moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
AI-driven Clinical Decision Support to Reduce Hospital-Acquired Venous Thromboembolism
Trial ID: NCT06939803
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-12-17
Completion date: 2027-03-31
Sponsor: Vanderbilt University Medical Center
Conditions: Venothromboembolism
Interventions: Risk model-driven CDS
Locations: Vanderbilt University Medical Center
Countries: United States
Enrollment: 2236
Age range: None - None
Sex: ALL

Summary:
Hospital-acquired blood clots (HA-VTE) are the leading cause of death in hospitalized patients in the US. Each year, about 900,000 people get blood clots, costing between $7 and $10 billion in medical expenses. HA-VTE is the second leading cause of long-term disability and causes significant health issues and deaths in both adults and children. About 1 in 3 people who get blood clots experience long-term complications. Reducing HA-VTE is a major challenge.

This study will test a new AI method to predict and prevent HA-VTE. The goal is to see if this AI tool can reduce the number of HA-VTE cases in the Vanderbilt Health System, which includes both urban and rural hospitals.

The AI tool, called VTE-AI, calculates a risk score without needing input from doctors. It will suggest reconsidering blood clot prevention measures for patients who don't have them ordered and have no reasons to avoid them. This suggestion will be made after admission and daily during the hospital stay.

Currently, doctors manually calculate a risk score and choose a prevention option. This study will compare the effectiveness of the AI tool against the current manual method in reducing HA-VTE cases. The study will randomly assign half of the patients to use the AI tool and the other half to the standard manual method.

Eligibility criteria:
Inclusion Criteria:

* Inpatient admission to Vanderbilt Adult Hospital, Vanderbilt Tullahoma Harton Hospital, Vanderbilt Bedford County Hospital, or Vanderbilt Wilson County Hospital

Exclusion Criteria:

* None

Outcomes:
Incidence of Hospital Acquired VTE
Artificial Intelligence for the Diagnosis of Oral Lesions
Trial ID: NCT07529769
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-04
Completion date: 2026-12
Sponsor: Assistance Publique - Hôpitaux de Paris
Collaborators: BPIfrance; Institut Universitaire de Cancérologie, Sorbonne University; Health Data Hub (France)
Conditions: Oral Potentially Malignant Disorders; Oral Cavity Squamous Cell Carcinoma
Enrollment: 5000
Age range: 18 Years - None
Sex: ALL

Summary:
Squamous cell carcinomas of the upper aerodigestive tract are among the most common cancers worldwide, with the oral cavity being the most frequent site. Oral cavity squamous cell carcinomas (OCSCC) represent a major cause of morbidity and mortality, mainly due to high rates of locoregional or metastatic recurrence and the frequent occurrence of second primary tumors. Unlike oropharyngeal squamous cell carcinomas, human papillomavirus (HPV) is not involved in the carcinogenesis of OCSCC.

In some cases, OCSCC develop from oral potentially malignant disorders (OPMDs), such as leukoplakia and erythroplakia, which have a worldwide incidence of 3-5%. The malignant transformation rate of OPMDs ranges from 3% to 50%, reflecting their marked heterogeneity. Although several clinical, histological, and molecular factors have been proposed to identify patients at high risk of malignant transformation, none have demonstrated sufficient clinical utility to date. In other cases, OCSCC arise from clinically normal oral mucosa in patients with OPMDs located at a distance and/or with established risk factors, particularly tobacco and alcohol use.

Currently, no chemopreventive or preventive strategy has been established as a standard of care to prevent malignant transformation of OPMDs. Improving the prognosis of OCSCC therefore requires the development of tools to better identify high-risk OPMDs and to enable the earliest possible diagnosis. Early detection of OPMDs is essential for secondary prevention of OCSCC. However, conventional oral examination based on visual inspection and palpation has limited sensitivity, and clinical recognition of OPMDs remains challenging. Consequently, there is a clear need for improved methods to enhance early detection and risk stratification of OPMDs.

Main objective:

To develop a tool to aid in the diagnosis of cancerous lesions in the oral cavity using Artificial Intelligence (AI). This tool appears promising in meeting the current needs of the oral cavity practitioner community.

Eligibility criteria:
Inclusion Criteria:

* Patient aged ≥ 18 years
* Patients followed up in the Oral Mucosa Pathology Department (Maxillofacial Surgery and Stomatology Department, Pitié-Salpêtrière Hospital, AP-HP, Paris) between January 1, 1970, and December 31, 2023, with a diagnosis of potentially malignant oral lesion and/or oral cavity cancer.

Exclusion Criteria:

* Photograph of the lesion unavailable (in standard care)

Outcomes:
Develop a tool to aid in the diagnosis of cancerous lesions in the oral cavity using Artificial Intelligence (AI)
Combining Biomarkers and Electronic Risk Scores to Predict AKI in Hospitalized Patients
Trial ID: NCT05988658
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-01-05
Completion date: 2028-03-01
Sponsor: University of Chicago
Collaborators: National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK); University of Wisconsin, Madison
Conditions: Acute Kidney Injury; Biomarkers
Interventions: ESTOP - AKI 2.0
Locations: University of Chicago Medical Center; University of Wisconsin Hospital
Countries: United States; United States
Enrollment: 800
Age range: 18 Years - None
Sex: ALL

Summary:
The study's objective is to evaluate the additive value of renal biomarkers (from blood and urine) for identifying individuals at high risk for severe acute kidney injury (AKI) above that of a novel natural language processing (NLP)-based AKI risk algorithm. The risk algorithm is based on electronic health records (EHR) data (labs, vitals, clinical notes, and test reports). Patients will enroll at the University of Chicago Medical Center and the University of Wisconsin Hospital, where the risk score will run in real time. The risk score will identify those patients with the highest risk for the future development of Stage 2 AKI and collect blood and urine for biomarker measurement over the subsequent 3 days.

Eligibility criteria:
Inclusion Criteria:

1. Age ≥ 18 years
2. E-STOP AKI 2.0 score in the top 10% of risk (historically from all hospitalized patients) within the last 12 hours. (First time across this 10% risk threshold during this hospital stay).
3. Admitted to an inpatient ward, intermediate, or ICU care at the University of Chicago Medical Center (UCMC) or University of Wisconsin Health (UWHealth). (No Emergency Department patients)
4. Patient or their legally authorized representative must be able to read, speak, and understand English, for the purposes of consenting. Otherwise, inclusion in this protocol will be done without regard to race, ethnic origin or gender

Exclusion Criteria:

1. Voluntary refusal or missing written consent of the patient / legal representative.
2. Patients with a known history of end-stage renal disease on dialysis (including renal transplantation).
3. Patients without a measured serum creatinine value during their inpatient stay.
4. Patients with a creatinine \>4.0 mg/dl at the time of admission or available in the EHR from the last 6 months
5. Patients with prior episode of KDIGO defined AKI during this same hospitalization- regardless of E-STOP AKI 2.0 score
6. Patients with prior renal consultation during their admission.
7. Patient with an E-STOP AKI 2.0 above the top 10% risk threshold more than 12 hours ago during this same hospital stay.
8. Incarcerated patients
9. Pregnant patients

Outcomes:
Developing KDIGO stage 2 AKI
AI-driven Clinical-trial Trial-Information and Viability Assessment Tool for EHRs (ACTIVATE)
Trial ID: NCT07232043
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-06
Completion date: 2030-06-30
Sponsor: Dana-Farber Cancer Institute
Collaborators: National Cancer Institute (NCI)
Conditions: Cancer
Interventions: MatchMiner-AI Artificial Intelligence Tool
Locations: Dana-Farber Cancer Institute
Countries: United States
Enrollment: 70000
Age range: 18 Years - None
Sex: ALL

Summary:
This study aims to develop and evaluate ACTIVATE, an AI-driven tool for clinical trial information and viability assessment using electronic health records (EHRs). The project will leverage retrospective and prospective EHR data to build and validate algorithms that identify potentially eligible participants for clinical trials and facilitate trial matching.

Eligibility criteria:
Inclusion Criteria:

* 3.1 The potentially eligible patient population includes any adult (≥18 years old) with a cancer diagnosis receiving care at DFCI. No direct patient recruitment will occur as part of this protocol; all data will be obtained retrospectively or prospectively from routine clinical documentation and electronic health records. TrialForecast will involve aggregate queries of this dataset for cohort size estimation. The randomized interventional component (TrialMatch) is a health system level email "nudge" to treating oncologists providing a list of clinical trial options for patients who have progressive disease based on their imaging reports as detected using our previously developed, validated, and deployed AI model for that purpose. 23-25 Secondary outcomes in our study will include oncologist satisfaction with information delivered via these pipelines. All DFCI oncologists at any DFCI-owned/operated site (Longwood, Chestnut Hill, and regional campus sites) will be eligible to use our pipeline and may receive notifications about clinical trial options for their patients. In 2024, there were approximately 593 such oncologists who had outpatient appointments with at least one patient. Clinicians will constitute study participants as well, since they will have the opportunity to provide feedback on our pipeline to be analyzed by the study team.
* 3.2 Our project will focus on adults with cancer treated at DFCI, as above. We will not have any mechanism for identifying, targeting, or excluding pregnant women or prisoners.

Exclusion Criteria:

* 3.2 Our project will focus on adults with cancer treated at DFCI, as above. We will not have any mechanism for identifying, targeting, or excluding pregnant women or prisoners.

Outcomes:
Proportion clinical trials
Data Acquisition for Connected Network for EMSs Comprehensive Technical-support Using Artificial Intelligence
Trial ID: NCT05939258
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2021-04-19
Completion date: 2021-12-31
Sponsor: Yonsei University
Conditions: Emergency Patients Being Transported by Rescue Ambulance
Locations: Severance hospital
Countries: South Korea
Enrollment: 15296
Age range: 18 Years - None
Sex: ALL

Summary:
Currently, the domestic emergency medical system is disconnected from the information flow between hospitals, emergency sites, and control agencies, which are participants in the emergency medical system, and there are limitations in collecting and utilizing integrated data in emergency situations \[1\]. In addition, due to the lack of manpower for emergency services at the site and the lack of a real-time patient information delivery system, sufficient data records are not made to reflect the situation at the emergency site, and emergency patient information at the pre-hospital stage is not delivered to the transfer hospital \[1\]. Records of pre-hospital patient information that are currently being prepared are often written by hand, relying on the memory of paramedics after completing patient transfer, so the data is highly inaccurate and cannot be guaranteed to be reliable\[2\]. In particular, in the case of the four major serious emergency diseases, which are called cardiac arrest, severe trauma, cardiovascular emergency, and cerebrovascular emergency, the patient information identified in the emergency stage is very important in determining the severity, so it is very important to collect real-time patient information in the field to evaluate the severity, and based on the results of this evaluation, it is possible to select a medical institution suitable for treatment \[3,4\]. In addition, in the case of these serious emergency diseases, since targeted treatment is determined to be performed within a certain time, if the medical staff of the medical institution is aware of the patient's information before the patient arrives at the hospital, it is possible to prepare in advance for emergency treatment, thereby increasing the performance rate of emergency treatment within a reasonable time \[5,6,7\].

Eligibility criteria:
Inclusion Criteria:

* Patients are transported from Seodaemun Fire Station, Mapo Fire Station to Severance Hospital Emergency Care Center via 119 ambulance

Exclusion Criteria:

* Minors under the age of 18

Outcomes:
Area Under Receiver operating characteristics, AUROC
Collection of Endoscopic Images and Videos for Validation of Artificial Intelligence Algorithms for Computer Aided Detection and Characterisation of Early Gastrointestinal Neoplasia
Trial ID: NCT04937647
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2021-06-16
Completion date: 2026-06-03
Sponsor: Portsmouth Hospitals NHS Trust
Conditions: Gastrointestinal Neoplasms
Interventions: collection of endoscopic images and videos
Locations: Portsmouth Hospitals NHS University Trust
Countries: United Kingdom
Enrollment: 1650
Age range: 18 Years - None
Sex: ALL

Summary:
This study aims to validate and evaluate AI algorithms for detection and characterization of early GI neoplasia.

Eligibility criteria:
Inclusion Criteria:

-The participant must meet ALL of the following criteria to be considered eligible for the study:

* Male or Female, aged 18 years or above.
* Requiring gastroscopy and/or colonoscopy to investigate gastrointestinal symptoms, or as part of screening or ongoing surveillance of GI neoplasia.
* Participant is willing and able to give informed consent for participation in the study.

Exclusion Criteria

The participant may not enter the study if ANY of the following apply:

* Patients with endoscopically active severe oesophagitis
* Poor bowel preparation

Outcomes:
neoplasia detection rate
Computer-aided, Evidence-based System Improved Clinical Diagnostic Accuracy of Certificated-Physicians in Acute Abdominal Pain
Trial ID: NCT05497258
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-08-15
Completion date: 2022-10-01
Sponsor: Renmin Hospital of Wuhan University
Conditions: Artificial Intelligence; Diagnoses Disease
Interventions: Artificial intelligence assistant system
Locations: Renmin Hospital of Wuhan University
Countries: China
Enrollment: 151
Age range: 18 Years - None
Sex: ALL

Summary:
This is a study to validate the effect of the intelligent diagnostic evidence-based analytic system in acute abdominal pain augmentation. Included physicians were randomly assigned into control or AI-assisted group. In this experiment, the whole electronic health record of each acute abdominal pain patient was divided into two parts, signs and symptoms recording (including chief complaint, present history, physical examination, past medical history, trauma surgery history, personal history, family history, obstetrical history, menstrual history, blood transfusion history, drug allergy history) and auxiliary examination recording (including laboratory examination and radiology report). For each case, the control group readers will first read the signs and symptoms recording of electronic health record and make a clinical diagnosis. Then the readers have to decide to either order a list of auxiliary examinations or confirm the clinical diagnosis without further examination. If the readers choose to order examinations, the corresponding examination results will be feedback to the readers, and the readers can then decide to either continue to order a list of auxiliary examinations or make a confirming diagnosis. Such cycle will last until the reader make a confirming diagnosis. For the AI-assisted readers, the physicians were additionally provided with the feature extracted by IDEAS-AAP, a list of suspicious diagnoses predicted by IDEAS-AAP, and corresponding diagnostic criteria according to guidelines. After the readers get the examination results, the IDEAS-AAP will renew its diagnosis prediction

Eligibility criteria:
Inclusion Criteria:

1. Males or females who are over 18 years old;
2. After qualified medical education and obtained the Certificate of medical practitioner;

Exclusion Criteria:

1. Physicians without qualified medical education and didn't obtain the Certificate of medical practitioner;
2. The researcher believes that the subjects are not suitable for participating in clinical trials.

Outcomes:
The accuracy of clinical diagnosis.
A Single-center, Prospective, Parallel Randomized Controlled Study Evaluating the Clinical Effectiveness of an Intelligent Graphic Report System for Upper Gastrointestinal Endoscopy
Trial ID: NCT05851885
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-05-20
Completion date: 2024-05-20
Sponsor: Renmin Hospital of Wuhan University
Conditions: Endoscopy, Gastrointestinal; Artificial Intelligence
Interventions: AI-based reporting system
Enrollment: 125
Age range: 18 Years - None
Sex: ALL

Summary:
The objective of this study is to assess the effectiveness of an AI-based reporting system for upper gastrointestinal endoscopy. The primary question that this study aims to address is whether the reporting system can enhance the completeness and accuracy of endoscopic reports when assisted by AI, as drafted by endoscopists. Patients will be randomly assigned to either the experimental group or the control group. In the experimental group, physicians will draft EGD reports with the assistance of the AI-based reporting system, while in the control group, physicians will use the conventional reporting system to draft EGD reports. At the same time, the AI-based reporting system will automatically generate a report of the EGD examination.

Eligibility criteria:
Inclusion Criteria:

1. Aged 18 years
2. Aim to undergo screening, surveillance, and diagnosis
3. Undergo sedated EGD
4. Able to read, understand, and sign informed consent

Exclusion Criteria:

1. EGD contraindications
2. Not suitable for sedated endoscopy after anaesthesia evaluation
3. Biopsy contraindications
4. Active upper gastrointestinal bleeding or emergency oesophagogastroduodenoscopy (EGD)
5. Pregnancy
6. Upper gastrointestinal surgery or residual stomach
7. Not suitable for recruitment after investigator evaluation because of other high-risk conditions

Outcomes:
Completeness of reporting lesions
Enhanced Deep Learning Model for Diagnosis of Pancreatic Solid Lesions Through Multimodal Clinical Images
Trial ID: NCT05706415
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2023-01-21
Completion date: 2023-02-21
Sponsor: Changhai Hospital
Collaborators: The Third People's Hospital of Chengdu; Ruijin Hospital; Sun Yat-sen University; Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
Conditions: AI Assist in the Diagnosis of Pancreatic Solid Lesions
Interventions: Clinicians will review the suggestions of a hypothetical AI
Locations: CT and EUS; CT and EUS
Countries: China; China
Enrollment: 200
Age range: 18 Years - 75 Years
Sex: ALL

Summary:
Solid lesions of the pancreas mainly include tumor and non tumor lesions. More than 90% of pancreatic tumor lesions are pancreatic cancer, which is characterized by high mortality and poor prognosis and requires surgical treatment; Non-tumor lesions of the pancreas are mainly inflammatory lesions, which usually do not require surgical treatment, but can be treated with drugs. The common ones are chronic pancreatitis and autoimmune pancreatitis, with a good prognosis. Clinically, the differential diagnosis between them is very difficult. Multi-disciplinary diagnosis and treatment of MDT makes our understanding of pancreatic diseases increasingly rich and in-depth. From disease diagnosis to preoperative evaluation and curative effect evaluation, non-invasive imaging involves almost every link under MDT mode. In view of this, improving the differential diagnosis of pancreatic solid space-occupying lesions on imaging will be more conducive to the diagnosis and treatment under MDT mode, so new technologies such as artificial intelligence should be considered. Our goal is to develop a clinically applicable artificial intelligence system, which uses multiple modes to simulate the routine clinical workflow and assist in the diagnosis of benign and malignant pancreatic solid space-occupying lesions.

Eligibility criteria:
Inclusion Criteria:

* pancreatic solid mass in CT and EUS

Exclusion Criteria:

* insufficient imaging quality of CT or EUS
* endoscopic ultrasound non accessible lesions

Outcomes:
Researchers use artificial intelligence (AI) support system to assist in diagnosis of pancreatic solid space-occupying lesions
Research of Pathological Imaging Diagnosis of Ocular Tumors Based on New Artificial Intelligence Algorithm
Trial ID: NCT04695015
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2020-12-31
Completion date: 2022-06-01
Sponsor: Peking University
Conditions: Melanoma (Skin); Melanoma in Situ; Nevus Eye; Sebaceous Gland Carcinoma of the Eyelid; Basal Cell Carcinoma; Squamous Cell Carcinoma in Situ; Ocular Tumor
Enrollment: 100
Age range: None - None
Sex: ALL

Summary:
The purpose of this study is to establish a standardized process for obtaining digital pathological image information of ocular tumors; use modern pathological techniques to obtain the co-expression information of multiple biomarkers in the pathological tissues of ocular tumors, and finally construct standardized digital ocular tumors with biomarkers Pathology image database.

Eligibility criteria:
Inclusion Criteria:

1. Patients diagnosed with eye tumors and undergoing eye tumor surgery.
2. Patients sign informed consent for sample collection and sample transfer agreement, and can cooperate with long-term regular follow-up requirements.

Exclusion Criteria:

1. Patients who are unable to undergo tumor surgery or retain samples due to various reasons .
2. Patients who are positive for hepatitis B, HIV, and syphilis.
3. Patient compliance is poor.

Outcomes:
To compare the diagnostic accuracy of OPAL and IHC for melanoma and other tumors.
Developing an Artificial Intelligence Chatbot to Promote HIV Testing
Trial ID: NCT04910984
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-07-20
Completion date: 2027-07-31
Sponsor: Yale University
Collaborators: University of Malaya; National Institute of Allergy and Infectious Diseases (NIAID)
Conditions: HIV Infections
Interventions: Chatbot messages; Attention-matched educational materials
Locations: University of Malaya
Countries: Malaysia
Enrollment: 80
Age range: 18 Years - None
Sex: MALE

Summary:
In this study, the feasibility of a Chatbot in promoting HIV testing in a pilot RCT with 80 men who have sex with men in Kuala Lumpur, Malaysia will be studied. Participants will be randomized to Chatbot or treatment as usual (TAU) groups. Participants in the intervention group will receive automated personalized messages containing HIV testing-related information, motivation and skills.

Eligibility criteria:
Inclusion Criteria:

* cis-gender male
* 18+ years;
* had condomless sex with men in the past 6 months
* has a smartphone
* speaks Bahasa Malay or English.

Exclusion Criteria:

* Not able to read Malay or English

Outcomes:
Change in HIV testing