moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Validation of a Multimodal Artificial Intelligence Model in in Diagnosing Pancreatic Solid Lesions: a Prospective, Multicenter, Randomized, Controlled Trial
Trial ID: NCT06753318
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-01
Completion date: 2026-01
Sponsor: Huazhong University of Science and Technology
Collaborators: Beijing Union Hosptial; Affiliated Drum Tower Hospital of Nanjing University Medical School; Shanghai Longhua Hospital; Beijing Friendship Hospital; Qilu Hospital of Shandong University; Sir Run Run Shaw Hospital
Conditions: Pancreatic Cancer; Pancreatitis; Pancreatic Neuroendocine Neoplasms (pNETs); Autoimmune Pancreatitis; Solid Pseudopapillary Neoplasm of the Pancreas
Interventions: The assistance of the Joint-AI model; The assistance of the interpretable Joint-AI model
Locations: Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology
Countries: China
Enrollment: 716
Age range: 18 Years - None
Sex: ALL

Summary:
This clinical trial aims to learn if a multimodal artificial intelligence (AI) model can enhance the diagnosis of pancreatic solid lesions. The main questions it aims to answer are:

1. Does the AI model enhance the diagnostic performance of endoscopists in diagnosing pancreatic solid lesions?
2. Does the addition of interpretability analysis further improve the diagnostic performance of the assisted endoscopists? Researchers will compare the diagnostic performance of endoscopists with or without the assistance of the AI model.

Participants will:

1. Their clinical data will be prospectively collected.
2. They will be randomized to the AI-assist group and the conventional diagnosis group.

Eligibility criteria:
Inclusion Criteria:

* Imaging examinations (MRI, CT, B-ultrasound) show a solid mass in the pancreas, which requires endoscopic ultrasound guided-fine needle aspiration/biopsy (EUS-FNA/B) to clarify the nature of the lesion in patients.
* Written consent provided

Exclusion Criteria:

* Age under 18 years old

Outcomes:
Rate of correct diagnostic classification with assistance of the Joint-AI Model; Rate of correct diagnostic classification with assistance of the Interpretable Joint-AI Model
Combined Artificial Intelligence and Mobile Application for Remote Infant Motor Screening: Development and Validation
Trial ID: NCT06521918
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-10-17
Completion date: 2027-07-31
Sponsor: National Taiwan University Hospital
Collaborators: National Health Research Institutes, Taiwan
Conditions: Motor Disorders
Locations: National Taiwan University
Countries: Taiwan
Enrollment: 242
Age range: 2 Months - 18 Months
Sex: ALL

Summary:
The purpose of this study is therefore five-fold: (1) designation of an APP "Baby Go" version 3.0 to include the assessment, follow-up, and education functions for parental use at home, (2) development and validation of the AI algorithm for infant motor assessment based on home videos obtained from term and preterm infants, (3) comparison of parental perception and report with AI-driven assessment results, (4) examination of the predictive validity of the AI algorithm for infant motor assessment on subsequent outcome, and (5) investigation of the usability of the APP "Baby Go" version 3.0 in parents and clinicians.

Eligibility criteria:
The inclusion criterion is:

Preterm infants: gestational age \< 37 weeks, birth body weight \< 2,500 grams, and corrected age of 2-18 months.

Term infants: gestational age 37-42 weeks, birth body weight \>2,500 grams, and age of 2-18 months.

Clinicians: who provide early intervention to the participating infants in this study.

The exclusion criterion is:

Infants: parents can not read Chinese. Clinicians: can not read Chinese

Outcomes:
Alberta Infant Motor Scale (AIMS); Age of walking attainment; User experience from parents; User experience from clinicians
Clinical Utility and Performance of the Sepsis ImmunoScore: A Prospective Cohort Device Study
Trial ID: NCT07543497
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-04
Completion date: 2027-12
Sponsor: Prenosis, Inc.
Conditions: Sepsis
Interventions: Sepsis ImmunoScore
Enrollment: 5000
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this prospective cohort device study is to assess clinical usage and impact, diagnostic performance, and prognostic performance of the Sepsis ImmunoScore.

Eligibility criteria:
Inclusion Criteria:

* Adult (age 18 years or older) patients presenting to hospitals and emergency departments of medical institutions across the United States.
* Adult (age 18 years or older) patients suspected of sepsis.

Exclusion Criteria:

* none

Outcomes:
Sepsis diagnosis; SEP-1 bundle compliance; Number of clinically significant events such as infection, sepsis, critical care interventions, escalation of care, mortality
Artificial Intelligence System for Early Warning of Adverse Events in Acute Myocardial Infarction
Trial ID: NCT07139860
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2022-11-26
Completion date: 2026-12-31
Sponsor: Hui Chen
Conditions: Acute Myocardial Infarction With ST Elevation; Intelligent Management Platform; Early Warning
Locations: Beijing Friendship Hospital
Countries: China
Enrollment: 1400
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this observational study is to learn about the effectiveness of an artificial intelligence-based early warning system for predicting adverse events in patients with acute myocardial infarction (AMI). The main question it aims to answer is:

Does an AI-based early warning system improve the assessment and prediction of adverse events across the full course of AMI care (from prevention to diagnosis, treatment, and rehabilitation)?

Participants who are receiving routine medical care for AMI in tertiary hospitals will have their multimodal medical data (clinical records, diagnostic tests, imaging, treatment pathways) collected and analyzed. Data will be integrated using innovative cross-modal representation methods and predictive models. The study will follow patients during their hospital stay and subsequent clinical follow-up to evaluate the feasibility, accuracy, and clinical value of the AI-based early warning system.

Eligibility criteria:
Inclusion Criteria:

* 1\. Hospitalized patients who meet the diagnostic criteria for acute myocardial infarction. 2. Patients who agree to participate and sign the informed consent form.

Exclusion Criteria:

* 1\. Patients with terminal malignant tumors and an expected survival time of less than 3 months. 2. Patients with complete disability and inability to communicate. 3. Patients unable to comply with follow-up.

Outcomes:
MACCE
IDEAL: Artificial Intelligence and Big Data for Early Lung Cancer Diagnosis Prospective Study (Phase 2)
Trial ID: NCT03753724
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2018-08-29
Completion date: 2022-03-31
Sponsor: University of Oxford
Collaborators: Optellum Ltd.
Conditions: Pulmonary Nodule, Solitary; Pulmonary Nodule, Multiple; Lung Cancer
Locations: Royal Berkshire Nhs Foundation Trust; Nottingham University Hospitals Nhs Trust; Oxford University Hospitals NHS Foundation Trust; Leeds Teaching Hospitals Nhs Trust
Countries: United Kingdom; United Kingdom; United Kingdom; United Kingdom
Enrollment: 1293
Age range: 18 Years - None
Sex: ALL

Summary:
This study aims to test the use of novel CT image analysis techniques to enable a better characterisation of small pulmonary nodules. The study will incorporate solid and predominantly solid nodules of 5-15 mm scanned using a variety of scanner types, imaging protocols and patient populations. The investigators hope that the new image processing techniques will improve the accuracy of lung nodule analysis which will in turn reduce the number of unnecessary investigations for benign nodules and may increase the accuracy of the early diagnosis of lung cancer in malignant nodules. This study aims to test this novel analysis software to subsequently allow validation.

Eligibility criteria:
Inclusion Criteria:

* Male or Female, aged 18 years or above
* CT scans identified as having pulmonary nodule(s) of 5-15mm
* Patients with solid or predominantly solid nodules referred to the pulmonary nodule clinic or for CT scan review by a specialist
* CT scan section thickness of 3mm and less

Exclusion Criteria:

* The CT scans are technically inadequate
* Having received treatment for cancer in the last 5 years
* Patient has more than five reported qualifying nodules

Outcomes:
The overall diagnostic performance of a new computer aided prediction (CAP) model for malignancy in small pulmonary nodules (% diagnostic accuracy).
Effect of the Automatic Surveillance System on Surveillance Rate of Colorectal Postpolypectomy Patients
Trial ID: NCT05894850
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-06-01
Completion date: 2029-12-31
Sponsor: Renmin Hospital of Wuhan University
Conditions: Artificial Intelligence; Surveillance
Interventions: AI based automatic surveillance (AS) system (ENDOANGEL-AS) reminds the patients through by telephone .; AI based automatic surveillance (AS) system (ENDOANGEL-AS) reminds the patients through message.; Manually remind the patients
Locations: Renmin Hospital of Wuhan University
Countries: China
Enrollment: 1644
Age range: 18 Years - None
Sex: ALL

Summary:
In this study, we proposed a prospective study about the effect of the automatic surveillance system on surveillance rate of colorectal postpolypectomy patients. The enrolled patients were divided into group A with intelligent surveillance system reminding though telephone and message, group B with intelligent surveillance system reminding though message, group C with manual reminder, and group D with natural state. The surveillance among the four groups were compared.

Eligibility criteria:
Inclusion Criteria:

* Male or female aged 18 years or older who undergo colonoscopy.

Exclusion Criteria:

* 1)No pathological result.
* 2\) No or invalid contact information.
* 3\) The surveillance interval cannot be determined according to the surveillance guidelines, including poor bowel preparation, colorectal cancer or suspicious malignance, surgery or colorectal ESD history, those who fail to complete colonoscopy due to unbearable, pathological indications of not polyps, hamartoma or lymphoma polyps, history of ulcerative colitis, and so on.
* 4\) Has participated in other clinical trials, signed the informed consent form and is in the surveillance period of other clinical trials.
* 5\) Have drug or alcohol abuse or psychological disorder in the past five years.
* 6）Pregnancy.
* 7）Not suitable for recruitment after investigator evaluation because of other high-risk conditions.

Outcomes:
On-time Surveillance Rate
Generative Artificial Intelligence Nurse Staffing (GAINS) Study
Trial ID: NCT06978790
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-04-01
Completion date: 2029-03-31
Sponsor: University of Hawaii
Conditions: Burnout, Healthcare Workers
Interventions: Generative Artificial Intelligence Nurse Staffing (GAINS) Intervention
Enrollment: 660
Age range: 18 Years - None
Sex: ALL

Summary:
This study is guided by Maslach's Burnout Theory and with Normalization Process Theory supporting the implementation of the GAINS intervention by facilitating its integration into routine system-level practice. In Year 1, the investigative team will collaborate with hospital-based nursing leadership and key stakeholders to identify staffing-specific factors essential for operationalizing the GAINS AI model/intervention. In Year 1, the investigators will also conduct a survey amongst nursing staff to measure baseline burnout. In Year 2, the AI-staffing intervention will be implemented with the medical-surgical nursing float pool team. In Year 3, the investigators will first repeat the nurse burnout survey and second, expand the intervention to include the nursing assistant float pool team. In Year 4, the investigators will conduct the final burnout survey with nurses, assess feasibility of GAINS (target vs. actual staffing- nurses and nursing assistants), and assess preliminary efficacy of GAINS to reduce costs related to staffing. the investigators will compare outcomes at three time points (pre, mid, and post-intervention). Interviews with nurses, nursing assistants, unit nurse managers, and leadership will further explicate the intervention's acceptability, feasibility, and impact on burnout.

Eligibility criteria:
Inclusion Criteria:

* Registered nurses, nursing assistants, or key stakeholders
* Employed by The Queen's Medical Center
* Working at least 24 hours per week
* Position associated with medical-surgical units where float pool nurses work

Exclusion Criteria:

* Employees working less than 24 hours per week at The Queen's Medical Center
* Employees whose roles are not related to medical-surgical units

Outcomes:
Maslach's Burnout Inventory; Qualitative Interviews to Evaluate Feasibility, Normalization, and Acceptability of the GAINS Intervention
An Artificial Intelligence Model for Screening and Diagnosis of Renal Tumors Based on Non-Contrast CT
Trial ID: NCT07304492
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-01
Completion date: 2028-12
Sponsor: Fudan University
Conditions: Renal Neoplasms; Renal Cyst
Locations: Fudan university Shanghai Cancer Center
Countries: China
Enrollment: 10000
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
The goal of this observational study is to learn whether the artificial intelligence method can automatically identify and diagnose renal lesions using non-contrast CT or opportunistic screening.

Eligibility criteria:
Inclusion Criteria:

1. Patients who underwent an abdominal CT examination.
2. Patients with renal lesions were managed according to standard clinical pathways, which included follow-up, biopsy, or surgery.
3. Malignant lesions were pathologically confirmed; benign lesions were confirmed by either pathological diagnosis or imaging follow-up.
4. No prior treatment had been received for the renal disease.

Exclusion Criteria:

1. Patients refuse to undergo recommended follow-up, biopsy, or surgery, which precluded definitive diagnosis of the renal lesion.
2. Absence of complete pathological confirmation for lesions suspected to be malignant.
3. Patients have received any form of prior treatment for the renal lesion.
4. Poor image quality that hampered diagnostic evaluation.

Outcomes:
Building an intelligent diagnostic system for renal diseases based on CT scans.
A Mixed Methods Study to Assess the Clinical Effectiveness and Acceptability of qER Artificial Intelligence Software to Prioritise CT Head Interpretation.
Trial ID: NCT06027411
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2024-03-27
Completion date: 2024-08
Sponsor: Guy's and St Thomas' NHS Foundation Trust
Collaborators: Qure.ai; NHS Greater Glasgow and Clyde; Northumbria Healthcare NHS Foundation Trust; Oxford University Hospitals NHS Trust
Conditions: Trauma, Brain; Mass Lesion; Skull Fractures; Hemorrhage, Intracranial; Midline Deviation; Stroke, Ischemic; Encephalomalacia
Interventions: qER (qER EU 2.0)
Locations: NHS Greater Glasgow and Clyde; Guy's and St.Thomas Trusts; Northumbria Healthcare NHS Foundation Trust; Oxford University Hospitals
Countries: United Kingdom; United Kingdom; United Kingdom; United Kingdom
Enrollment: 16800
Age range: 18 Years - None
Sex: ALL

Summary:
Non-Contrast Computed Tomography (NCCT) of the head is the most common imaging method used to assess patients attending the Emergency Department (ED) with a wide range of significant neurological presentations including trauma, stroke, seizure and reduced consciousness. Rapid review of the images supports clinical decision-making including treatment and onward referral.

Radiologists, those reporting scans, often have significant backlogs and are unable to prioritise abnormal images of patients with time critical abnormalities. Similarly, identification of normal scans would support patient turnover in ED with significant waits and pressure on resources.

To address this problem, Qure.AI has worked to develop the market approved qER algorithm, which is a software program that can analyse CT head to identify presence of abnormalities supporting workflow prioritisation.

This study will trial the software in 4 NHS hospitals across the UK to evaluate the ability of the software to reduce the turnaround time of reporting scans with abnormalities that need to be prioritised.

Eligibility criteria:
Inclusion Criteria:

* Individuals undergoing Head CT scan at the ED / A\&E (Accident and Emergency Services).
* Non-contrast axial CT scan series with consistently spaced axial slices.
* Soft reconstruction kernel covering the complete Brain.
* Maximum slice thickness of 6mm.

Exclusion Criteria:

There are no explicit exclusion criteria for qER as all scans in inclusion criteria will be processed by qER. Exclusion criteria are implicit within the inclusion criteria listed above.

Outcomes:
Reporting turnaround time with qER prioritisation
Artificial Intelligence-Assisted/Computer Guided Ridge Splitting for the Treatment of Horizontal Ridge Defects With Simultaneous Guided Implant Placement: Randomized Controlled Clinical Study
Trial ID: NCT06135506
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-12-15
Completion date: 2026-01-19
Sponsor: Kafrelsheikh University
Conditions: Alveolar Bone Resorption; Dental Implant Failure Nos
Interventions: free-hand ridge splitting; AI/guided ridge splitting
Locations: Walid Elamrousy
Countries: Egypt
Enrollment: 22
Age range: 18 Years - 40 Years
Sex: ALL

Summary:
Aim of the current randomized clinical trial is to evaluate and compare the effectiveness of computer-guided ridge splitting approach assisted by artificial intelligence versus conventional approach combined with simultaneous implant Placement.

Eligibility criteria:
Inclusion Criteria:

* The target population with inadequate bone volume for implant placement due to width insufficiency of maxillary anterior alveolar ridges.
* Age ranges from 18-40 years of both sexes.
* No gender restrictions were considered for initial screening.
* Absence of any complicating systemic condition that may contraindicate surgical procedures and implant placement.
* Adequate oral hygiene.
* Eligible participants should present good general health and agree to random assignment to any of the two parallel study groups.
* Participants had minimum 3 months as post extraction healing period and horizontal maxillary anterior ridge defects with at least bone width of 3 mm and bone height of 13 mm.

Exclusion Criteria:

* Vertical ridge defect.
* Undercut on the labial/buccal side.
* Thick cortical bone without cancellous bone inside.
* Uncontrolled systematic disorders as, diabetes mellitus, uncontrolled periodontal disease, history of head and neck radiotherapy, smokers, pregnancy, noncompliant patients, allergy to the used medications, uncooperative individuals or those unable to attend the study follow-up appointments.

Outcomes:
alveolar ridge width
Using Large Language Models Such As GPT-4 to Assess Guideline Adherence in Patients With Chronic Obstructive Pulmonary Disease
Trial ID: NCT06410547
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-05-15
Completion date: 2025-04-01
Sponsor: Charite University, Berlin, Germany
Conditions: COPD
Interventions: LLM
Locations: Charité University
Countries: Germany
Enrollment: 78
Age range: 18 Years - None
Sex: ALL

Summary:
According to studies in the US and the Netherlands, 33-40% of patients with chronic conditions receive care that does not follow guideline recommendations. These findings have also been demonstrated in the management of COPD. This leads to under- or over-treatment of patients and, in the case of COPD, to exacerbations and hospitalisations. These exacerbations are a significant clinical problem, affecting patient's lung function, quality of life and mortality. They are also a burden on the healthcare system. Technological advances in artificial intelligence offer the opportunity to address these issues in COPD management. In the past year, there have been remarkable innovations in the field of natural language processing, especially through large language models such as GPT-4 from OpenAI and Bard or Gemini from Google. These models offer an opportunity to improve the implementation of evidence-based care in clinical practice.

This study is a prospective, randomised trial that will compare therapy on discharge for patients with COPD. One arm will receive no intervention, while the other arm will receive a treatment recommendation from an LLM. The study will compare the percentage of patients treated according to the guideline.

Eligibility criteria:
Inclusion Criteria:

* Diagnosis of COPD
* Consent
* Discharge after hospitalization

Exclusion Criteria:

* Lack of Consent

Outcomes:
Adherence to treatment guidelines at the time of hospital discharge; Percentage of patients treated in concordance with treatment guidelines at the time of hospital discharge
Application of Watson for Oncology in Hepatocellular Carcinoma Surgery
Trial ID: NCT03904667
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2017-01-01
Completion date: 2021-12-31
Sponsor: Zhujiang Hospital
Conditions: Liver Resection
Interventions: Watson on oncology surgery decision evaluation
Locations: Zhujiang Hospital of Southern Medical University
Countries: China
Enrollment: 150
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
The aim of this study was to evaluate the surgical decisions of Watson artificial intelligence in patients with hepatocellular carcinoma.

Eligibility criteria:
Inclusion Criteria:

1. 18 years≤ Age ≤80 years
2. Compling with the diagnosis criteria of complex hepatic carcinoma.
3. Primary hepatic carcinoma without intrahepatic or extrahepatic extensive cancer metastasis, the metastatic hepatic carcinoma whose primary focal has been controlled
4. Preoperative liver function is Child - Pugh grade A or B.
5. The patients are volunteered for the study.

Exclusion Criteria:

1. Patients with mental illness.
2. Patients can't tolerate the operation owe to a variety of basic diseases (such as severe cardiopulmonary insufficiency, renal insufficiency, cachexia and blood system diseases, etc.)
3. The patients refused to take part in the study.
4. There are other co-existed malignant tumors.
5. Benign liver diseases

Outcomes:
Operative outcomes