moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
An Intelligent Voice-interactive Tailored Communication System (ScreenTalk) for Enhancing Colorectal Cancer Screening Among First-degree Relatives of Patients: a Randomized Type I Hybrid Effectiveness-implementation Trial
Trial ID: NCT06710860
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-07
Completion date: 2026-07
Sponsor: Sun Yat-sen University
Collaborators: National Natural Science Foundation of China
Conditions: Colonoscopy; Colorectal Cancer; First-degree Relatives
Interventions: The intelligent voice-interactive tailored communication system-based intervention
Locations: Sun Yat-sen University Cancer Center; Sun Yat-sen Memorial Hospital; The Third Affiliated Hospital of Sun Yat-sen University
Countries: China; China; China
Enrollment: 314
Age range: 40 Years - 75 Years
Sex: ALL

Summary:
Participants in intervention group will receive the ScreenTalk intervention consisting of 3 module: measurement module, algorithmic tailoring module, and open dialogue module. The primary outcome, CRC screening uptake will be verified by medical records; and health belief will be measured at baseline, 3, 6, 9 and 12 months.

Eligibility criteria:
Inclusion Criteria:

* Age 40 to 75 years or 10 years before the age at which the relative was diagnosed;
* individuals with one FDR with CRC age \<60 years or with two or more FDRs with CRC at any age;
* have not had a colonoscopy within 5 years;
* access to WeChat themselves or through close family members living in the same household;
* able to read and speak Chinese.

Exclusion Criteria:

* Have history of cancer or inflammatory bowel disease;
* have history of a hereditary syndromes (Lynch syndrome or familial adenomatous polyposis);
* recently participate in another study or previously participated in cancer risk counseling or study;
* with doctor-diagnosed psychiatric illness.

Outcomes:
Participate rate of CRC screening
Real-time Artificial Intelligence System for Detecting Multiple Ocular Fundus Lesions by Ultra-widefield Fundus Imaging: A Prospective Multicenter Study
Trial ID: NCT04859634
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2020-11-01
Completion date: 2022-12-25
Sponsor: Sun Yat-sen University
Collaborators: Shenzhen Eye Hospital; Xudong Ophthalmic Hospital; IKang Physical Examination Center; Beijing Tongren Hospital; Guangdong Provincial People's Hospital; Yangxi General Hospital People's Hospital
Conditions: Artificial Intelligence; Diagnostic Imaging; Abnormality of the Fundus; Diagnostic Screening Programs
Interventions: Taking an ultra-widefield fundus image
Locations: Zhongshan Ophthalmic Center, Sun Yat-sen University
Countries: China
Enrollment: 2000
Age range: None - None
Sex: ALL

Summary:
This prospective multicenter study will evaluate the efficacy of a real-time artificial intelligence system for detecting multiple ocular fundus lesions by ultra-widefield fundus imaging in real-world settings.

Eligibility criteria:
Inclusion Criteria:

All the participants who agree to take ultra-widefield fundus images.

Exclusion Criteria:

1. Patients who cannot cooperate with a photographer such as some paralytics, the patients with dementia and severe psychopaths.
2. Patients who do not agree to sign informed consent.

Outcomes:
Accuracy
Data Science for Decision Support in the HIV Care Cascade
Trial ID: NCT06604663
Status: ENROLLING_BY_INVITATION
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-05-20
Completion date: 2026-10-31
Sponsor: Brown University
Collaborators: Moi University College of Health Sciences; Moi Teaching and Referral Hospital; National Institute of Allergy and Infectious Diseases (NIAID)
Conditions: Human Immunodeficiency Virus; Treatment Adherence; Treatment Compliance; Patient No Show; Patient Engagement; Patient Dropouts; HIV Viremia
Interventions: Activation of the CDSS system
Locations: AMPATH
Countries: Kenya
Enrollment: 80000
Age range: 18 Years - 100 Years
Sex: ALL

Summary:
The goal of this study is to determine whether clinical prediction algorithms derived using statistical machine learning methods can be used to improve patient outcomes in large HIV care programs in sub-Saharan Africa and elsewhere.

There are two main questions to be answered. First, can the prediction algorithms accurately identify those who are at risk for (a) missing scheduled clinic visits and/or (b) treatment failure, evidenced by elevated HIV viral load? And second, can the risk predictions be used in a structured way to (a) improve retention in care and/or (b) reduce the number of patients having elevated viral load? Researchers will develop machine learning prediction algorithms, incorporate the risk prediction information into the electronic health record, provide guidance to clinical health workers on use of the point-of-care interface tools that display risk prediction information, and incorporate feedback from clinic staff to modify and co-develop the protocol for using risk predictions for improving patient outcomes.

They will then compare the proportion of patients having missed visits and longer-term loss to follow up, and the proportion with elevated viral load, between clinics that use the information from the risk prediction algorithms and those that do not.

Eligibility criteria:
Inclusion Criteria:

* The study will include adult patients (age 18 and over) receiving HIV care through the AMPATH program in Eldoret, Kenya. There is not a patient-level enrollment process. The primary endpoints will be summarized at the clinic level (e.g., proportion of patients who keep an appointment within 7 days of the scheduled appointment).

Exclusion Criteria:

* None

Outcomes:
The proportion of scheduled patient visits kept by the patient (1-day); The proportion of scheduled patient visits kept by the patient (7-day); The proportion of patients with suppressed VL among those with measured VL; The proportion of patients with suppressed VL among those with scheduled VL measurement, whether or not that measure was taken.
Development and Validation of a Blood Test for Early Diagnosis of Colorectal Cancer Based on Molecular Markers and Risk Factors Identified With Artificial Intelligence Tools
Trial ID: NCT06889883
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2023-06-06
Completion date: 2025-12-31
Sponsor: ADVANCED MARKER DISCOVERY S.L.
Conditions: Colorectal Carcinoma; Advanced Adenomas (AA)
Locations: Hospital Clinico Universitario Lozano Blesa; Hospital Universitario de Cruces; Policlinica Gipuzkoa Hospital; Hospital Universitario Marques de Valdecilla; Hospital Universitario de Salamanca; Hospital Sant Rafael; Hospital Universitario Vall d' Hebron; MD Anderson Cancer Centre; Hospital Centro Médico de Asturias
Countries: Spain; Spain; Spain; Spain; Spain; Spain; Spain; Spain; Spain
Enrollment: 510
Age range: 18 Years - None
Sex: ALL

Summary:
This is a prospective, multicenter, case-control study. The aim is to develop an optimized algorithm by combining the levels of molecular markers in plasma together with risk factors for developing colorectal cancer and clinical data of patients diagnosed with CRC or AA and healthy individuals.

Eligibility criteria:
Inclusion Criteria:

* Patients over the age of 18 who are able to understand the participant information and sign the informed consent.
* Patients who are going to undergo colonoscopy or surgical resection for tumor (CRC).

Exclusion Criteria:

* Patients who had developed any type of cancer in the 5 years prior to their participation in this study.
* Patients who have received previous chemotherapy or radiotherapy.
* Patients diagnosed with non-advanced adenomas, serrated polyps, Familial Adenomatous Polyposis or Lynch Syndrome.
* Patients with inadequate bowel preparation for colonoscopy.
* Patients who have undergone colonoscopy/polipectomy in the previous 5 years.
* Patients with hemolyzed plasma.

Outcomes:
Clinical Validation
Dissemination of an Adolescent Obesity Prevention Intervention to Louisiana Schools
Trial ID: NCT06335056
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-02-19
Completion date: 2026-04
Sponsor: Louisiana State University and A&M College
Collaborators: Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD); Pennington Biomedical Research Center; Texas A&M University; University of Nevada, Las Vegas
Conditions: Treatment (ProudMe); Waitlist Control
Interventions: ProudMe; Waitlist Control
Locations: LSU Pedagogical Kinesiology Lab
Countries: United States
Enrollment: 480
Age range: 10 Years - 15 Years
Sex: ALL

Summary:
This project aims to test the effectiveness and implementation of an adolescent obesity prevention intervention called ProudMe. The study recruits 480 adolescents from 12 middle schools (cluster-randomized to 6 ProudMe schools vs 6 wait-list control schools) and expect to observe improvements of obesity-prevention behaviors and weight status in the ProudMe group compared to the control. The investigators also collect mixed-methods data from 24 school implementers at the 6 ProudMe schools and expect to observe appropriate levels of adaptation, fidelity, reach, penetration, and sustainability, with manageable time and financial cost.

Eligibility criteria:
Inclusion Criteria:

* Students enrolled in 6th, 7th, or 8th grades in participating schools.
* Students with parental consent and child assent.

Exclusion Criteria:

* Students enrolled in grades other than 6th, 7th, or 8th grades
* Students in non-participating schools.
* Students without parental consent and child assent.
* Students without adequate readiness for physical activity.

Outcomes:
Physical activity: NHANES physical activity recall question, Leisure-Time Exercise Questionnaire, ActiGraph accelerometers; Screen time; Food environment
Metabolomic Evaluation of the Impact of Rocchetta Water on the Skin of Healthy Patients Through the Innovative Method of Artificial Intelligence.
Trial ID: NCT05033795
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-06-29
Completion date: 2021-09
Sponsor: I.R.C.C.S Ospedale Galeazzi-Sant'Ambrogio
Conditions: Skin
Interventions: Water A; Water B
Locations: IRCSS Istituto Ortopedico Galeazzi
Countries: Italy
Enrollment: 48
Age range: 30 Years - 50 Years
Sex: FEMALE

Summary:
Evaluation of changes in skin metabolism after one month of intake of Acqua Rocchetta (water A) vs bicarbonate-calcic mediomineral water (water B) (reprogramming effect on skin metabolism); Evaluation of urinary profiles after intake of Acqua Rocchetta (water A) vs bicarbonate-calcic mediomineral water (water B) and possible relation with skin metabolites.

Eligibility criteria:
Inclusion Criteria:

* healthy female subjects
* in good health without disease with an ICD code in compliance with the World Health Organisation definition (a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity." )
* abstemious
* individuals who agree to sign an informed consent form
* individuals who follow an omnivorous diet

Exclusion Criteria:

* subjects outside the age range considered
* smokers and/or those with chronic or acute diseases coded by the ICD-10 system
* persons who do not consent to sign the informed consent form
* subjects who follow diets that are not omnivorous or with fattening / slimming purposes
* subjects undergoing hormonal or contraceptive hormonal therapies or in general drugs that may alter the hydroelectric balance
* persons with significant alterations in the menstrual cycle (amenorrhoea or dysmenorrhoea).
* use of alcohol.
* pregnancy (verified by self-declaration) and/or breastfeeding
* allergy to the components of Leukopor

Outcomes:
Lipidomics
Evaluation of the Psychosocial Effects of a Trauma-Informed Care-Based Artificial Intelligence-Supported Chatbot in Newly Diagnosed Cancer Patients: A Randomized Controlled Trial
Trial ID: NCT07583069
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-07-01
Completion date: 2027-01-01
Sponsor: Koç University
Collaborators: The Scientific and Technological Research Council of Turkey
Conditions: Cancer; Psychosocial Distress; Trauma and Stress Related Disorders; Anxiety
Interventions: Trauma-Informed AI-Supported Chatbot
Enrollment: 62
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this clinical trial is to evaluate whether a trauma-informed, artificial intelligence-supported chatbot can reduce psychological distress and unmet psychosocial needs in adults newly diagnosed with cancer.:

* Does the chatbot reduce trauma-related distress and anxiety levels compared to standard care?
* Does the chatbot decrease unmet psychosocial needs in patients following a new cancer diagnosis? Researchers will compare the chatbot intervention to standard care to determine whether the chatbot provides additional psychosocial benefit.

Participants will:

* Be randomly assigned to either the chatbot intervention group or the standard care group
* Use the chatbot freely for 12 weeks (intervention group only)
* Complete questionnaires at baseline, 1 month, and 3 months
* Be monitored for any psychological risk indicators during the study period

Eligibility criteria:
Inclusion Criteria:

* Over 18 years of age,
* Able to speak and understand Turkish,
* Without any cognitive, visual, or auditory impairments,
* Diagnosed with cancer within the last month (Stage I, II, and III) (regardless of cancer type),
* Have started active cancer treatment,
* Have access to a digital device and the internet,
* Volunteer to participate in the study will be included.

Exclusion Criteria:

* Patients participating in another simultaneous psychosocial/digital intervention study,
* Patients diagnosed with advanced stage (Stage IV) cancer, in the terminal phase and with metastases,
* Patients diagnosed with schizophrenia, bipolar disorder, substance abuse, depression and anxiety disorders will be excluded.

Outcomes:
Trauma-related distress
A Randomized, Single-blinded, Controlled Trial on the Efficacy of Mammography Screening With Artificial Intelligence - the MASAI Study
Trial ID: NCT04838756
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-04-12
Completion date: 2025-08-12
Sponsor: Region Skane
Collaborators: Unilabs; Norwegian Institute of Public Health
Conditions: Breast Cancer
Interventions: AI screening modality; Conventional screening modality
Locations: Mammography Unit, Unilabs/Skane University Hospital
Countries: Sweden
Enrollment: 100000
Age range: 40 Years - 74 Years
Sex: FEMALE

Summary:
The purpose of this randomized controlled trial is to assess whether AI can improve the efficacy of mammography screening, by adapting single and double reading based on AI derived cancer-risk scores and to use AI as a decision support in the screen reading, compared with conventional mammography screening (double reading without AI).

Eligibility criteria:
Inclusion Criteria:

Women eligible for population-based mammography screening.

Exclusion Criteria:

None.

Outcomes:
Interval-cancer rate
App-based Stress Management: A Pragmatic Randomized Controlled Trial on the Efficacy of the mHealth Application "Harmony"
Trial ID: NCT06744452
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-11-25
Completion date: 2025-03-03
Sponsor: University of Witten/Herdecke
Conditions: Stress; Self Efficacy; Resilience, Psychological; Goal Achievement; Well-Being, Psychological
Interventions: harmony
Locations: Witten/Herdecke University
Countries: Germany
Enrollment: 251
Age range: 18 Years - None
Sex: ALL

Summary:
The purpose of this study is to investigate the efficacy of app-based stress prevention via the harmony application in a prospective, interventive, monocentric, explanatory pilot randomized controlled trial (RCT), with participants' subjective stress experience as the primary endpoint.

Research question: Does usage of the app harmony have a positive impact on the subjective experience of stress of its users? Do usage frequency, intensity or width of content engagement influence the efficacy of the intervention?

Hypothesis: We expect improvement of participants' subjective stress experience, stress-related symptoms, subjective well-being, resilient coping, self-efficacy, life satisfaction and goal-attainment.

Eligibility criteria:
Inclusion Criteria:

* Increased stress level (PSS-10 \&gt;= 19; corresponds to one standard deviation (SD 6.42)) above the mean (PSS-10 = 12.57) in a representative sample of the German population (Klein et al., 2016)
* Fulfillment of technical minimum requirements (internet and smartphone access with suitable operating systems)
* German language skills at a native level or the ability to use the language at at least B level
* Written informed consent to participate in the study after being informed about the study

Exclusion Criteria:

* Inclusion criteria not met.

Outcomes:
Change from baseline stress at 6 weeks and 12 months; Change from baseline stress symptoms at 6 weeks and 12 months
Assessing the Efficacy and Impact of Ambient AI Scribes in Healthcare: A Randomized Controlled Trial
Trial ID: NCT07113938
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-08
Completion date: 2025-08
Sponsor: University of Saskatchewan
Conditions: Use of Ambient AI Scribes; Patient-Phyisican Interaction; Physician Workload; Physician Burnout
Interventions: Ambient AI Scribe
Enrollment: 64
Age range: None - None
Sex: ALL

Summary:
The goal of this clinical trial is to assess the impacts of ambient AI scribes on the workload and burnout in physicians who see patients in a clinic setting at least twice in a week, as well as the impacts on patient-physician interaction.

The main questions it aims to answer are:

* What is the impact of ambient AI scribe use on physician workload and burnout?
* What is the impact of ambient AI scribe use on quality of patient-physician interaction?

Researchers will compare the group of physicians using the ambient AI scribes to the group not using ambient AI scribes to see if there are any significant differences.

Participants randomly assigned to Group A will make use of the AI scribe and participants randomly assigned to Group B will not use any AI scribe for the 10 working day duration of the study. They will be asked to complete a survey assessing workload and burnout immediately prior to the commencement of the study and at the end of each week of the study or 5 full working days for part time physicians. They will also invite their patients to complete a survey assessing their experience after each clinical interaction.

Eligibility criteria:
Inclusion Criteria:

* Physicians from family medicine or any specialty
* Physicians who regularly see patients in a clinic setting at least 2 days per week

Exclusion Criteria:

* Physicians who are planning to leave their practice during the study period

Outcomes:
Physician Workload as Measured Using the NASA Task Load Index; Physician Burnout as Measured Using the MBI - HSS (MP); Quality of Patient-Physician Interaction As Measured Using the CARE Patient Feedback Measure Domain of "Really Listening"
Clinical Validation of Machine Learning Triage of Chest Radiographs
Trial ID: NCT05224479
Status: WITHDRAWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-08
Completion date: 2022-11
Sponsor: Stanford University
Collaborators: Society of Thoracic Radiology
Conditions: Chest--Diseases
Interventions: Traditional workflow triage; Machine learning workflow triage; Random workflow triage
Locations: Stanford University
Countries: United States
Enrollment: 0
Age range: 18 Years - None
Sex: ALL

Summary:
Artificial intelligence and machine learning have the potential to transform the practice of radiology, but real-world application of machine learning algorithms in clinical settings has been limited. An area in which machine learning could be applied to radiology is through the prioritization of unread studies in a radiologist's worklist. This project proposes a framework for integration and clinical validation of a machine learning algorithm that can accurately distinguish between normal and abnormal chest radiographs. Machine learning triage will be compared with traditional methods of study triage in a prospective controlled clinical trial. The investigators hypothesize that machine learning classification and prioritization of studies will result in quicker interpretation of abnormal studies. This has the potential to reduce time to initiation of appropriate clinical management in patients with critical findings. This project aims to provide a thoughtful and reproducible framework for bringing machine learning into clinical practice, potentially benefiting other areas of radiology and medicine more broadly.

Eligibility criteria:
Inclusion Criteria:

* Radiologist at Stanford Hospital and Clinics

Exclusion Criteria:

* None

Outcomes:
Turnaround time
An Integrated System for the Assessment of Carotid Plaque Stability Based on the Artificial Intelligence
Trial ID: NCT04928547
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-07-15
Completion date: 2023-07-15
Sponsor: Second Affiliated Hospital, School of Medicine, Zhejiang University
Conditions: Carotid Plaque Stability
Interventions: Test
Enrollment: 100
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
The project will be properly embedded in the clinical practice landscape to both provide a real-world context for research requirements gathering and research result assessment, and a practical application context from the industrial perspective. The landing is planned for cardiology solutions as carotid plaque assessment that can be value-adding features to tackle the current challenges of stroke prediction. The development of stroke prediction model could accelerate the R\&D process and land to key market ultrasound prototypes/products in an agile way. Collaboration and alignment with key market business and R\&D (China Digital Innovation CDI) through workshops and hackathons will be a unique feature of this agile project.

Eligibility criteria:
Inclusion Criteria:

1. Age is greater than or equal to 18 years old and less than or equal to 80 years old;
2. At least one imaging examination suggests the presence of carotid artery plaque;
3. No allergy and other contraindications to ultrasound contrast;
4. Contrast-enhanced ultrasound examination of carotid artery plaque is required;
5. Volunteer to join this research project and sign informed consent.

Exclusion Criteria:

1. Patients who are unable to coordinate their body position or hold their breath during the ultrasound examination;
2. Patients with severe lack of clinical data;
3. During the follow-up, use drugs for the treatment of carotid plaque except lipid-lowering drugs, antiplatelet drugs, antioxidants, and calcium channel blockers;
4. Follow up those who have lost contact;
5. Ischemic stroke caused by non-carotid artery plaque shedding;
6. Patients with atrial fibrillation and other serious heart diseases;
7. A history of allergy to ultrasound contrast agents.

Outcomes:
Stroke