The main question it aims to answer is: "Does adding a simple summary help readers correctly understand if a cause for the headache was found in the report?" Researchers will compare standard MRI reports to reports that include an AI-generated explanation to see if the extra summary improves understanding.

Participants will:

Read 6 fictional brain MRI reports online. Answer questions to check if they understood the results. Rate their satisfaction and if they feel they would need to ask a doctor for help.

Eligibility criteria:
Inclusion Criteria:

* Participants of the COMPARE e-cohort

Exclusion Criteria:

* None

Outcomes:
Objective understanding of the report
Artificial Intelligence-based Image Processing Methods to Advance the Characterization of Polycystic Kidney Disease
Trial ID: NCT06688981
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-10-12
Completion date: 2034-10
Sponsor: Mario Negri Institute for Pharmacological Research
Conditions: Autosomal Dominant Polycystic Kidney Disease (ADPKD)
Locations: Clinical Research Centre for Rare Diseases Aldo e Cele Daccò
Countries: Italy
Enrollment: 100
Age range: None - None
Sex: ALL

Summary:
The primary aim of this observational exploratory study is to develop AI-based image processing methods to advance the characterization of Polycystic Kidney Disease using medical images and associated clinical data, including:

1. AI-based fully automatic segmentation techniques for the accurate identification of kidneys, liver, and cysts, with a focus on AI interpretability and robustness;
2. advanced AI-based image processing techniques allowing to identify new imaging biomarkers, including through the use of radiomics, to characterize ADPKD tissue microstructure and therefore stage the disease and monitor and predict disease progression and response to therapy;
3. multiparametric models including image-based radiomic features alongside clinical and laboratory data to stratify ADPKD patients and predict ADPKD progression over time.

The study will also have the secondary aim of validating the novel techniques against gold standard (manual) methods, when available.

Eligibility criteria:
Inclusion Criteria:

* Patients with ADPKD

Exclusion Criteria:

* None

Outcomes:
Image-processing methods
Analysis of Diabetic Retinopathy, Glaucoma and Macular Degeneration Diagnosis Via Digital Fundus Images With Artificial Intelligence
Trial ID: NCT06645964
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-06-01
Completion date: 2023-10-01
Sponsor: Retinow Health Technologies and R&D Industry Joint Stock Company
Conditions: Diabetic Retinopathy; Glaucoma; Macular Degeneration
Locations: Ankara Bilkent Şehir Hastanesi
Countries: Turkey (Türkiye)
Enrollment: 915
Age range: 18 Years - None
Sex: ALL

Summary:
Eye diseases are a major public health problem worldwide and one of the main causes of vision loss. Diseases such as diabetic retinopathy, glaucoma and macular degeneration in particular can lead to serious vision loss and negatively affect quality of life. Early diagnosis of these diseases, determination of appropriate treatment methods and protection of patients' quality of life are of great importance.

In recent years, artificial intelligence (AI) technologies have offered great opportunities for disease diagnosis and management in the medical field. Artificial intelligence algorithms developed for retinal image analysis have become an effective tool in the early diagnosis of eye diseases such as diabetic retinopathy, glaucoma and macular degeneration. Ophthalmic imaging and scanning systems supported by AI technology facilitate the diagnosis of these diseases and contribute to the treatment processes.

Artificial intelligence can provide an effective solution for automatic diagnosis of this disease and prediction of disease progression. Retinow AI was developed to accelerate early diagnosis of these three important eye diseases (diabetic retinopathy, glaucoma, macular degeneration), increase access and reduce costs. This software aims to provide a solution to the shortage of ophthalmologists and the limitations of existing methods. Retinow AI's ability to diagnose these diseases with high sensitivity and accuracy through fundus photographs is being evaluated within the scope of clinical research. According to the hypothesis, the software's accuracy rate can reach 90%, thus speeding up clinical processes and reducing the workload of healthcare personnel. In addition, it is planned to be used as an effective screening tool in regions where ophthalmologists are insufficient.

Eligibility criteria:
Inclusion Criteria:

* Patients with diabetic retinopathy, glaucoma or macular degeneration
* Volunteers with written informed consent form
* Volunteer must be 18 years or older
* Healthy individuals without retinal disorders

Exclusion Criteria:

* Volunteers who do not want to have fundus imaging
* Cases that do not comply with fundus photography for any reason
* Patients with conjunctival and corneal infections,
* People with hereditary or congenital retinal diseases,
* People with cataracts,
* People with uveitis,
* Patients with permanent visual impairment in one or both eyes,
* Patients with correction of + 6D and above - 6D,
* Pregnant woman

Outcomes:
Sensitivity; Specificity; Accuracy
Comparison of Large Language Models and Expert Multidisciplinary Team Decisions in Colorectal Cancer
Trial ID: NCT07045207
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-07-01
Completion date: 2026-06-01
Sponsor: Peking University Cancer Hospital & Institute
Conditions: Colorectal Cancer
Interventions: LLM-MDT
Locations: Peking University Cancer Hospital
Countries: China
Enrollment: 1500
Age range: None - None
Sex: ALL

Summary:
The goal of this observational study is to evaluate the decision-making consistency between large language models (LLMs) and expert multidisciplinary teams (MDTs) in adult patients diagnosed with colorectal cancer who underwent MDT consultation between January 2023 and December 2024.

The main questions it aims to answer are:

How consistent are the treatment decisions generated by LLMs compared to actual MDT decisions? Do different LLMs (e.g., ChatGPT, DeepSeek) show varying levels of agreement with expert recommendations? What clinical factors contribute to differences between AI-generated and human expert decisions? Researchers will compare the AI-generated treatment recommendations with real-world MDT decisions using anonymized patient records to see if LLMs can reliably support clinical decision-making in oncology.

Participants will:

Have their de-identified clinical data (e.g., imaging, pathology, MDT notes) processed through several LLMs Not be contacted or receive any interventions, as this is a retrospective study using existing clinical records only.

Eligibility criteria:
Inclusion Criteria:

* Patients with a histologically confirmed diagnosis of colorectal cancer
* Patients who received multidisciplinary team (MDT) consultation at Peking University Cancer Hospital between January 1, 2023 and December 31, 2024
* Availability of complete clinical records, including(MDT consultation notes, CT or MRI imaging reports, Pathology reports, Outpatient or inpatient medical summaries)

Exclusion Criteria:

* Incomplete or missing medical records related to MDT decision-making
* MDT consultations conducted for non-oncologic purposes (e.g., hernia evaluation, stoma planning)
* Missing critical clinical data such as imaging or pathology reports
* Duplicate or conflicting records that prevent reliable data analysis

Outcomes:
Agreement Between AI-Generated and MDT Treatment Decisions
AI-Driven Multimodal Therapeutic System With Virtual Reality Integration for Anxiety Disorders in Adolescents
Trial ID: NCT07364136
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-07-15
Completion date: 2025-09-15
Sponsor: STEM Egypt
Conditions: Anxiety Disorder of Adolescence
Interventions: Ísland app
Locations: STEM Egypty
Countries: Egypt
Enrollment: 587
Age range: 13 Years - 19 Years
Sex: ALL

Summary:
Many young people in the Middle East face anxiety problems, and social stigma often stops them from getting help. Doctors' usual talks sometimes miss the right diagnosis, and most treatments only target one part of anxiety (thoughts, feelings, or body reactions). This study tested a free mobile app designed to help teens aged 13-19 manage anxiety better on their own or with support.

The app uses artificial intelligence (AI) to chat with users, ask questions based on standard medical guidelines, and suggest a personalized plan. It combines four proven therapy styles:

* Cognitive Behavioral Therapy (CBT) - helps change unhelpful thoughts and behaviors
* Acceptance and Commitment Therapy (ACT) - teaches accepting feelings while focusing on what matters
* Mindfulness-Based Cognitive Therapy (MBCT) - combines mindfulness with ways to handle negative thoughts
* Mindfulness-Based Stress Reduction (MBSR) - uses mindfulness exercises to reduce stress

Users can also write in a private journal, review their past entries to track patterns, join video group sessions with other teens (after a short readiness check to ensure a good fit), and try virtual reality exercises to face fears safely. The app works in many languages, including Egyptian Arabic dialects, keeps chats private and encrypted, and only shares info with a psychiatrist for diagnosis help if needed. It also uses AI to read facial expressions and voice tone for real-time feedback on emotions.

We enrolled 587 teens (some used the app, others did not as a comparison group) and measured how well the app diagnosed anxiety and reduced symptoms.

Eligibility criteria:
Inclusion Criteria:

* Adolescents aged 13 to 19 years (inclusive) at the time of enrollment.
* Presence of anxiety symptoms or diagnosed anxiety disorder
* Access to a compatible smartphone

Exclusion Criteria:

* Age \<13 years or \>19 years
* Severe psychiatric comorbidities (psychosis or active suicidality)
* Intellectual disability or conditions preventing app use

Outcomes:
Change in Anxiety Symptoms (SCAS Total Score); Diagnostic Performance of the AI Chatbot (Sensitivity); Diagnostic Performance of the AI Chatbot (Specificity)