moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Acceptance and Perceived Benefits of Digitalization by Medical Assistants and Other Generational Groups (ANDI-MFA-2): "Trust in AI and Digitalization in Healthcare Across Generational Groups: A Comparative Study of Barriers and Facilitators Toward Equitable Adoption"
Trial ID: NCT07265427
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-11-24
Completion date: 2026-12-30
Sponsor: Jacobs University Bremen gGmbH
Collaborators: Hamburg University of Applied Sciences/Hochschule für Angewandte Wissenschaften Hamburg (HAW Hamburg)
Conditions: Digital Health; Artificial Intelligence (AI)
Locations: Constructor University (formerly known as Jacobs University); HAW Hamburg
Countries: Germany; Germany
Enrollment: 250
Age range: 18 Years - None
Sex: ALL

Summary:
This study aims to investigate differences in perception of barriers and facilitators of digitalization and Artificial Intelligence (AI) usage in healthcare across different generational groups (youth, working-age adults, and seniors). The results will help create practical recommendations for public health projects and consultants to support fair and inclusive use of new digital tools in healthcare. A cross-sectional online survey will be conducted among students at HAW, patients and employees in the rehabilitation center in Oldenburg, and seniors participating in the "Digital im Alter"(DIA) project.

Eligibility criteria:
Inclusion Criteria:

* Age 18 or older
* Belonging to one of the defined participant groups
* Consent to participate in the online survey
* Ability to participate in the survey (e.g., sufficient German or English language skills)

Exclusion Criteria:

* Individuals under 18
* Inability to give informed consent
* Illiteracy

Outcomes:
Trust in digitalization and AI in healthcare; Perceived barriers to adoption of AI and digitalization in healthcare; Perceived facilitators to the adoption of AI and digitalization in healthcare
Continuous Non-Invasive Eye Tracking for the Early Detection of Delirium on the Intensive Care Unit
Trial ID: NCT04589169
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2020-12-07
Completion date: 2021-10-12
Sponsor: Chelsea and Westminster NHS Foundation Trust
Collaborators: Westminster Medical School Research Trust; BMA Foundation for Medical Research
Conditions: Delirium
Locations: Chelsea and Westminster Hospital NHS Foundation Trust
Countries: United Kingdom
Enrollment: 30
Age range: 18 Years - None
Sex: ALL

Summary:
In this trial, the investigators seek to demonstrate the feasibility of a system in continuously detecting 'inattention' as a subset of CAM-ICU in a small representative sample of patients in the Intensive Care Unit (ICU) at Chelsea \& Westminster NHS Foundation Trust.

Eligibility criteria:
Inclusion Criteria:

1. Aged \>18
2. Predicted Delirium as defined by the Early PREdiction of DELIRium in ICU patients (E-PREDELIRIC) score ≥ 20%
3. Expected length of stay ≥ 2 days

Exclusion Criteria:

1. Non-concordant eyes
2. Visual Impairment
3. Dementia
4. Inability for facial recognition and eye tracking to be performed reliably
5. Lack of signed consent form / nominated consultee form
6. In-ability to perform CAM-ICU reliably

Outcomes:
Demonstrate eye gaze correlates with CAM-ICU
Effects of AI-Based Physiotherapy on Mandibular Mobility, Shoulder Function, Whole-Body Physical Function, and Quality of Life in Oral Cancer Patients
Trial ID: NCT06785051
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-02-19
Completion date: 2027-07-31
Sponsor: National Taiwan University Hospital
Conditions: Oral Cancer
Interventions: AI-based physiotherapy; Conventional physiotherapy; Scar management
Locations: National Taiwan University Hospital; Taipei Veterans General Hospital
Countries: Taiwan; Taiwan
Enrollment: 100
Age range: 20 Years - 65 Years
Sex: ALL

Summary:
This study examines the effects of AI-based physiotherapy on oral function, shoulder function, whole-body physical function, and quality of life in oral cancer patients. One hundred patients will be recruited before surgery, chemotherapy, or radiotherapy and randomly assigned to an experimental group receiving 12 weeks of AI-guided physiotherapy or a control group receiving usual care. Both groups will perform jaw, neck, and shoulder exercises, with the experimental group using a mobile app to monitor progress. Assessments at baseline, pre-intervention, and 3 months post-intervention will measure maximal in the maximum interincisal opening (MIO), joint range of motion (ROM), pain, endurance (e.g., 6-minute walk test), upper extremity function, and quality of life.

Eligibility criteria:
Inclusion Criteria:

* Newly diagnosed oral cancer patients who are scheduled to receive cancer-related treatments (e.g., oral cancer surgery, radiotherapy, chemotherapy, or chemoradiotherapy).
* Age between 20 and 65 years.

Exclusion Criteria:

* Could not communicate.
* Had any disorder that could influence movement performance.

Outcomes:
Change from baseline in maximal mouth opening; Change from baseline in shoulder joint range of motion
Feasibility and Safety of Artificial Intelligence-Driven Autonomous Registration in Robotic Navigational Bronchoscopy
Trial ID: NCT07368829
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-03-01
Completion date: 2026-06-30
Sponsor: Ruijin Hospital
Conditions: Lung Nodules; Bronchoscopy; Localization Efficiency
Interventions: AI-driven autonomous registration
Locations: Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine
Countries: China
Enrollment: 20
Age range: 18 Years - None
Sex: ALL

Summary:
This study aims to evaluate the feasibility and safety of an artificial intelligence (AI)-driven autonomous registration technology in robotic navigational bronchoscopy. A total of 20 patients with pulmonary nodules requiring localization will be enrolled. The Langhe Bronchoscopy Robot System equipped with AI-based autonomous registration software will be used. Primary outcomes include the success rate of autonomous registration and the rate of manual intervention during the process. Secondary outcomes encompass registration time, complication rates, and nodule localization success.

Eligibility criteria:
Inclusion Criteria:

* Age ≥ 18 years.
* Radiologically confirmed pulmonary nodules requiring preoperative localization.
* Scheduled for robotic navigational bronchoscopy using the Bronchoscopy Robot System.
* Willing to provide written informed consent.

Exclusion Criteria:

* Severe cardiopulmonary dysfunction (e.g., FEV1 \< 30% predicted).
* Coagulopathy or anticoagulation therapy that cannot be safely interrupted.
* Pregnancy or lactation.
* Inability to tolerate bronchoscopy under general anesthesia.

Outcomes:
Autonomous registration success rate; Manual intervention rate during autonomous registration
Developing an Artificial Intelligence System to Detect Mild Cognitive Impairment and Alzheimer's Disease Dementia Through Self-Figure Drawing: An Innovative Approach
Trial ID: NCT05794451
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-03-20
Completion date: 2026-03-31
Sponsor: University of Haifa
Collaborators: Technion, Israel Institute of Technology
Conditions: Alzheimer Disease; Healthy Aging; Mild Cognitive Impairment
Locations: University of Haifa
Countries: Israel
Enrollment: 3413
Age range: 60 Years - None
Sex: ALL

Summary:
Alzheimer's disease dementia (AD) is a debilitating and prevalent neurodegenerative disease in older adults globally. Cognitive impairment, a hallmark of AD, is assessed through verbal tests that require high specialization, and while accepted as screening tools for AD, general practitioners seldom use them. AD can be diagnosed with expensive, invasive neuroimaging and blood tests, but these are usually conducted when cognitive functioning is already severely impaired. Thus, finding a novel, non-invasive tool to detect and differentiate mild cognitive impairment (MCI) and AD is a prime public health interest. Self-figure drawings (a projective tool in which individuals are asked to draw a picture of themselves), are easy to administer and have been shown to differentiate between healthy and cognitively impaired individuals, including AD. Convolutional Neural Network (CNN) (a type of deep neural network, applied to analyze visual imagery) has advanced to assess health conditions using art products. Therefore, the proposed study suggests utilizing CNN-based methods to develop and test an application tailored to differentiate between drawings of individuals with MCI, AD, and healthy controls (HC) using 4,000 self-figure drawings. This

Eligibility criteria:
Inclusion Criteria:

* Adults aged 60 and above with subtle signs of risk of future cognitive decline, residing in the community or in nursing homes with a minimum of 10 years of education.

Exclusion Criteria:

* Current or past psychiatric illness, the presence of congenital/organic cognitive condition, severe visual or motor impairment, and terminal illness (to avoid the effect of comorbidities).

Outcomes:
Cognition; Cognition for adults diagnosed with Alzheimer's disease; Self-figure drawing -Cognition
Clinic Versus Hotspot Active Case Finding and Linkage to Preventive Therapy (ACF/TPT) Strategy Evaluation for TB: A Cluster-Randomized Crossover Trial
Trial ID: NCT05285202
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-06-01
Completion date: 2026-11
Sponsor: Johns Hopkins University
Collaborators: National Heart, Lung, and Blood Institute (NHLBI); Walimu
Conditions: Tuberculosis, Pulmonary; Mycobacterium Tuberculosis Infection
Interventions: Active TB case finding with linkage to preventive therapy (ACF/TPT)
Locations: Walimu
Countries: Uganda
Enrollment: 150000
Age range: 5 Years - None
Sex: ALL

Summary:
This five-year study will evaluate two strategies for conducting tuberculosis (TB) active case finding (ACF) and linkage to TB treatment or TB preventive therapy (TPT) in peri-urban Uganda. The two strategies differ in the location where ACF activities are performed: A "facility-based" ACF/TPT strategy will perform ACF, plus linkage to TPT, in the immediate vicinity of a large public health facility and will primarily recruit individuals who are attending the health facility, irrespective of TB suspicion or symptoms. Alternatively, a "hotspot-based" strategy will use routine notification data and local expertise to identify local TB hotspots - defined as the geographic areas though to have the highest burden of undiagnosed TB per estimated population. The same infrastructure (personnel, equipment, supplies, etc.) for ACF/TPT will then be placed in those zones for a period of four months at a time, and the general population will be recruited for screening and linkage to TPT.

The two interventions will be compared in a Type 1 hybrid effectiveness-implementation trial with a cluster-randomized, multiple-period crossover design. The study will evaluate whether hotspot-focused ACF/TPT results in a greater number of TB patients diagnosed and linked to care, and a greater number of individuals started on preventive therapy, than facility-based ACF/TPT. Secondarily, it will also compare the two interventions in terms of number of people initiated on TPT, and it will compare TB cases detected in regions performing ACF/TPT (either approach) against cases detected in regions that continue to perform the standard of care.

Eligibility criteria:
Inclusion Criteria:

* Age ≥15 years, OR age 5-14 and a close contact of someone diagnosed with TB,
* Provision of oral informed consent, or, if age \<18 years and not legally emancipated, oral informed assent (if ages 8-17) and parental informed consent (ages 5-17) to participate in the study
* Ability to communicate with study staff in English or Luganda, or availability of a capable interpreter who is acceptable to the participant

Exclusion Criteria:

* On treatment for, or diagnosed with but not yet treated for, active TB

Outcomes:
Pulmonary bacteriologically confirmed TB notifications, hotspot vs facility
Evaluation of the Diagnostic Potential of Artificial Intelligence-assisted Fecal Microbiome Testing for Inflammatory Bowel Disease
Trial ID: NCT05797207
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-04-10
Completion date: 2024-12-31
Sponsor: Istanbul Medipol University Hospital
Collaborators: Izmir Metropolitan Municipality Esrefpasa Hospital; Bozyaka Training and Research Hospital; Tepecik Training and Research Hospital; SB Istanbul Education and Research Hospital; Bursa City Hospital
Conditions: Inflammatory Bowel Diseases; Microbiota; Colonoscopy
Interventions: Artificial Intelligence-assisted Fecal Microbiome Testing; Colonoscopy
Locations: Medipol University Esenler Hospital
Countries: Turkey (Türkiye)
Enrollment: 300
Age range: 18 Years - 70 Years
Sex: ALL

Summary:
The goal of this clinical trial is to evaluate the diagnostic potential of Artificial Intelligence-assisted Fecal Microbiome Testing for the diagnosis of inflammatory bowel disease. The main question it aims to answer is:

• Is Artificial Intelligence-assisted Fecal Microbiome Testing a reliable screening test for inflammatory bowel disease?

Participants will be asked to provide fecal samples to be analyzed with next-generation sequencing techniques.

If there is a comparison group: Researchers will compare the diagnostic performance of AI-assisted Fecal Microbiome Testing with colonoscopy to see the correlation between the results of both interventions.

Eligibility criteria:
Inclusion Criteria:

* being over 18 years of age not to be pregnant To apply with the complaint of chronic diarrhea (4 weeks or more) Not meeting any of the exclusion criteria Signing the voluntary consent form

Exclusion Criteria:

* under 18 years old Pregnant or planning to become Acute diarrhea cases Have another known diagnosis of gastrointestinal disease ( malabsorption of any macronutrient, intestinal resection, celiac disease, etc.)
* Abdominal surgery other than appendectomy or hysterectomy history
* Psychiatric comorbidity
* Chronic disease that will affect the microbiome (cancer, diabetes, cardiovascular disease, liver diseases, neurological diseases, etc.)
* Use of drugs that may affect digestive function (including use in the last 4 weeks), probiotics, narcotic analgesics, lactulose (prebiotics) in the 4 weeks before the study
* Patients taking dietary supplements will not be included in the study.

Outcomes:
The diagnostic accuracy of the AI-assisted fecal microbiome testing in detecting inflammatory bowel disease compared to colonoscopy
Accuracy and Effect of Prescribed Generative AI Patient Education Module Within Breast Oncology Care
Trial ID: NCT07512271
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-05-01
Completion date: 2026-12-31
Sponsor: University of California, San Francisco
Conditions: Breast Cancer; Metastatic Breast Cancer; Carcinoma of the Breast
Interventions: Survey (Participant); OpenEvidence CareConnect; Survey (Provider)
Locations: University of California, San Francisco
Countries: United States
Enrollment: 35
Age range: 18 Years - None
Sex: ALL

Summary:
This study evaluates the safety, accuracy, and impact of an artificial intelligence (AI) tool designed to support patient education in breast cancer care for breast oncology patients under selected physicians care within the University of California, San Francisco breast cancer center and affiliate sites.

Eligibility criteria:
Inclusion Criteria:

1. Females or males ages 18 and over.
2. Patients must have either:

   1. Stage IV breast cancer facing a treatment change and are expected to have follow-up visits at least once every three months, or
   2. Stage I-III breast cancer diagnosed within the past 6 months.
3. Basic computer literacy and regular internet access at home.
4. Able to understand study procedures and to comply with them for the entire length of the study.
5. Ability of individual or legal guardian/representative to understand a written informed consent document, and the willingness to sign it.

Exclusion Criteria:

1. Contraindication to any study-related procedure or assessment.
2. Cognitive impairment that would interfere with tool usage or survey completion

Outcomes:
Mean score of Safety and Risk Assessment (Lead-in and Provider); Mean score of Provider Assessment of AI patient Education Tool Survey (Provider); Mean score of Assessment of Answer Accuracy and Content Appropriateness (Provider)
Implementing Artificial Intelligence (AI) to Prevent Vision Loss From Diabetes
Trial ID: NCT07559292
Status: ENROLLING_BY_INVITATION
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-02-15
Completion date: 2027-02
Sponsor: University of Wisconsin, Madison
Conditions: Vision Disorders; Diabetes
Interventions: AI; Usual Practice
Locations: University of Wisconsin
Countries: United States
Enrollment: 1700
Age range: 22 Years - None
Sex: ALL

Summary:
This pragmatic clinical trial is being conducted to test the effectiveness of AI in improving screening and follow-up eye care compared to usual-care among patients with diabetes across 4 primary care clinics. This is an autonomous AI-based screening to detect diabetic eye disease at primary care visits.

Eligibility criteria:
Clinic Inclusion Criteria:

* Serve at least 267 patients with diabetes during the study period
* No point-of-care screening system in use for diabetic eye disease
* Agree to share limited identifiers data as requested

Patient Inclusion Criteria:

* Age 22 years or older
* Diagnosis of type 1 or 2 diabetes
* No known diabetic eye disease
* No diabetic eye exam in the past 12 months

Exclusion Criteria:

* Have a documented eye exam in the electronic health record within 12 months of the date of the primary care visit.
* Contraindication includes diagnosed with macular edema, severe non-proliferative retinopathy, proliferative retinopathy, radiation retinopathy, or retinal vein occlusion.
* Pregnant

Outcomes:
Proportion of patients who get eye screening in the AI and usual-care arms within 5 months of the recommendation
Impact of Generative Artificial Intelligence on Diagnosing Rheumatoid Arthritis Complications
Trial ID: NCT07301892
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-10-01
Completion date: 2026-06
Sponsor: Guang'anmen Hospital of China Academy of Chinese Medical Sciences
Conditions: Rheumatoid Arthritis (RA; Osteoporosis; Osteoarthritis; Interstitial Lung Disease; Thyroid Diseases; Cardiovascular Diseases; Pulmonary Complications; Sjogren's Syndrome; Liver Disorders; Renal Lesions; Vasculitis; Amyloidosis; Peripheral Neuropathy; Thrombosis; RA Complications
Interventions: Generative AI prediction report for RA complications
Locations: Guang'anmen Hospital of China Academy of Chinese Medical Sciences
Countries: China
Enrollment: 100
Age range: None - None
Sex: ALL

Summary:
Generative AI (GenAI) based on large language models (LLMs) is expected to improve the diagnosis and treatment of autoimmune diseases. We are studying how GenAI may affect the diagnosis of various complications of rheumatoid arthritis (RA). In a retrospective study using RA patients' EHR records, we will quantify physician adoption of GenAI predictions for RA complications and co-existing diseases. In a prospective observational study, we will assess the feasibility of using GenAI predictions as additional clinical information to help physicians make more complete diagnoses of RA complications and co-existing diseases, including complex, uncommon, or rare conditions.

Eligibility criteria:
Inclusion Criteria:

* Patients with an initial diagnosis of rheumatoid arthritis (RA).
* All real-world RA inpatients admitted to our department.
* Admission occurring within the real-world data study period.

Exclusion Criteria:

* Patients subsequently confirmed not to have RA during the study.

Outcomes:
Will physicians adopt GenAI predictions in diagnosing RA complications?
Using a Diffuse Reflectance Spectroscopy Probe In-vivo to Identify Tumour and Non-tumour Tissue in the Gastrointestinal Tract to Aid Margin Assessment
Trial ID: NCT05830292
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-05-20
Completion date: 2024-04-01
Sponsor: Imperial College London
Collaborators: National Institute for Health Research, United Kingdom; Cancer Research UK
Conditions: Gastrointestinal Cancer
Interventions: Diffuse reflectance spectroscopy (DRS) probe
Locations: Imperial College NHS Trust
Countries: United Kingdom
Enrollment: 20
Age range: 18 Years - 99 Years
Sex: ALL

Summary:
A spectroscopic probe is a sensor designed to illuminate and gather light directly from the tissue while touching it. The probe contains optical fibres, some which illuminate the surface of the tissue with harmless visible (white) light, and others that collects the light that has been reflected from inside the tissue. As the investigators track the position of the probe, they can create spectroscopic images with richer colour information, allowing surgeons to differentiate between different types of tissue, such as normal or cancerous regions.

The spectroscopic probe, either sterilised or covered with a sterile probe cover, will be used on the tissue as part of the patient's operation, inside the body itself. The tissue will be sampled using the spectroscopic probe before the surgeons remove the tissue from the body. The research team will be in the operating theatre. The surgeon or a member of the research team will use the optical probe on the tissue samples. This will involve taking pictures and videos of the sample while the research team scan the samples with the spectroscopic probe.

Eligibility criteria:
Inclusion Criteria:

* Patients undergoing primary upper GI cancer resection surgery
* Patients undergoing primary lower GI (colorectal) cancer resection surgery
* Patients \>18 years of age
* Patients who consent to take part in the study

Exclusion Criteria:

* Patients who do not consent to the study or decline to participate
* Patients who do not meet the inclusion criteria
* Patients who lack capacity
* Patients undergoing emergency lower or upper GI cancer surgery
* Patients undergoing re-operation for cancer surgery
* Pregnant women

Outcomes:
Diagnostic accuracy of DRS probe to differentiate normal versus tumour tissue in vivo