moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Scenario-based Clinical Interview Data Construction and User Experience Evaluation
Trial ID: NCT06415201
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-03-01
Completion date: 2027-12-31
Sponsor: Seoul National University Hospital
Collaborators: Dongguk University Medical Center; Eulji University Hospital
Conditions: Cancer
Interventions: Interactive Artificial Intelligence
Locations: Seoul National University Hospital
Countries: South Korea
Enrollment: 260
Age range: 19 Years - None
Sex: ALL

Summary:
The aims of this study are developments of mental health care services for cancer patients based on interactive artificial intelligence. To do these things, survey of unmet medical needs and data collection of clinical interviews will be done, and newly developed assessment and intervention scenarios will be simulated to cancer patients group.

Eligibility criteria:
Inclusion Criteria:

* Patients diagnosed with malignant tumor and undergoing treatment, or patients undergoing follow-up after completing curative treatment

Exclusion Criteria:

* Patients unable to consent to research or be interviewed due to cognitive decline

Outcomes:
Distress; Quality of life measurement; Stress; Self-esteem; Acceptance of Disability; Pain measurement; Insomnia; Quality of sleep; Fatigue; Anxiety & Depression; Fear of Disease Progression; Social Support
Real-world Study to Investigate Optimal Standard Treatment Selection for Solid Tumor Patients by Guided by Biologically-informed Multi-agent System
Trial ID: NCT06824792
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: PHASE4
Start date: 2025-03-01
Completion date: 2030-02-28
Sponsor: NING LI
Conditions: Advanced Solid Tumors
Interventions: Biologically-informed multi-agent system (Quasar) including targeted drugs Osimertinib, chemotherapy pemetrexed, immunotherapy pembrolizumab et al. approved by China CDE.
Locations: Cancer Institute and Hospital, Chinese Academy of Medical Sciences (Langfang Branch)
Countries: China
Enrollment: 3000
Age range: 18 Years - None
Sex: ALL

Summary:
This study is an exploratory cohort study conducted under real-world conditions, aiming to evaluate the feasibility of an artificial intelligence (AI)-guided standard treatment selection model for advanced solid tumors, as well as its superiority compared to clinician-selected treatment plans. A multi-agent system based on multimodal AI models will rank the priority of standard treatment options based on the personalized information of the patients, including including demographics, clinical information, and multi-omics data. The final treatment plan will be jointly selected by the patient and the clinician from the AI-recommended options, thereby delivering a personalized treatment.

Eligibility criteria:
Inclusion Criteria:

* Voluntarily participate in the clinical study, fully understand and be informed about the study, sign the informed consent form, and be willing and able to comply with and complete all trial procedures.
* Aged ≥18 years, no gender restrictions.
* Patients with advanced or metastatic malignant tumors confirmed by histology or cytology.
* Able to provide tumor tissue and peripheral blood samples for multi-omics testing, or able to provide qualified whole-exome sequencing and transcriptomics data.

Exclusion Criteria:

* As assessed by the investigator, no standard treatment is available, or the patient is unsuitable for guideline-recommended anti-tumor therapies.
* Other conditions deemed unsuitable for participation in this study by the investigator.

Outcomes:
Progression-free survival (PFS)
Integration of Omics-based Technologies and Artificial Intelligence to Identify Predictive Risk Models in a Air Force's Pilot Cohort for the Maintenance of Safety, Well-being, Health, and Performance to be Translated to Civil Population
Trial ID: NCT06372054
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-02-05
Completion date: 2027-02-05
Sponsor: Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico
Collaborators: University of Milan; Italian Air Force; A-Tono; Ministry of Defense, Italy
Conditions: Oxidative Injury; Stress Physiological; Discogenic Pain; Cardiovascular Risk Factor; Space Maintenance; Epigenetic Changes; LONGEVITY 1; Neuroplasticity; NGS
Interventions: Biological sample collection
Locations: CeMATA - Joint Center for Aerospace Medicine and Advanced Therapy
Countries: Italy
Enrollment: 200
Age range: 26 Years - 38 Years
Sex: ALL

Summary:
The goal of this observational study is to define a personalized risk model in the super healthy and homogeneous population of Italian Air Force high-performance pilots. This peculiar cohort conducts dynamic activities in an extreme environment, compared to a population of military people not involved in flight activity. The study integrates the analyses of biological samples (urine, blood, and saliva), clinical records, and occupational data collected at different time points and analyzed by omic-based approaches supported by Artificial Intelligence. Data resulting from the study will clarify many etiopathological mechanisms of diseases, allowing the creation of a model of analyses that can be extended to the civilian population and patient cohorts for the potentiation of precision and preventive medicine.

Eligibility criteria:
Inclusion Criteria:

* Being part of the Italian Air Force, as in active flight service or ground staff
* Age between 26 and 38 years
* Consent to collect biological samples and use the wearable device to monitor exposure parameters

Exclusion Criteria:

* Age \< 25 years and \> 39 years
* no signature on informed consent

Outcomes:
Assessment of flight-related exposure data and molecular modifications
Collection of Electronic Health Records (EHR) for Validation of Artificial Intelligence Based Tool for Data Quality Assessment
Trial ID: NCT05144230
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-02
Completion date: 2022-04
Sponsor: University of Portsmouth
Collaborators: Portsmouth Hospitals NHS Trust
Conditions: Data Quality
Enrollment: 60000
Age range: None - None
Sex: ALL

Summary:
Electronic Health Record Systems (EHR) play an integral role in healthcare practice, enabling health organisations to collect, access and manage data more consistently. There is also a great deal of interest in using EHR data to improve decision-making and accelerate medical interventions. However, like all information systems, they are prone to data quality problems such as incomplete records, values outside normal ranges and implausible relationships. These problems are expected to become more prevalent as more organisations adopt electronic health record systems, aggregate, share and explore health data. The investigators believe current efforts to improve health data quality can be made more effective if backed by appropriate technology in the form of a readily accessible intelligent tool. Building on this, the investigators developed an Artificial Intelligence (AI) tool for automating data quality assessment of health data. In this study, the investigators evaluate the AI tool using a real-world dataset.

Eligibility criteria:
Inclusion Criteria:

* No specific exclusion criteria

Exclusion Criteria:

* No specific exclusion criteria

Outcomes:
Data quality dimensions prevalence; Consistency of AI tool; Validity of AI tool detection
Real-Time Cholangioscopy Artificial Intelligence Evaluation of Biliary Strictures
Trial ID: NCT05779436
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2023-04-01
Completion date: 2027-09
Sponsor: Mayo Clinic
Conditions: Biliary Stricture
Locations: University of Massachusetts; Mayo Clinic Minnesota
Countries: United States; United States
Enrollment: 100
Age range: 18 Years - None
Sex: ALL

Summary:
The purpose of this study is to demonstrate the feasibility and validity of a previously developed peroral cholangioscopy (POC) convolutional neural network (CNN) to determine the etiology of biliary strictures when used in real-time.

Eligibility criteria:
Inclusion Criteria:

* Age ≥ 18 years old
* Anticipation that patient may undergo POC during an endoscopic retrograde cholangioscopy
* Pregnant patients can be included in the study. Use of the POC-CNN does not convey any new risk to the patient nor fetus.
* Non-English speaking subjects will be included in the study. Consent for the procedure itself is always done with the assistance of an interpreter via telephone or video. Use of the POC-CNN will be discussed with all patients via this interpreter during the procedure consent.

Exclusion Criteria:

* Age \<18 years old
* No indication that cholangioscopy will be performed during endoscopic retrograde cholangiopancreatography (ERCP)
* Prisoner

Outcomes:
POC-CNN Model AUC
The COMBO CAD Study: Characterization cOMparison Between twO CAD Systems
Trial ID: NCT05141409
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2022-01-26
Completion date: 2022-09-30
Sponsor: Istituto Clinico Humanitas
Conditions: Artificial Intelligence
Interventions: Artificial Intelligence
Locations: Department of Gastroenterology, Humanitas Research Hospital
Countries: Italy
Enrollment: 500
Age range: 40 Years - None
Sex: ALL

Summary:
Implementation of clinical strategies based on optical diagnosis of \<5 mm colorectal polyps may lead to a substantial saving of economic and financial resources. Despite this, 84.2% of European endoscopists reported not to use such strategies - also named as leave-in situ and resect- and-discard - in their practice due to the fear of an incorrect optical diagnosis.

Indeed, accuracy of optical diagnosis is operator-dependent, and values reported in the community setting are below the safety thresholds proposed for its incorporation in clinical practice.

Artificial intelligence (AI) is being increasingly explored in different domains of medicine, particularly those entailing image analysis. As optical diagnosis involves subitaneous processing of multiple images, searching for specific visual clues, and recognizing well-defined visual patterns, AI systems has the potential to help endoscopists in distinguish neoplastic from non-neoplastic polyps, making the characterization process more reliable and objective. Computer-Aided-Diagnosis systems aiming at characterization are called CADx.

Preliminary data on CADx showed a high feasibility and accuracy of AI for optical diagnosis of colorectal polyp, and initial experiences in clinical practice confirmed preliminary results.

To assess the potential benefit and risk of AI-assisted optical diagnosis with standard colonoscopy, we exploited two new Computer-Aided-Diagnosis systems (CAD-EYE® Fujifilm Co., and GI-Genius® Medtronic) that provide the endoscopist with a real-time polyp characterization without the need of optical magnification.

Eligibility criteria:
Inclusion Criteria:

\- All patients aged 40 or older undergoing a colonoscopy for gastrointestinal symptoms, fecal immunohistochemical test positivity, primary screening or post-polypectomy surveillance

Exclusion Criteria:

* subjects with personal history of CRC, or IBD.
* Subjects affected with Lynch syndrome or Familiar Adenomatous Polyposis.
* patients with inadequate bowel preparation (defined as Boston Bowel Preparation Scale \< 2 in any colonic segment).
* patients with previous colonic resection.
* patients on antithrombotic therapy, precluding polyp resection.
* patients who were not able or refused to give informed written consent.

Outcomes:
AI-assisted optical diagnosis performance
A Prospective Cohort Study of the Articulate Medical Intelligence Explorer's Ability to Take an Initial Triage History From Patients Prior to an Urgent Care Visit
Trial ID: NCT06911398
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2025-04-02
Completion date: 2026-04-06
Sponsor: Beth Israel Deaconess Medical Center
Collaborators: Google LLC.
Conditions: Artificial Intelligence (AI); Clinical Reasoning
Interventions: Articulate Medical Intelligence Explorer (AMIE)
Locations: Beth Israel Deaconess Medical Center
Countries: United States
Enrollment: 100
Age range: 18 Years - None
Sex: ALL

Summary:
The purpose of this study is to determine the feasibility of a conversational artificial intelligence (AI) system to have a meaningful clinical conversation with a patient prior to an urgent care visit with their primary care physician. In this study, patients who are seeking an urgent care visit (that is, any type of medical visit with their primary care provider for a new complaint) will first have a conversation with an AI system. This interaction with the AI system will happen less than a week before their visit with their physician, and will be supervised by an independent physician who will interrupt in case there are any concerns about patient safety. After the interaction, a summary of the conversation will be sent to the patient's PCP, who will review prior to the in-person visit.

The researchers will investigate:

* Patient views on the AI system
* PCP views on the AI system
* Overall safety, as measured by the physician safety supervisor
* Quality of clinical conversations, measured by standardized rubrics
* Quality of diagnostic and management plans generated by the AI; these will not be shared with the patient or physician, but will be generated after the fact and compared with the actual diagnosis and management plan.

Eligibility criteria:
Inclusion Criteria:

* Age \> 18
* English included as a primary language in the electronic health record
* Patient is seeking episodic (urgent) care at HCA

Exclusion Criteria:

* Age \< 18
* English not listed as a primary language in the electronic health record
* Visit reason is related to pregnancy
* Visit reason is a psychiatric concern
* Visit is to establish care, or for follow-up care

Outcomes:
Chat terminations; Patient-centered evaluation of clinical dialogue; Clinical skills-based assessment of clinical dialogue; Communication skills evaluation of clinical dialogue; Patient satisfaction pre-AMIE encounter; Patient satisfaction post-AMIE encounter; PCP satisfaction; Patient satisfaction post-PCP encounter
Validation of the Clinical Decision Support Systems (CDSS) Developed in the EU Horizon PREPARE Project in the Rehabilitative Treatment of Idiopathic Scoliosis
Trial ID: NCT07379593
Status: ACTIVE_NOT_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-11-07
Completion date: 2027-05-30
Sponsor: Istituto Scientifico Italiano Colonna Vertebrale
Conditions: Adolescent Idiopathic Scoliosis (AIS)
Interventions: CDSS simulation; Focus group - clinicians; Focus group - patients
Locations: ISICO - Italian Scientific Spine Institute
Countries: Italy
Enrollment: 178
Age range: 10 Years - None
Sex: ALL

Summary:
The study aims to validate both the predictive capacity and the clinical usability of the Clinical Decision Support System (CDSS) developed within the PREPARE project (European Union Grant Agreement 101080288 PREPARE HORIZON-HLTH-2022-TOOL-12-01) for the management of idiopathic scoliosis (IS). This will be achieved through simulation-based studies.

Eligibility criteria:
Inclusion criteria:

Initial visits or follow-up appointments of patients with IS attending the Milan site of ISICO - Italian Scientific Spine Institute for rehabilitative treatment;

Age between 10 and 18 years;

Informed consent to participate in the study

Exclusion criteria:

Visits not related to the treatment of IS (other spinal conditions);

Patients undergoing treatment at other clinics or hospitals.

Outcomes:
Changes in clinical decision
Evaluating the Efficacy of AI-Augmented Cognitive Rehabilitation in Mild Cognitive Impairment
Trial ID: NCT07474038
Status: ENROLLING_BY_INVITATION
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-03-16
Completion date: 2027-12-31
Sponsor: Kaiser Permanente
Collaborators: NewDays, Inc.
Conditions: Mild Cognitive Impairment (MCI)
Interventions: AI-Supported Cognitive Rehabilitation
Locations: Kaiser Permanente Southern California
Countries: United States
Enrollment: 130
Age range: 65 Years - None
Sex: ALL

Summary:
Mild cognitive impairment (MCI) is associated with increased risk of progression to dementia, highlighting the need for accessible interventions to support cognitive health.

This randomized controlled trial will evaluate the safety and efficacy of a 12-month artificial intelligence (AI)-supported cognitive rehabilitation program for older adults with MCI. Participants will be recruited from an existing research study conducted within Kaiser Permanente Southern California and randomized 1:1 to either (1) AI-supported cognitive rehabilitation or (2) usual care alone.

The intervention combines clinician-delivered telehealth cognitive rehabilitation sessions with daily AI-guided cognitive exercises and education. The primary outcome is change in global cognition at 6 months measured by the Montreal Cognitive Assessment (MoCA)-BLIND. Secondary outcomes at 6 and 12 months include additional measures of cognition, subjective memory, goal attainment, mood, and loneliness. Exploratory outcomes include engagement with the AI intervention and health-related behavioral outcomes derived from electronic health record data.

Participant safety will be monitored throughout the study through adverse event tracking, review of AI interactions for safety concerns, and predefined procedures for responding to psychological or clinical risk.

This study will determine whether integrating AI-guided cognitive exercises with clinician-delivered rehabilitation improves cognitive and related outcomes in older adults with MCI compared to usual care.

Eligibility criteria:
Inclusion Criteria:

* Age 65 years or older
* Diagnosis of mild cognitive impairment (MCI)
* Montreal Cognitive Assessment (MoCA)-BLIND adjusted score (13-18)
* Able to provide informed consent
* English fluency sufficient to complete study assessments and participate in intervention sessions
* Access to and ability to use a computer or mobile device with internet connectivity

Exclusion Criteria:

* Current receipt of ongoing behavioral health treatment or psychotherapy from a Kaiser Permanente clinician
* Diagnosis of bipolar disorder, schizophrenia spectrum disorder, or other serious mental illness that would interfere with participation
* Significant communication impairment that would prevent participation in conversational or telehealth sessions

Outcomes:
Change in MoCA-BLIND Score at 6 Months
Artificial Intelligence-assisted Colonoscopy Versus Conventional Colonoscopy for Missed Lesions in the Proximal Colon: A Prospective Multi-center Randomized Study in Asia
Trial ID: NCT04294355
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-03-01
Completion date: 2022-04-15
Sponsor: The University of Hong Kong
Collaborators: Tan Tock Seng Hospital; Institute of Gastroenterology and Hepatology, Vietnam
Conditions: Colon Adenoma; Colon Polyp
Interventions: Artificial intelligence-Assisted colonoscopy; Conventional colonoscopy
Locations: Queen Mary Hospital; Tan Tock Seng Hospital; Institute of Gastroenterology and Hepatology
Countries: China; Singapore; Vietnam
Enrollment: 216
Age range: 40 Years - None
Sex: ALL

Summary:
This is a prospective multi-center randomized study is to determine whether the use of artificial intelligence (AI)-assistance could reduce the miss rates of polyps and adenomas in the proximal colon during tandem examination

Eligibility criteria:
Inclusion Criteria:

* All adult patients, aged 40 or above, undergoing outpatient colonoscopy in the participating centers will be recruited

Exclusion Criteria:

* history of inflammatory bowel disease
* history of colorectal cancer
* previous bowel resection (apart from appendectomy)
* Peutz-Jeghers syndrome, familial adenomatous polyposis or other polyposis syndromes
* bleeding tendency or severe comorbid illnesses for which polypectomy is considered unsafe.

Outcomes:
Proximal adenoma missed rate
Retrospective, Multi-Center Study to Evaluate the Performance of DermDx as an Adjunctive Tool for Primary Care Physicians in the Detection of Skin Cancers
Trial ID: NCT06463860
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-06-12
Completion date: 2024-11-27
Sponsor: MetaOptima Technology Inc.
Conditions: Skin Cancer
Interventions: DermDx
Locations: Remote
Countries: United States
Enrollment: 81
Age range: None - None
Sex: ALL

Summary:
The proposed study is a pivotal, multi-center retrospective reader study designed to determine whether the use of DermDx as a concurrent reading aid improves the performance of primary care physicians (PCPs) in diagnosing skin cancers.

Eligibility criteria:
Inclusion criteria:

* The subjects must be Primary Care Physicians who are board certified in family medicine or internal medicine.

Exclusion criteria:

* Subjects not meeting inclusion criteria.

Outcomes:
The change in the diagnostic sensitivity of Primary Care Physicians (PCPs) with and without the use of DermDx in the diagnosis of lesions suspicious of skin cancer; The change in the diagnostic accuracy of Primary Care Physicians (PCPs) with and without the use of DermDx in the diagnosis of lesions suspicious of skin cancer
Research of the Application of Artificial Intelligence Model 'PANDA': A MultiCenter, Prospective Randomized Controlled Clinical Trial
Trial ID: NCT06528223
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-08-15
Completion date: 2027-12-31
Sponsor: Zhejiang University
Conditions: Pancreatic Cancer
Interventions: biopsy or operation
Locations: the First Affiliated Hospital, School of Medicine, Zhejiang University; the First Affiliated Hospital, School of Medicine, Zhejiang University
Countries: China; China
Enrollment: 200000
Age range: None - None
Sex: ALL

Summary:
The research objective of this project is to conduct a large-scale and prospective real-world validation of the Pancreatic Cancer Screening Model PANDA, which was developed based on deep learning and plain CT scans in previous studies. This validation will be carried out across different scenarios at the First Affiliated Hospital of Zhejiang University, leveraging clinical big data. The goal is to verify the model's role in suggesting and supplementing the diagnosis of PDAC in clinical practice, thereby laying the groundwork for large-scale opportunistic screening of PDAC.

Eligibility criteria:
Inclusion Criteria:

* The participants have undergone chest and/or abdominal plain CT scans at outpatient, inpatient, or physical examination centers

Exclusion Criteria:

* Chest CT scan without pancreatic coverage
* Patients undergoing thoracic/abdominal surgical procedures that affect or alter the anatomical display of the pancreas (esophageal/gastric/pancreatic/vascular/ERCP postoperative, etc.)
* Scanning non-standard examinations, such as significant respiratory motion artifacts

Outcomes:
OS
Based on Artificial Intelligence, Augmented Reality and New Optical Imaging Technology, the Research and Development of a Skin Diseases Intelligent Diagnosis and Treatment System: A Multi-centric Clinical Trial in China
Trial ID: NCT05463523
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-04-15
Completion date: 2025-12-31
Sponsor: Xiangya Hospital of Central South University
Conditions: Skin Diseases; Artificial Intelligence; Augmented Reality; Medical Imaging
Interventions: A Real-time Augmented Reality Device with Artificial Intelligence Integration
Locations: Xiangya Hospital
Countries: China
Enrollment: 200
Age range: None - None
Sex: ALL

Summary:
In response to clinical needs, infrared multi-spectral images are combined with traditional clinical images and other multi-modal data to build a more efficient intelligent auxiliary diagnosis system and intelligent equipment for skin health and diseases, including skin lesions automatically segmentation on skin diseases images, automatically design surgical margin and planning for skin tumor surgery.

Eligibility criteria:
Inclusion Criteria:

* Informed consented.
* With a diagnosis of skin disease made by at least 3 dermatologists.
* Without life-threatening risk to intervention.
* Requires surgical treatment (For devices).

Exclusion Criteria:

* Having difficulties to follow-up.
* Poor general condition.

Outcomes:
Doctors' Evaluation