moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Data Collection Using Eko Digital Devices in a Clinical Setting
Trial ID: NCT07227376
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-09-14
Completion date: 2026-07-01
Sponsor: Eko Devices, Inc.
Conditions: Lung Diseases
Interventions: Eko digital stethoscopes
Locations: Nemours Children's Health; Jefferson Einstein Philadelphia Hospital
Countries: United States; United States
Enrollment: 250
Age range: None - None
Sex: ALL

Summary:
The purpose of this research is to prospectively train and validate an artificial intelligence machine learning (ML) algorithm to detect the presence of adventitious lung sounds in adults. Clinicians will use the Eko CORE and/or Eko CORE 500 device(s) in real clinical settings to collect normal and abnormal lung sounds, as part of standard of care clinical practice, which will then be used to explore an ML algorithm for classifiers for wheeze, coarse crackle, fine crackle, rhonchus, stridor, rales, and cough, as well as determine any correspondences between the type and/or location of adventitious lung sounds and the type of pulmonary conditions as reported by clinicians.

Eligibility criteria:
Inclusion Criteria:

* Suspected or diagnosed lower respiratory condition OR Presence of wheeze, coarse crackle, fine crackle, rhonchus, stridor, rales, and cough discovered during routine auscultation
* Normal patients with no adventitious lung sounds
* Adults and pediatric patients (as available)

Exclusion Criteria:

* Unable to have multiple recordings taken on chest and back (e.g. compromised mobility)
* On mechanical ventilation

Outcomes:
Primary Objective
AI-Enhanced Analysis of Breast Density and Background Parenchymal Enhancement (BPE)
Trial ID: NCT06838130
Status: ENROLLING_BY_INVITATION
Study type: OBSERVATIONAL
Start date: 2022-05-01
Completion date: 2025-02
Sponsor: Link Campus University
Collaborators: University of Campania Luigi Vanvitelli
Conditions: Breast Parenchimal Enhancement; Artificial Intelligence (AI)
Locations: University of Campania Luigi Vanvitelli
Countries: Italy
Enrollment: 213
Age range: 18 Years - None
Sex: FEMALE

Summary:
This study expands upon previous research investigating the correlation between breast density, Background Parenchymal Enhancement (BPE), and age in contrast-enhanced mammography (CEM). By integrating Artificial Intelligence (AI) methodologies, including Artificial Neural Networks (ANNs) and deep learning models, the study aims to optimize the accuracy of predictions and validate prior findings obtained through multiple linear regression.

Eligibility criteria:
Patients who underwent CEM, mammography, and ultrasound between May 2022 and June 2023.

Availability of BPE assessment, BI-RADS density classification, and age data.

Complete dataset available for statistical and AI-based analysis.

Exclusion Criteria:

Patients with prior breast cancer treatment that could alter BPE.

Incomplete imaging or missing classification data.

Contraindications to contrast-enhanced imaging.

Outcomes:
Correlation between breast density, BPE, and age using AI-driven analysis.
Covid-19 Triage Using Camera-based AI
Trial ID: NCT04383457
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2020-06-15
Completion date: 2020-10-08
Sponsor: Vastra Gotaland Region
Collaborators: Detectivio AB
Conditions: Coronavirus Infections
Interventions: RIA-device (Remote Investigation and Assessment)
Locations: Östra Sjukhuset
Countries: Sweden
Enrollment: 214
Age range: 18 Years - None
Sex: ALL

Summary:
The vital signs are critical in assessing the severity and prognosis of infections, such as Covid-19. The devices used today for measuring the vital signs have to be in physical contact with the patients. There is an apparent risk of transferring infections from one patient to the next (or to healthcare professionals).

This project aims to evaluate a new camera-based system for contactless measurement of vital signs as well as an artificial intelligence (AI) predicting hospitalization or death within 30 days. This particular study will evaluate the new system's ability without interfering with standard care of the patient.

Eligibility criteria:
Inclusion Criteria:

1. The patient is attending for triage presenting with symptoms of an infection.
2. Subject has provided informed consent
3. Age ≥18 years
4. Fluent in Swedish (reading, writing, conversational)
5. Mental state is such that he or she is able to understand and give informed consent to participation in the study by signing the Information and Consent Form
6. The investigator determines that the new method, and the reference methods, can be used as intended with adequate reliability and safety
7. The time for investigations in this study is estimated to approximately 15-20 minutes. Vital signs will be handed over to the care provider responsible for the further management of the patient saving approximately 5-10 minutes of their time. Hence, the delay in the management of each patient introduced by this study is approximately 10 minutes. Patients deemed being in such a severe medical condition on arrival, that 10 minutes of delay is deemed detrimental will not be included.

Exclusion Criteria:

1. Depressed level of consciousness from inclusion up until all investigations are completed (during approximately 15-20 minutes).
2. Patient request to be withdrawn from the study.

Outcomes:
Agreement between the new camera based method and reference standard to estimate body temperature; Agreement between the new camera based method and reference standard to estimate heart rate; Agreement between the new camera based method and reference standard to estimate blood oxygen saturation; Agreement between the new camera based method and reference standard to estimate systolic blood pressure; Agreement between the new camera based method and reference standard to estimate diastolic blood pressure; Agreement between the new camera based method and reference standard to estimate respiratory rate
Artificial Intelligence for Pathology Diagnosis and Prognosis Prediction of Lung Nodule Using Smartphone Photos
Trial ID: NCT07098884
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-06-01
Completion date: 2025-10-30
Sponsor: Anhui Provincial Hospital
Collaborators: The First Affiliated Hospital of Soochow University; Ningbo No. 1 Hospital
Conditions: Artificial Intelligence; Lung Nodule
Interventions: AI model
Locations: Anhui Provincial Hospital; The First Affiliated Hospital of Soochow University; Ningbo First Hospital
Countries: China; China; China
Enrollment: 600
Age range: 20 Years - 75 Years
Sex: ALL

Summary:
The current study aims to develop and validate a deep learning signature for diagnosing pathology and predicting prognosis of lung nodule using smartphone photos of resected tumor specimens.

Eligibility criteria:
Inclusion Criteria: (1) Participants scheduled for surgery for radiological finding of pulmonary lesions from the preoperative thin-section CT scans; (2) Age ranging from 20-75 years.

Exclusion Criteria: (1) Participants with incomplete clinical information; (2) Participants who have received anti-tumor therapy.

Outcomes:
Area under the receiver operating characteristic curve
COMPARING AI ROLE-PLAY AND PEER ROLE-PLAY FOR INFORMED CONSENT TRAINING IN ENDOSCOPY: A RANDOMIZED CONTROL TRIAL
Trial ID: NCT07069504
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-07-02
Completion date: 2026-12-31
Sponsor: The Hospital for Sick Children
Collaborators: University of Toronto; Scarborough General Hospital
Conditions: Informed Consent Process
Interventions: AI-based role play; Peer role play
Locations: Scarborough Health Network, University of Toronto
Countries: Canada
Enrollment: 40
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this randomized controlled trial is to find out whether AI-based role play is effective in teaching healthcare trainees how to obtain informed consent from patients for endoscopic procedures.

The main question is:

• How does generative AI-based role play compare to traditional peer role play in training healthcare trainees to deliver informed consent for endoscopic procedures?

Researchers will compare AI-based role play with peer role play to see which method is more effective for teaching this skill.

What participants will do:

* Attend a lecture on how to obtain informed consent.
* Take part in simulation-based training using either peer role play or AI-based role play.
* Participate in a simulated clinical encounter to assess their ability to obtain informed consent.

Eligibility criteria:
Inclusion Criteria:

1\) physician trainee enrolled at the University of Toronto.

Exclusion Criteria:

1\) have previously obtained informed consent for an endoscopic procedure 25 or more times in either real clinical or simulated settings.

Outcomes:
Informed consent checklist; Communication Skills
Development and Evaluation of a Large Language Model - Based Training Program for Nurses in Public Health Emergencies
Trial ID: NCT07141433
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-10-01
Completion date: 2024-12-31
Sponsor: The Affiliated Hospital Of Guizhou Medical University
Conditions: The Emergency Response Capabilities of Nurses (Including Occupational Protection, Critical Thinking, Communication Skills and Humanistic Care, Etc.)
Interventions: LLM-Assisted Public Health Emergency Training Program; Standard Public Health Emergency Training Program
Locations: The Affiliated Hospital of Guizhou Medical University
Countries: China
Enrollment: 204
Age range: 18 Months - 35 Months
Sex: ALL

Summary:
The goal of this randomized controlled trial is to evaluate the immediate efficacy of a Large Language Model (LLM)-assisted training program in enhancing nurses' emergency response capabilities in 204 practicing nurses with ≤5 years of experience from tertiary hospitals in Guiyang, China, focusing on public health emergencies (PHEs). The main questions it aims to answer are:

1. Does LLM-assisted training improve nurses' comprehensive emergency response capabilities in PHEs?
2. Does it specifically enhance rescue skills and occupational protection abilities? Researchers will compare the experimental group (receiving routine PHE training + LLM-assisted learning) to the control group (receiving routine PHE training only) to see if LLM supplementation leads to significantly greater improvements in targeted emergency competencies.

Participants will:

Complete pre- and post-training assessments (Nurse Self-Assessment Scale for Emergency Response Ability, Nurse's Emergency Response Capacity Scale for PHEs).

Undergo a one-month PHE training program. (Experimental Group Only): Use LLMs for knowledge review, question answering, and exploring unfamiliar concepts during the training period.

Eligibility criteria:
Inclusion Criteria:

* Holds a valid nursing professional qualification certificate；
* ≤5 years of nursing work experience；
* Voluntarily agrees to participate in the training program。

Exclusion Criteria:

* Inability to complete the 1-month training program (e.g., planned leave, transfer, or resignation during the study period)
* Prior experience using Large Language Models (LLMs) for professional training (to avoid confounding effects)
* Refusal to comply with group assignment protocols (e.g., control group participants attempting to use LLMs)

Outcomes:
Comprehensive Emergency Response Capability Total Score Nurse's Self-Assessment Capability Total Score
Relisten: Improvement in Perceived Quality of Care and Time Savings in Writing Tasks Through the Automatic Generation of Clinical Notes in Primary Care: a Proof of Concept.
Trial ID: NCT06618092
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-07-01
Completion date: 2025-01-30
Sponsor: Fundacio d'Investigacio en Atencio Primaria Jordi Gol i Gurina
Collaborators: Fundació TicSalut; Institut Català de la Salut
Conditions: Primary Care
Interventions: Relisten
Locations: CAP Amposta; CAP Artès; CAP les Corts; CAP Centelles; CAP Sallent; CAP Súria
Countries: Spain; Spain; Spain; Spain; Spain; Spain
Enrollment: 500
Age range: 18 Years - None
Sex: ALL

Summary:
Background:

Relisten is an artificial intelligence-based software developed by Recog Analytics that improves patient care by facilitating more natural interactions between healthcare professionals and patients. Relisten extracts relevant information from recorded conversations, structuring it in the medical record and sending it to the Health Information System after the professional\&amp;amp;#39;s approval. This approach allows professionals to focus on the patient without the need to perform clinical documentation tasks.

Method:

This Proof of Concept (PoC) study is conducted as a multi-centre trial with the participation of several health professionals in Primary Care Centres (CAPs) of Amposta, Centelles, Artés, Sallent, Súria and the Consorci d\&amp;amp;#39;Atenció Primària de Salut Barcelona Esquerra (CAPSBE). During the study, Relisten will be used in consultations under informed consent, followed by patient and professional surveys. Statistical analyses will be performed for each objective, using independent sample comparison tests according to normality evaluated with the Kolmogorov-Smirnov test and Lilliefors correction. The sample has been determined a priori to optimise the achievement of satisfactory results. Stratified statistical tests will also be performed to consider the variance between professionals.

Discussion:

The investigators expect an improvement in the quality of care perceived by patients and a significant reduction in the time spent taking clinical notes, with a saving of at least 30 seconds per visit. Although a high quality of the notes generated is expected, it is uncertain whether a significant improvement over the control group, which is already expected to have high quality notes, will be demonstrated.

Eligibility criteria:
Inclusion criteria:

* To have signed the informed consent form.
* To carry out face-to-face medical and nursing consultations in Catalan and/or Castilian Spanish.
* To be over 18 years old.

Exclusion criteria:

* Inability to understand the nature of the study.
* Not fluent in Catalan or Castilian Spanish.
* Not giving consent to be recorded.
* Existence of any technical failure in the recording (e.g., internet service downtime).

Outcomes:
Relisten used; Time saved
Formative Evaluation of an eLearning Approach to Suicide Prevention Training
Trial ID: NCT05178121
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-11-01
Completion date: 2023-03-01
Sponsor: University of Washington
Collaborators: National Institute of Mental Health (NIMH)
Conditions: Suicide
Interventions: eLearning Training in Suicide Safety Planning
Locations: Harborview Medical Center
Countries: United States
Enrollment: 19
Age range: 18 Years - None
Sex: ALL

Summary:
Project WISE (Workplace Integrated Support \& Education) includes developmental and pilot research to create an e-learning training in suicide safety planning enhanced with novel skill-building technologies that can be integrated into the routine workflow of nurses serving patients hospitalized for medical, surgical, or traumatic injury reasons. This study pilots an initial version of this eLearning training and collects data on the acceptability of the training and its components as well as engagement of nurses in the training. The evaluation will inform iterations of the training.

Eligibility criteria:
Inclusion Criteria:

* Nurses working in acute or intensive care units at Harborview Medical Center

Exclusion Criteria:

\-

Outcomes:
Training Acceptability - System Usability Scale for Client Bot Emily; Training Acceptability Self-Report - Post-Training; Training Acceptability Self-Report - 6-Month Follow-up; Training Engagement With Client Bot User Data - Post-Training; Training Engagement With Lyssn Advisor User Data - Post-Training; Training Engagement With Client Bot User Data - 6-Month Follow-up; Training Engagement With Lyssn Advisor User Data - 6-Month Follow-up; Training Engagement Self-Report - Post-Training; Client Bot Training Engagement Self-Report - 6 Month Follow-up; Participant Retention; Training Acceptability - System Usability Scale for Lyssn Advisor; Lyssn Advisor Training Engagement Self-Report - 6 Month Follow-up
Cardiovascular Acoustics and an Intelligent Stethoscope. An Observational Study to Collect Heart Sound Recordings From Patients to Understand the Acoustic Characteristics of Murmurs and Develop an Artificially Intelligent Stethoscope.
Trial ID: NCT04445012
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2019-10-24
Completion date: 2023-01-31
Sponsor: Papworth Hospital NHS Foundation Trust
Conditions: Heart Valve Diseases
Locations: University Hospitals Birmingham NHS Foundation Trust; Royal Papworth Hospital NHS Foundation Trust; Guy's and St Thomas' NHS Foundation Trust; King's College Hospital NHS Foundation Trust; Imperial College Healthcare NHS Trust
Countries: United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom
Enrollment: 1150
Age range: 14 Days - None
Sex: ALL

Summary:
The aim of the project is to develop an artificial intelligence software capable of analysing heart sounds to provide early diagnosis of a variety heart diseases at an early stage. Since the invention of the stethoscope by Laennec in 1816, the basic design has not changed significantly. Our software could be coupled with existing electronic stethoscopes to create an 'intelligent' stethoscope that could be used by healthcare assistants or practice nurses to screen for sound producing heart diseases. It could also be used at home by patients who would otherwise go undiagnosed.

The study investigators at Cambridge University Engineering Department (CUED) have developed a proof-of-concept AI algorithm to detect heart murmurs. However, in order to accurately detect the specific pathology and severity underlying the murmur, more heart sound recordings (matched with the ground truth from the patient's echocardiogram) are required. Patients presenting to one of the partner hospitals requiring an echocardiogram as part of their routine care will be invited to consent to this study. Participation will entail recording of a patient's heart sounds using an electronic stethoscope as well as collection of routine clinical data and a routine clinical echocardiogram at a single routine out patient visit.

Eligibility criteria:
Inclusion Criteria:

* Participant willing and able to give informed consent for participation in study
* Participant to undergo an echocardiogram as part of their routine assessment

Exclusion Criteria:

* Informed consent is not given
* New York Heart Association (NYHA) functional class = 4

Outcomes:
To assess specificity of an algorithm for detecting clinically significant valve disease and congenital heart disease relative to the performance of General Practitioners
Smartphone-Based Treatments for Social Anxiety With Artificial Intelligence Support: A Three-Arm Randomized Controlled Pilot Study Comparing Psychodynamic Therapy, Cognitive Behavioral Therapy, and Waitlist (STePS-Ai)
Trial ID: NCT07533812
Status: ACTIVE_NOT_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-04-21
Completion date: 2026-07-01
Sponsor: Stockholm University
Conditions: Social Anxiety Disorder (SAD)
Interventions: Ai chatbot
Locations: Stockholm university
Countries: Sweden
Enrollment: 90
Age range: 18 Years - None
Sex: ALL

Summary:
This pilot study compares two 4-week, smartphone-based treatments for social anxiety: one uses a psychodynamic therapy approach and the other uses a cognitive behavioral therapy approach. Both treatments are guided by an AI assistant rather than a human therapist. The AI assistant is programmed to support participants in reflecting on core therapeutic principles, much like a well-informed coach would.

Participants are randomly assigned to one of three groups: psychodynamic therapy with AI support, cognitive behavioral therapy with AI support, or a waitlist control. Those in the waitlist group can receive an active treatment after the 4-week waiting period. All participants complete weekly online questionnaires to monitor anxiety and any changes in mood or well-being.

Eligibility criteria:
* Age 18 years or older
* Score of 30 or higher on the Liebowitz Social Anxiety Scale-Self Report (LSAS-SR)
* Access to a smartphone
* Ability to read and write Swedish
* Currently engaged in psychological treatment
* Severe depression (Patient Health Questionnaire-9 score 20 or higher)
* Use of medication for anxiety or depression if there have been changes within the past 30 days
* Suicidality as assessed by the Columbia Suicide Severity Rating Scale (C-SSRS)

Outcomes:
Social Phobia Inventory (SPIN)
Experiment on the Use of Innovative Computer Vision Technologies for Analysis of Medical Images in the Moscow Healthcare System
Trial ID: NCT04489992
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2020-02-21
Completion date: 2024-01-01
Sponsor: Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department
Conditions: AI (Artificial Intelligence); Mammary Cancer; Lung Cancer; X-Rays; Lesion; COVID-19; Chest--Diseases; Abdomen Disease; Brain Disease; Fractures, Bone
Locations: Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department
Countries: Russia
Enrollment: 133000
Age range: 18 Years - None
Sex: ALL

Summary:
It is planned to integrate various services based on computer vision technologies for analysis of the certain type of x-ray study into Moscow Unified Radiological Information Service (hereinafter referred to as URIS).

As a result of using computer vision-based services, it is expected:

1. Reducing the number of false negative and false positive diagnoses;
2. Reducing the time between conducting a study and obtaining a report by the referring physician;
3. Increasing the average number of radiology reports provided by a radiologist per shift.

Eligibility criteria:
Inclusion Criteria:

* Age (over 18 years)
* Gender (male and female)
* Referral for the study
* Signed informed consent to participate in the Experiment
* Chest computed tomography and Low-dose computed tomography for lung cancer detection or mammography for breast cancer detection or chest X-ray for lung pathology detection

Exclusion Criteria:

* Another type of study (including a different modality and anatomical area)

Outcomes:
Number of errors