moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Comparative Analysis Between Artificial Intelligence vs. Human Generated Nutrition Messages for Colorectal Cancer Survivors
Trial ID: NCT06661590
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-10-15
Completion date: 2025-02-06
Sponsor: University of Minnesota
Conditions: No Disease State or Condition
Interventions: Nutritional Messaging
Locations: The Hormel Institute - University of Minnesota, Medical Research Center
Countries: United States
Enrollment: 6
Age range: 18 Years - None
Sex: ALL

Summary:
Colorectal cancer survivors often face unique nutritional challenges and require support in their recovery and long0term health. While human experts have traditionally provided that support, there has been an increase in the use of Large Language Models (LLM) in medicine and in nutrition. The LLM offers a potential supplementary resource for generating personalized nutritional advice, specifically in personalized messaging. However, the efficacy and reliability of these AI-generated messages in comparison to human expert advice remain underexplored specific to this population.

This study aims to compare the nutrition-related content generated by popular LLMs-ChatGPT, Claude, Gemini, and Co-Pilot-against messages crafted by human experts. By evaluating the generated content in terms of readability, thematic relevance, medical relevance, perceived effectiveness, and implementation of participants' clinical practice, this research will provide insights into the strengths and limitations of using AI for nutritional guidance in colorectal cancer care.

Eligibility criteria:
Inclusion Criteria:

* 18+ years of age
* Currently practicing Registered Dietitian Nutritionist with at least five years of experience working with oncology patients and survivors in their practice.
* Must have access to computer and internet access.

Exclusion Criteria:

* Non-English speakers, as the study materials and assessments are in English.
* Experts with conflicts of interest related to any of the LLMs that are being evaluated.

Outcomes:
Outcome Measure Title: Readability of Nutrition Messages; Outcome Measure Title: Thematic Relevance of Nutrition Messages; Outcome Measure Title: Medical Relevance to Colorectal Cancer Survivors; Outcome Measure Title: Perceived Effectiveness of Nutrition Messages; Outcome Measure Title: Potential for Implementation in Clinical Practice
ARtificial Intelligence for heAlth and Prevention of Smoking-related Diseases
Trial ID: NCT06626178
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-10-01
Completion date: 2028-11-01
Sponsor: Scientific Institute San Raffaele
Conditions: Lung Cancer Screening Program; Artificial Intelligence (AI)
Interventions: Computed tomography (CT) scan low dose; Blood sampling; Tissue sampling (lung); Spirometry; Questionnaires; Smoking cessation program; Carbon monoxide measurment; Cardiovascular primary prevention
Locations: Scientific Institute Ospedale San Raffaele
Countries: Italy
Enrollment: 2840
Age range: 18 Years - None
Sex: ALL

Summary:
The study is an interventional pilot study. The study is designed to be monocentric and it presents additional procedues.

Eligibility criteria:
High-risk screening subjects

Inclusion Criteria:

* Age \>= 50 years old
* Active smokers
* Former smokers (from no more than 15 years)
* Pack/year \>20
* Risk-prediction model from Prostate, Lung, Colorectal, and Ovarian study (PLCOm2012) \>1.2%
* Provision and signature of informed consent

Exclusion Criteria:

* Previous or concurrent neoplastic disease, excluding skin cancers
* Cognitive or other problems that could hinder the collection of informed consent
* Severe pulmonary or extra pulmonary disease
* Previous low-dose computed tomography (CT) scan in the past 12 months

Previous high-risk positive screening subjects

Inclusion Criteria:

* Subjects enrolled in previous lung cancer screening with the presence of lung nodules \>4 mm and candidate to additional computed tomography (CT)
* Signed informed consent

Exclusion Criteria:

\- None

Previous high-risk negative screening subjects

Inclusion Criteria:

* Subjects enrolled in previous lung cancer screening in this Institute with negative computed tomography (CT)
* Signed informed consent

Exclusion Criteria:

\- None

Lung Cancer patients

Inclusion Criteria:

* Patients with diagnosis or suspicious diagnosis of lung cancer candidate to surgical treatment or already submitted to it
* Patients with diagnosis of lung cancer treated with surgical resection
* Signed informed consent

Exclusion Criteria:

* computed tomography (CT) scans not available at San Raffaele Hospital
* Previous neoadjuvant treatment

Outcomes:
Creation of Artificial Intelligence (AI) algorithm
A Randomized Controlled Pilot Study on AI-Based Imagery Rescripting for Childhood Criticism Memories
Trial ID: NCT07189715
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-06-01
Completion date: 2025-12-31
Sponsor: University of Social Sciences and Humanities, Warsaw
Conditions: Generalized Anxiety; Fear of Failure; Rumination
Interventions: Imagery Rescripting Intervention (AI-Generated)
Locations: SWPS University (University of Social Sciences and Humanities); Poznań Laboratory of Affective Neuroscience
Countries: Poland
Enrollment: 80
Age range: 18 Years - 35 Years
Sex: ALL

Summary:
This pilot randomized controlled trial will investigate the feasibility and effectiveness of using generative artificial intelligence to create personalized therapeutic scripts for imagery rescripting (ImRs). Eighty participants will listen to autobiographical scenarios based on their own memories of childhood criticism and neutral events. The scenarios will be generated by the Gemini large language model and reviewed by trained experimenters. On Day 1, all participants will be exposed to both critical and neutral scenarios and randomly assigned to either an experimental group receiving an ImRs intervention or a control group receiving no therapeutic modification. Skin conductance and subjective emotional ratings will be collected during the session, with follow-up questionnaires administered one week later. In addition, cognitive-behavioral therapists will evaluate the quality of the generated scripts. The study aims to assess emotional and physiological responses to AI-generated content, compare outcomes between groups, and explore the potential of large language models in scalable psychological interventions.

Eligibility criteria:
Inclusion Criteria:

* Age 18-35
* Score ≥ 8 on GAD-7 (Plummer et al., 2016)
* Ability to recall at least two childhood memories involving parental criticism

Exclusion Criteria:

* History of prolonged physical or sexual abuse
* Current psychotherapy or psychopharmacology
* PTSD diagnosis (DSM-5 screening)
* Substance abuse (TAPS tool)

Outcomes:
Generalized Anxiety Disorder Scale (GAD-7); The Performance Failure Appraisal Inventory; Intrusive Thought Frequency (Rumination Inventory - adapted); Skin Conductance Level (SCL); Emotional Response Ratings (Subjective)
Construction of an Early Breast Cancer Screening Warning Model Based on Self-supervised Learning With Plain MRI Scans and Prospective Clinical Utility Evaluation
Trial ID: NCT07205276
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-10-01
Completion date: 2027-12-01
Sponsor: Second Affiliated Hospital, School of Medicine, Zhejiang University
Collaborators: Alibaba DAMO Academy
Conditions: Breast Cancer Detection; Early Detection of Cancer; AI (Artificial Intelligence)
Interventions: Non-contrast multiparametric breast MRI with AI-based radiomics analysis; Standard radiologist reading of non-contrast multiparametric breast MRI
Enrollment: 30000
Age range: 30 Years - 70 Years
Sex: FEMALE

Summary:
Breast cancer is the most common malignant disease among women worldwide, with rising incidence and younger age at onset in China. Early detection is critical for improving survival, yet current screening methods such as mammography and ultrasound show limited sensitivity in Chinese women, particularly those with dense breast tissue. Contrast-enhanced MRI offers higher diagnostic performance but its use is limited by high costs, safety concerns with gadolinium-based contrast agents, and limited accessibility.

This investigator-initiated trial aims to evaluate the clinical application of non-contrast multiparametric MRI, combined with advanced artificial intelligence algorithms, for the early detection and diagnosis of breast cancer. The study will collect MRI imaging data from multiple centers and integrate radiomic features across T2-weighted imaging, diffusion-weighted imaging, and apparent diffusion coefficient maps. A deep learning-based model will be developed and validated to improve lesion detection, differential diagnosis, and risk stratification.

The ultimate goal of this project is to establish a safe, accurate, and scalable breast cancer screening pathway suitable for Chinese women. By reducing dependence on invasive procedures and contrast agents, and by leveraging AI for standardization and efficiency, this approach may significantly improve early detection rates and contribute to better patient outcomes.

Eligibility criteria:
* Inclusion Criteria:

  1. Female, age 30-70 years
  2. Completed breast MRI scan, including at least T2WI, DWI, and ADC sequences
  3. Multimodal data acquired within the same time window (≤90 days)
  4. A clear clinical outcome: pathologically confirmed or ≥12-24 months of negative follow-up
  5. The time window between imaging examination and outcome determination was ≤90 days
  6. Signed informed consent
* Exclusion Criteria:

  1. Absolute contraindications to MRI (pacemaker, cochlear implant, ocular metal foreign body, etc.)
  2. Pregnant or lactating women
  3. Recent history of breast surgery/radiotherapy (≤6 months) or imaging after neoadjuvant therapy
  4. Substandard image quality (severe motion artifact, signal-to-noise ratio below threshold)
  5. Incomplete clinical data or time window exceeded
  6. Known breast cancer metastasis or recurrence

Outcomes:
Diagnostic accuracy of AI-based non-contrast multiparametric MRI for breast cancer detection
Developing Artificial Intelligence Solutions to Improve Diagnosis and Risk Stratification in Acute Pulmonary Embolism
Trial ID: NCT06093217
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-01-08
Completion date: 2025-02-06
Sponsor: Royal United Hospitals Bath NHS Foundation Trust
Collaborators: University of Bath; University of Bristol; London School of Hygiene and Tropical Medicine
Conditions: Pulmonary Embolism
Interventions: Artificial Intelligence
Locations: Royal United Hospitals, Bath NHS Foundation Trust
Countries: United Kingdom
Enrollment: 3872
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this exploratory observational study is to assess the feasibility and real-world clinical impact of implementing Artificial Intelligence (AI) software for the detection of acute Pulmonary Embolism (PE) in patients who undergo Computed Tomography Pulmonary Angiogram (CTPA). The main questions that this study aims to answer are:

\[Question 1\] What is the real-world impact of AI on the clinical outcomes and decision making by radiologists and clinicians in the management of acute PE?

\[Question 2\] Is AI software for the detection of acute PE acceptable to use in clinical practice and do they have a favourable impact on clinical workload?

\[Question 3\] Is it cost-effective to implement AI software for the detection of acute PE in clinical practice?

Patients having a CTPA for the detection of acute PE will have their imaging analysed by AI software in combination with a human radiologist. Researchers will aim to compare the clinical and radiology specific outcomes with a retrospective cohort of patients who have had standard routine radiology reporting.

Eligibility criteria:
Inclusion Criteria:

* Patients over 18 years of age
* Patient requiring CTPA to exclude or diagnose acute PE

Exclusion Criteria:

* Patients under 18 years of age
* Patients who have registered with the national opt-out scheme for research
* CTPA performed for reasons other than acute PE
* CTPA performed for acute PE but reported by external radiologists
* Incomplete or discontinued CTPA scans
* Insufficient quality CTPA to allow for analysis by a radiologist

Outcomes:
Proportion of patient decisions made in line with evidence based best practice guidelines after introducing AI technology within CTPA reporting
The Use of Machine Learning Techniques for the Differential Diagnosis Between Eosinophilic Granulomatosis With Polyangiitis and Hypereosinophilic Syndrome
Trial ID: NCT07275190
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-06-01
Completion date: 2026-12
Sponsor: Fondazione IRCCS Policlinico San Matteo di Pavia
Conditions: EGPA - Eosinophilic Granulomatosis With Polyangiitis; HES - Hypereosinophilic Syndrome
Interventions: Blood draw for the laboratory assessment
Locations: Fondazione IRCCS Policlinico San Matteo, SC Reumatologia
Countries: Italy
Enrollment: 60
Age range: 18 Years - None
Sex: ALL

Summary:
The purpose of this study is to collect clinical, laboratory, and instrumental data from patients with eosinophilic granulomatosis with polyangiitis or hypereosinophilic syndrome, which will then be analyzed using artificial intelligence techniques with the aim of identifying characteristics that differentiate the two diseases and can predict the response to the treatment plan.

Eligibility criteria:
Inclusion Criteria:

1. 18 years of age or over
2. A diagnosis of EGPA or HES
3. Willing and able to give informed written consent, or willing to give permission for a nominated friend or relative to provide written informed assent if they are unable to do so because of physical disabilities

Exclusion Criteria:

1. Lack of a confirmed diagnosis of EGPA or HES.
2. Other causes of eosinophilia

Outcomes:
characteristics that are different between HES and EGPA
AI-Flex: An Artificial Neural Network Integrated Flexible Multi-Modal Sensor Patch for Real-Time Continuous Physiological Monitoring and Inference
Trial ID: NCT05581290
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-11-17
Completion date: 2023-11-17
Sponsor: Mayo Clinic
Conditions: Cardiac Disease
Interventions: AI-Flex
Locations: Imon Banerjee
Countries: United States
Enrollment: 19
Age range: 18 Years - 85 Years
Sex: ALL

Summary:
The purpose of this research is to validate the prototype AI-Flex device. Researchers at Mayo Clinic developed a new a flexible multi-modal bio-sensing device, AI-Flex, with integrated artificial intelligence (AI) capability. Integration of sensing and AI analysis on the same device removes the need for data storage on the cloud for later analysis. The goal of the device is to allow real-time monitoring of patient health and timely intervention based on patient health condition. It is hoped that the proposed flexible device will allow intimate skin contact using ultra-thin (\<10 µm) geometry to reduce or eliminate relative movement between the skin and flexible epidermal sensors even during rapid motion of the subject, which would significantly improve the sensor signal quality for AI analysis.

Eligibility criteria:
Inclusion Criteria:

* Subject must be referred for Cardiology Clinic Visit.
* Subject must have underlying cardiac structural disease or condition to increase yield of clinically significant transmission
* Subject must be 18 - 85 years of age.

Exclusion Criteria:

\- \< 18 years of age or \> 85 years of age.

Outcomes:
AI-Flex ECG Accuracy; AI-Flex Temperature Measurement Accuracy; AI-Flex PPG Measurement Accuracy
Improving Pancreatic Cancer Care by the Use of Computational Science and Technology
Trial ID: NCT06055010
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2014-01-01
Completion date: 2029-12-31
Sponsor: UMC Utrecht
Collaborators: Catharina Ziekenhuis Eindhoven; St. Antonius Hospital; Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA); University Medical Center Groningen; Medisch Spectrum Twente; Erasmus Medical Center; Maastricht University Medical Center; Leiden University Medical Center; NYU Langone Health; Johns Hopkins University; Delft University of Technology
Conditions: Pancreatic Cancer; Pancreatic Cyst; Pancreatic Adenocarcinoma; Pancreatic Diseases; Pancreas Adenocarcinoma; Pancreas Cyst
Interventions: No interventions
Locations: Regional Academic Cancer Center Utrecht
Countries: Netherlands
Enrollment: 5000
Age range: None - None
Sex: ALL

Summary:
The goal of the IMPACT project is to set up a data sharing infrastructure between expert centers for pancreatic surgery that enables training, testing and validation of computer science tools to improve quality of care for patients with pancreatic cancer.

Eligibility criteria:
Inclusion Criteria:

* ≥18 years of age
* Patients who received diagnostic procedures and/or treatment for (suspected) benign and malignant pancreatic lesions as registered in the Dutch Pancreatic Cancer Project (PACAP) audit database and healthy individuals who received an abdominal CT-scan (controls)

Exclusion Criteria:

\- Subjects who object to the use of their data for the purpose of scientific research

Outcomes:
Subproject dependent
Effect of Artificial Intelligence on Nutritional Status of Children Post Cardiac Surgery
Trial ID: NCT04782635
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-03-25
Completion date: 2021-08-03
Sponsor: Al-Shifa Trust Eye Hospital
Conditions: Post-cardiac Surgery
Interventions: mobile based application; Pamphlet
Locations: Armed Forces Institute of Cardiology and National Institute of Heart Disease; Maryam Zahid
Countries: Pakistan; Pakistan
Enrollment: 61
Age range: 2 Years - 12 Years
Sex: ALL

Summary:
The objective of the study will be to assess the effect of artificial intelligence on nutritional status of children post cardiac surgery in comparison to usual care group.To assess usefulness of diet related mobile application in comparison to usual care group.To identify diet related problems among children post cardiac surgery in both groups (usual care group and intervention group ).

Eligibility criteria:
Inclusion Criteria:

* Children of age 2-12 (early and middle childhood) years will be included in the study.
* Surgeries within first and second class of RACHS score are included in the study.
* Patients with smart phones and internet facilities are included in the study.
* Patients with caregivers who can read English are included in the study
* Patients discharged from the hospital will be selected

Exclusion Criteria:

* Patients with multiple congenital abnormalities and surgeries without an RACHS score are excluded.
* Patient who will be rehospitalized will be excluded from the research

Outcomes:
Weight changes; Caloric intake; Proteins intake; Mobile application usage; nutritional issues
Screening for Peripartum Cardiomyopathies Using Artificial Intelligence (SPEC-AI) in Nigeria
Trial ID: NCT05438576
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-08-15
Completion date: 2024-05-15
Sponsor: Mayo Clinic
Collaborators: Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD); National Center for Advancing Translational Sciences (NCATS); National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS)
Conditions: Cardiomyopathy; Pregnancy Related
Interventions: Digital stethoscope electrocardiogram
Locations: Rasheed Shekoni Specialist Hospital; University of Ilorin Teaching Hospital; Olabisi Onabanjo University Teaching Hospital; University College Hospital; Aminu Kano Teaching Hospital; Lagos University Teaching Hospital
Countries: Nigeria; Nigeria; Nigeria; Nigeria; Nigeria; Nigeria
Enrollment: 1232
Age range: 18 Years - 49 Years
Sex: FEMALE

Summary:
This study will evaluate the effectiveness of an artificial intelligence-enabled ECG (AI-ECG) for cardiomyopathy detection in an obstetric population in Nigeria.

Eligibility criteria:
Inclusion Criteria:

* Currently pregnant or within 12 months postpartum
* Willing and able to provide informed consent

Exclusion Criteria:

* Complex congenital heart disease (single ventricle physiology or significant shunts with cardiac structural changes)
* Significant conduction abnormalities (ventricular pacing on recorded ECG, pacemaker dependence, or severely abnormal/bizarre QRS morphology on ECG tracings)
* Unable or unwilling to provide consent

Outcomes:
Left Ventricular Ejection Fraction (LVEF) <50%
Adenoma Detection Rate Using Artificial Intelligence System in China
Trial ID: NCT03840590
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2019-07-10
Completion date: 2019-12-05
Sponsor: Changhai Hospital
Collaborators: Sixth Affiliated Hospital, Sun Yat-sen University; Shanghai Zhongshan Hospital; Tianjin People's Hospital; Tianjin Nankai Hospital
Conditions: Colorectal Neoplasms; Colonic Polyp; Adenoma
Interventions: CSK AI system; Standard Colonoscopy
Locations: Sixth affiliated Hospital, Sun Yat-Sen University; Shanghai Zhongshan Hospital; Tianjin Nankai Hospital; Tianjin People's Hospital
Countries: China; China; China; China
Enrollment: 743
Age range: 45 Years - 80 Years
Sex: ALL

Summary:
The primary aim of this study is

\- to explore the usefulness of Artificial Intelligence system in colonoscopy on adenoma detection rate (ADR). Other aims include to explore the data below when Artificial Intelligence is used.

Mean adenomas detected per procedure, MAP Proximal Adenoma detection rate, pADR Polyp detection rate, PDR Proximal polyp detection rate, pPDR Mean polyps detected per procedure, MPP Withdrawal time, WT Cecal intubation rate, CIR Cecal intubation time, CIT

Eligibility criteria:
Inclusion Criteria:

* All patients referred for screening, surveillance, or diagnostic colonoscopy
* All patients must be able to give informed consent

Exclusion Criteria:

* Patients with any absolute contraindications to colonoscopy
* Patients with established or suspicion of large bowel obstruction or pseudo-obstruction
* Patients with known colon cancer or polyposis syndromes
* Patients with known colonic strictures
* Patients with known severe diverticular segments (that is likely to impede colonoscope passage)
* Patients with active colitis (ulcerative colitis, Crohn's colitis, diverticulitis, infective colitis)
* Patients lacking capacity to give informed consent
* Pregnancy
* Patients who are on clopidogrel, warfarin, or other new generation anticoagulants who have not stopped this for the procedure.
* Patients who are attending for a therapeutic procedure or assessment of a known lesion

Outcomes:
Adenoma detection rate, ADR
Artificial Intelligence Enhanced ECG to Detect Cardiac Amyloidosis: Protocol for a Pragmatic Cluster Randomized Clinical Trial (PREDICT-AMY)
Trial ID: NCT05557162
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-06-19
Completion date: 2025-07-23
Sponsor: Mayo Clinic
Conditions: Cardiac Amyloidosis
Interventions: AI ECG Amyloid algorithm
Locations: Mayo Clinic in Arizona; Mayo Clinic in Florida; Mayo Clinic in Rochester; Mayo Clinic Health System - Eau Claire
Countries: United States; United States; United States; United States
Enrollment: 20143
Age range: 18 Years - None
Sex: ALL

Summary:
The purpose of this study is to assess a novel artificial intelligence (AI)-enabled electrocardiogram (ECG)-based screening tool for improving the diagnosis of cardiac amyloidosis (CA).

Eligibility criteria:
Inclusion Criteria:

* Mayo Clinic cardiology or hematology providers who care for adult patients
* Mayo Clinic providers who consent to participate on this study

Exclusion Criteria:

\- None

Outcomes:
To determine if AI ECG algorithm and enhanced algorithms and education enable earlier diagnosis of cardiac amyloidosis