moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Using Wearable Device and Smart Phone to Improve Survival Prediction and Quality of Life in Patients Receiving Palliative Care
Trial ID: NCT05054907
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-09-23
Completion date: 2023-04-30
Sponsor: National Taiwan University Hospital
Collaborators: National Taiwan University
Conditions: Terminal Cancer; End Stage Cancer
Locations: National Taiwan University Hospital; National Taiwan University, Cancer Center
Countries: Taiwan; Taiwan
Enrollment: 75
Age range: 20 Years - 105 Years
Sex: ALL

Summary:
This study is going to use wearable devices and smartphones to collect physical data from terminal patients and build a survival predicting model for terminal patients with machine learning. Investigators hypothesize that continuous physical data monitoring could offer a hint to better predictability in end-of-life care.

Eligibility criteria:
Inclusion criteria

* Age: 20 years old or older
* Clinical diagnosis: cancer in terminal stage.

Exclusion criteria

\- Cannot cooperate with use of wearable devices or smartphones.

Outcomes:
Area Under the Receiver Operating Characteristic curve (AUC-ROC) of the machine-learning model to predict survival using wearable device parameters and clinical assessment; Area Under the Receiver Operating Characteristic curve (AUC-ROC) of machine-learning model to predict unexpected medical needs using wearable device parameters and clinical assessment
Nationwide Study of Artificial Intelligence in Adenoma Detection for Colonoscopy
Trial ID: NCT05870332
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2023-10-16
Completion date: 2025-05-31
Sponsor: King's College Hospital NHS Trust
Collaborators: Medtronic; National Institute for Health Research, United Kingdom
Conditions: Colonic Polyp; Colonic Adenoma; Colo-rectal Cancer
Interventions: GI Genius (GIG)
Locations: King's College Hospital NHS Foundation Trust
Countries: United Kingdom
Enrollment: 4000
Age range: 18 Years - 85 Years
Sex: ALL

Summary:
The goal of this trial is to determine whether use of a Computer Assisted Detection (CADe) programme leads to an increase in ADR for either units or individual colonoscopists, independent of setting or expertise

Eligibility criteria:
Inclusion Criteria:

* Any patient aged 18-85 scheduled for colonoscopy by current NHSE / British Society of Gastroenterology criteria

Exclusion Criteria:

* Colonoscopy being performed for polyp surveillance
* Unable to provide informed, written consent

Outcomes:
CADe-ADR in real world practice
Acoustic Cough Monitoring for the Management of Patients With Known Respiratory Disease
Trial ID: NCT05042063
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2021-09-15
Completion date: 2022-09-15
Sponsor: Clinica Universidad de Navarra, Universidad de Navarra
Collaborators: Centre de Recherche du Centre Hospitalier de l'Université de Montréal; Hyfe Inc
Conditions: Cough; COPD; GERD; Asthma; Tuberculosis; Non-Tuberculous Mycobacterial Pneumonia; COVID-19 Pneumonia
Interventions: Hyfe Cough Tracker; Hyfe Air
Locations: Clinica Universidad de Navarra
Countries: Spain
Enrollment: 616
Age range: 5 Years - 100 Years
Sex: ALL

Summary:
This study pretends to evaluate the potential use of Hyfe Cough Tracker (Hyfe) to screen for, diagnose, and support the clinical management of patients with respiratory diseases, while enriching a dataset of disease-specific annotated coughs, for further refinement of similar systems.

Eligibility criteria:
Inclusion Criteria:

For participants in the main study group

* Outpatient or inpatients at the Clinical Universidad de Navarra with a complaint of cough.
* The patient or his/her legal representative, have given consent to participate in the study.

For participants in the sub-study groups:

* Being 18 years or older.
* Providing consent for the sub-study

Exclusion Criteria:

* Inability to accept the privacy policy and terms of use of Hyfe.
* Lack of access to a Wi-Fi network at the site of residence (for the main study group).
* Unwillingness to regularly use the cough-surveillance system throughout the monitoring period

Outcomes:
Correlation between subjective perception of cough and objective frequency
Preventing Medication Dispensing Errors in Pharmacy Practice With Interpretable Machine Intelligence
Trial ID: NCT06245044
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-04-11
Completion date: 2024-12-04
Sponsor: University of Michigan
Collaborators: National Library of Medicine (NLM)
Conditions: Machine Intelligence in the Pharmacy
Interventions: No MI Help; Scenario #1; Scenario #2
Locations: University of Michigan
Countries: United States
Enrollment: 68
Age range: 18 Years - None
Sex: ALL

Summary:
Pharmacists currently perform an independent double-check to identify drug-selection errors before they can reach the patient. However, the use of machine intelligence (MI) to support this cognitive decision-making work by pharmacists does not exist in practice. This research is being conducted to examine the effectiveness of the timing of machine intelligence (MI) advice on to determine if it results in lower task time, increased accuracy, and increased trust in the MI.

Eligibility criteria:
Inclusion Criteria:

* Licensed pharmacist in the United States
* Age 18 years and older at screening
* PC/Laptop with Microsoft Windows 10 or Mac (Macbook, iMac) with MacOS with Google Chrome, Edge, Opera, Safari, or Firefox web browser installed on the device
* Screen resolution of 1024x968 pixels or more
* A laptop integrated webcam or USB webcam is also required for the eye tracking purpose.

Exclusion Criteria:

* Participated in Wave 1 or Wave 2
* Eyeglasses
* Uncorrected cataracts, intraocular implants, glaucoma, or permanently dilated pupil
* Require a screen reader/magnifier or other assistive technology to use the computer
* Eye movement or alignment abnormalities (lazy eye, strabismus, nystagmus)

Outcomes:
Reaction Time; Decision Accuracy; Trust Change; Trust
Can Artificial Intelligence Reduce Consumption of Standard High Volume Bowel Preparation Regimen Among Older Population, Without Compromising the Quality of Colonoscopy? An International Multi-centre Randomized Controlled Trial.
Trial ID: NCT06904209
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-06-01
Completion date: 2028-06-30
Sponsor: Chinese University of Hong Kong
Conditions: Bowel Preparation Quality
Interventions: AI
Locations: Centre of Digestive Health
Countries: Hong Kong
Enrollment: 1824
Age range: 65 Years - None
Sex: ALL

Summary:
The goal of this study is to assess the clinical and cost-effectiveness of the AI bowel preparation evaluation system in reducing the consumption volume of the standard high-volume PEG regimen before colonoscopy among the older population.

Researchers will compare bowel preparation adequacy and other colonoscopy quality matrices, and patient satisfaction between patients using the AI system and those using Standard Practice.

Eligibility criteria:
Inclusion Criteria:

* individual aged 65 years or above
* undergoing colonoscopy for any indication
* requiring a standard high-volume PEG regimen for bowel preparation
* having a smartphone themselves or their caregivers (including ward nurses) having one will be recruited.

Exclusion Criteria:

* allergy to PEG,
* suspected or diagnosed gastrointestinal obstruction or perforation,
* suspected or diagnosed ileus
* suspected or diagnosed gastric retention
* suspected or diagnosed toxic colitis, toxic megacolon,
* prior gastrointestinal surgery, and
* unable to provide informed consent.

Outcomes:
Volume of PEG consumed
Pathological Classification of Pulmonary Nodules From Gross Images of Tumor Using Deep Learning
Trial ID: NCT05221814
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2020-06-01
Completion date: 2023-01-01
Sponsor: Jiangxi Provincial Cancer Hospital
Collaborators: Guangdong Provincial People's Hospital
Conditions: Lung Cancer; Artificial Intelligence
Interventions: gross pathologic photo based deep learning model
Locations: Guagndong Provincial People's Hospital; Jiangxi Cancer Hospital
Countries: China; China
Enrollment: 2000
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
This study aimed to develop a deep-learning model to automatically classify pulmonary nodules based on white-light images and to evaluate the model performance. Besides, suitable operation could be chosen with the help of this model, which could shorten the time of surgery.

Eligibility criteria:
Inclusion Criteria:

1. Male or female，18 years and older.
2. Patients haven't undergone any therapy.
3. The pulmonary nodules were confirmed AIS, MIA or IAC.
4. The sizes of pulmonary nodules were less than 3cm.
5. The images were jpg format.

Exclusion Criteria:

1. Suffering from other tumor disease before or at the same time.
2. Images with poor quality or low resolution that precluded proper classification.

Outcomes:
1. Pathological subtype; Area Under the Curve (AUC)
Can a Machine Learning Recognise of Out-of-Hospital Cardiac Arrest During Emergency Calls and Assist Medical Dispatchers
Trial ID: NCT04219306
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2018-09-01
Completion date: 2020-04-02
Sponsor: Emergency Medical Services, Capital Region, Denmark
Conditions: Out-Of-Hospital Cardiac Arrest
Interventions: Alert on dispatchers screen 'Suspect cardiac arrest'
Locations: Emergency Medical Services Copenhagen
Countries: Denmark
Enrollment: 5242
Age range: None - None
Sex: ALL

Summary:
Emergency medical Services Copenhagen has developed a machine learning model that analyzes the calls to 1-1-2 (9-1-1) in real time. The model are able to recognize calls where a cardiac arrest is suspected. The aim of the study is to investigate the effect of a computer generated alert in calls where cardiac arrest is suspected.

The study will investigate

1. whether a potential increase in recognitions is due to machine alerts or the increased focus of the medical dispatcher on recognizing Out-of-Hospital cardiac Arrest (OHCA) when implementing the machine
2. if a machine learning model based on neural networks, when alerting medical dispatchers will increase overall recognition of OHCA and increase dispatch of citizen responders.
3. increased use of automated external defibrillators (AED), cardiopulmonary resuscitation (CPR) or dispatch of citizen responders in cases of OHCA on machine recognised OHCA vs. medical dispatcher recognised OHCA.

Eligibility criteria:
Inclusion Criteria:

* Call regarding a cardiac arrest registered in the national Danish Cardiac Arrest Registry
* OHCA is recognized by machine-learning model
* Call originates from 1-1-2

Exclusion Criteria:

* OHCA Emergency Medical Services - witnessed
* Call is from another authority (police or fire brigade)
* Call is a repeat call
* Call has been on hold for conference

Outcomes:
Dispatcher recognition of cardiac arrest
Bladder Cancer Detection Using Convolutional Neural Networks
Trial ID: NCT05193656
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2021-06-01
Completion date: 2026-06-01
Sponsor: Zealand University Hospital
Conditions: Bladder Cancer
Interventions: Al_bladder
Locations: Zealand University Hospital
Countries: Denmark
Enrollment: 5000
Age range: None - None
Sex: ALL

Summary:
The investigators aim to experiment and implement various deep learning architectures to achieve human-level accuracy in Computer-aided diagnosis (CAD) systems. In particular, the investigators are interested in detecting bladder tumors from CT urography scans and cystoscopies of the bladder in this project.

Eligibility criteria:
Inclusion Criteria:

* Patients with first time hematuria
* Patients with the control program for previous bladder cancer

Exclusion Criteria:

* Patients with control cystoscope for noncancer suspected disease

Outcomes:
Comparing standard technique to Machine Learning
AI-HOPE Lung Cancer: Building a Predictive Tool for Metastatic Lung Cancer
Trial ID: NCT06788366
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-02-12
Completion date: 2026-12-31
Sponsor: IRCCS San Raffaele
Conditions: NSCLC Stage IV
Interventions: Immunotherapy; Chemotherapy
Locations: Francesca Rita Ogliari
Countries: Italy
Enrollment: 2000
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of our project is building a predictive response algorithm for patients with metastatic lung cancer, exploiting an artificial intelligence platform. It will collect patient information from all areas (clinical, laboratory, radiological, pathological) and analyse them, understanding connections and correlations, both at baseline and at pre-specified timepoints. It would lead to the development of a reliable and constantly evolving predictive score, able to continuously re-weight the importance of each variable as new data come in.

Since the greatest clinical need is identifying non-responders to immunotherapy and chemo-immunotherapy combination (30% of all treated patients), these two populations are defined as the starting cohorts (Cohort A, immunotherapy alone, Cohort B, chemo-immunotherapy combinations).

For each cohort, three main questions are to be answered:

Q1) Early progressors (defined as progressive disease or death within three months of treatment or at first radiological restaging) Q2) Toxicity (with a special focus on severe toxicities G≥3) Q3) Long survivors (defined as patients reaching an overall survival of at least 1.5x median overall survival in registrative trials)

The early identification of non-responders, high-risk patients (or on the other hand, long survivors) would help their healthcare planning, providing individualised follow-up strategies or prompting their inclusion in alternative treatments (eg clinical trials).

For all cohorts, first data entry will be retrospective and second data entry will be prospective (as validation set).

Eligibility criteria:
Inclusion criteria:

* Patients with histological or cytological diagnosis of NSCLC
* Stage IV according to investigator's staging procedures (or any locally advanced tumour not feasible for local radical treatment)
* Treatment with at least 1 cycle of mono-immunotherapy or chemo-immunotherapy (as per clinical practice)
* Availability of follow-up

Exclusion criteria:

* Patients with other thoracic tumours non-NSCLC (i.e. SCLC)
* Stage other than IV or feasible for radical treatment upfront
* Treatment within clinical trials (with combination regimens different from the aforementioned combinations)
* Lost to follow-up

Outcomes:
Early progressive disease; Lung toxicity; Long survivors
COVID-19 Volumetric Quantification on Computer Tomography Using Computer Aided Diagnostics
Trial ID: NCT05282056
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-02-24
Completion date: 2022-03-15
Sponsor: Bogdan Bercean
Collaborators: Pius Brinzeu Timisoara County Emergency Hospital
Conditions: COVID-19
Interventions: CAD analysis
Locations: Pius Brinzeu Timisoara County Emergency Hospital
Countries: Romania
Enrollment: 200
Age range: 16 Years - None
Sex: ALL

Summary:
The aim of the study is to asses the influence of computer aided diagnostic to the process of lung affection quantification on computer tomography in COVID-19 confirmed patients.

Eligibility criteria:
Inclusion Criteria:

* RT-PCR confirmed patients of COVID-19

Exclusion Criteria:

* 15 or lower

Outcomes:
Mean difference of lung affection quantification percentage
Chicago Data-driven Opioid Use Disorder Screening, Engagement, Treatment and Planning System
Trial ID: NCT07498322
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-06-01
Completion date: 2029-06-30
Sponsor: University of Illinois at Chicago
Collaborators: National Institute on Drug Abuse (NIDA)
Conditions: Opiod Use Disorder
Interventions: OUD screening; MOUD; Continuity of care
Locations: University of Illinois Hospitals and Clinics (UI Health)
Countries: United States
Enrollment: 271031
Age range: 16 Years - None
Sex: ALL

Summary:
This study, called the Chicago Data-driven Opioid use disorder Screening, Engagement, Treatment and Planning (C-DOSETaP) System, tests a new system of clinical care for patients with opioid use disorder (OUD) across a large health system.

The main questions this study aims to answer are:

1. Does the C-DOSETaP System increase screening for patients with OUD;
2. Does the C-DOSETaP System improve continuity of health care for patients with OUD;
3. Does the C-DOSETaP System increase use of medications for opioid use disorder; and
4. Does the C-DOSETaP System reduce the number of opioid-related deaths in the neighborhoods served.

Eligibility criteria:
Inclusion Criteria

* Participant must be a patient seen at the University of Illinois Hospital and Clinics
* Adults and adolescents age 16 or older

Exclusion Criteria

\- Children younger than age 16

Outcomes:
Number of people screened for OUD; Continuity of care for patients with OUD; Utilization of MOUD across the health system
The Application of Artificial Intelligence to Patients Undergoing Endoscopic Transsphenoidal Surgery: a Single-site Prospective Feasibility and Exploratory Study (IDEAL Stage 1 and 2a)
Trial ID: NCT07568366
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: EARLY_PHASE1
Start date: 2026-06-01
Completion date: 2029-01-31
Sponsor: University College, London
Collaborators: University College London Hospitals
Conditions: Pituitary Adenoma
Interventions: Live intra-op AI analysis of endoscopic video feed, with output displayed on supplementary monitor
Locations: National Hospital for Neurology and Neurosurgery
Countries: United Kingdom
Enrollment: 30
Age range: 18 Years - None
Sex: ALL

Summary:
This study focuses on bringing artificial intelligence into the operating room to assist with pituitary tumour surgeries performed through the nose. These procedures are technically demanding, and training new surgeons is often inconsistent. To address this, researchers at the National Hospital for Neurology and Neurosurgery are testing AI systems that "watch" surgical videos in real-time to identify anatomy, instruments, and the specific phase of the operation.

The core goal of the prospective trial is to improve education and team coordination without interfering with the surgery itself. The AI displays its analysis on tablets positioned for the surgical residents and nurses, rather than the lead surgeon. This setup allows the team to follow the procedure's progress, key anatomy and anticipate next steps without the surgeon needing to stop and explain. Because hospital internet can be unreliable, the study is prioritizing specialized hardware from NVIDIA that processes data locally. This "edge computing" approach ensures the AI is fast and doesn't require a live cloud connection to function.

This trial will assess the device feasibility (IDEAL Stage 1 study, \~6 cases), followed by early safety and system technical refinement (IDEAL 2a study, \~20-30 cases).

Eligibility criteria:
The inclusion criteria will be:

1. Adult patients (above the age of 18 years old)
2. Undergoing endoscopic transsphenoidal surgery
3. Able to provide consent

The exclusion criteria will be:

1. Patients less than 18 years of age
2. Undergoing transcranial surgery or microscopic transsphenoidal surgery
3. Unable to provide consent e.g., cannot understand, mental illness, or later withdrawing consent

Outcomes:
Feasibility of live AI video analysis
Evaluating the Cost-Efficiency and Workflow Impact of AI-Supported Fracture Detection in an Orthopedic Emergency Care Unit
Trial ID: NCT06754137
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-03-31
Completion date: 2026-04-30
Sponsor: Salzburger Landeskliniken
Collaborators: Klinikum Nürnberg
Conditions: Fractures, Bone; Effusion Joint; Bone Lesion; Dislocation
Interventions: AI-Assisted Fracture Detection System; Standard Physician-Interpreted Fracture Detection
Locations: Landesklinik Hallein, Salzburger Landeskliniken; University Hospital Salzburg, Salzburger Landeskliniken; University Hosptial Nuremberg, Klinikum Nürnberg
Countries: Austria; Austria; Germany
Enrollment: 4800
Age range: None - None
Sex: ALL

Summary:
Brief Summary The purpose of this study is to determine if artificial intelligence (AI) can assist doctors in detecting broken bones, effusions, dislocations and bone lesions more quickly and accurately in an emergency room setting. The study will also evaluate whether AI can save time and reduce costs in healthcare.

The main questions to be addressed are:

* Does AI improve the accuracy of detecting broken bones/dislocations/effusions/bone lesions?
* Can AI expedite the process of diagnosing broken bones/dislocations/effusions/bone lesions?
* Does AI reduce healthcare costs by enhancing efficiency?

To investigate these questions, two groups of patients will be compared. One group will follow the traditional diagnostic approach, while the other group will utilize AI to assist in diagnosing X-rays.

Participants in the study will:

Undergo standard X-ray imaging of injured arms or legs, as part of routine care.

Have X-rays reviewed by doctors with or without AI support, depending on the assigned group.

The study will include patients of all ages presenting to the emergency room with an isolated injury or joint complaints. No additional tests or treatments beyond standard care will be involved.

Eligibility criteria:
Inclusion Criteria:

* Presenting to the emergency department with an isolated injury or joint complaint
* Patients able and willing to provide informed consent.

Exclusion Criteria:

* Patients with injuries or complaints involving multiple body regions
* Patients with prior imaging of the affected extremity or region within the past 6 months
* Contraindications to X-ray imaging (e.g., pregnancy or severe instability)
* Patients with other ongoing studies that may interfere with this study
* Patients unable to provide consent due to cognitive impairment or language barriers without an available representative.

Outcomes:
Diagnostic Accuracy of Fracture/Dislocation/Effusion/Bone Lesion Detection
Simulating Psychotherapeutic Sessions With Generative Artificial Intelligence: A Proof-of-Concept Study of In Silico Psychotherapy Research
Trial ID: NCT06813066
Status: ACTIVE_NOT_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-02-01
Completion date: 2027-01-27
Sponsor: University Hospital, Basel, Switzerland
Collaborators: Trier University; RWTH Aachen University; University of Basel
Conditions: Mental Disorder
Interventions: High Levels of Common Therapeutic Factors; Low Levels of Common Therapeutic Factors; Standard motivational interviewing
Locations: University Hospital Basel
Countries: Switzerland
Enrollment: 520
Age range: 18 Years - None
Sex: ALL

Summary:
The study assesses the potential of using computational models, specifically large language models, to simulate psychotherapeutic sessions, aiming to improve therapy outcomes and advance therapist training through innovative technology.

Eligibility criteria:
Inclusion Criteria:

* Simulation of psychotherapy sessions of conversations between an adult person presenting with a mental or behavioral health problem and a psychotherapist using large language models and 8 real-world transcripts

Exclusion Criteria:

* Simulation protocols with severe simulation errors

Outcomes:
Simulation's Accuracy in generating Psychotherapeutic Dialogues