moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
A Pilot Study on the Use of Personalised Non-Fungible Token (NFT) Artwork Generated From Digital Therapeutics (DTx) as a Trial Participation Incentive (Avatar.DTx)
Trial ID: NCT06647030
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-07-02
Completion date: 2026-06-02
Sponsor: Institute for Digital Medicine (WisDM)
Conditions: Healthy; Digital Health; Incentive Interventions
Interventions: CURATE.DTx sessions; Interview
Enrollment: 15
Age range: 21 Years - None
Sex: ALL

Summary:
NFTs are blockchain-based digital and physical assets traded on digital marketplaces. Current and exploratory NFT use cases include art, collectibles, in-game items, real estate, ticketing, events, fintech, licenses, IDs, and healthcare. NFT marketplaces, online games, virtual worlds, and open-source coding platforms use NFTs to incentivize participation. In trials, where the effectiveness of financial remuneration to increase trial participation is unclear, NFTs could encourage involvement. Considering the increasing opportunities for blockchain technology and NFT use in both the economy and society, we present a pilot study protocol to gauge the interest and feasibility of using NFTs as payment for trial participants, the first of its kind to our knowledge. Additionally, as the global population ages and chronic diseases become more prevalent, innovative solutions to sustain engagement in longevity-focused interventions are needed. Harnessing CURATE.AI, an indication-agnostic artificial intelligence (AI) platform that modulates the intensity of interventions to generate truly personalized profiles - or digital avatars, we will develop N-of-1 learning trajectory profiles for fifteen healthy volunteers trained on the CURATE.DTx, a digital therapeutic (DTx) platform. The profiles will be artistically modified and minted for the participants in the Ethereum blockchain, whereafter the recipient can choose to keep, display, or trade their NFT. Through interviews, we will evaluate the interest and acceptability of NFTs as an incentive for trial participation. This pioneering exploration connects AI-driven personalized medicine with inventive blockchain solutions. Our research aims to advance the field of digital therapeutics and pave the way for novel approaches to patient-centered care and incentivization strategies in clinical trials.

Eligibility criteria:
Inclusion Criteria:

* Healthy
* Agree to audio-recording of the interview
* Meet the cognitive criteria for adequate mental capacity as determined by the MMSE.

Exclusion Criteria:

* Difficulty to understand and read English (the DTx text and audio are in English)
* Significant hearing impairment
* Evidence/diagnosis of severe cognitive impairment
* Inability to complete the study at the judgement of the investigators

Outcomes:
Interest and Acceptability of Personalized NFTs as Incentives
The Development and Evaluation of a Co-designed, AI-based Information-providing Chatbot for Sarcoma Patients: A Pilot Randomised Controlled Trial
Trial ID: NCT07505732
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2027-04-01
Completion date: 2028-07-31
Sponsor: The Hong Kong Polytechnic University
Collaborators: Queen Mary Hospital, Hong Kong; Prince of Wales Hospital, Shatin, Hong Kong; Queen Elizabeth Hospital, Hong Kong
Conditions: Sarcoma
Interventions: ASCC (sarcoma-specific AI chatbot); Routine Clinical Care
Enrollment: 78
Age range: 18 Years - None
Sex: ALL

Summary:
This study aims to develop and examine the feasibility and preliminary effectiveness of an AI-based sarcoma chatbot building on ChatGPT (ASCC) to address the information needs of sarcoma patients. We hypothesized: 1) the ASCC will demonstrate good usability; 2) the study will be feasible in terms of all feasibility indicators; 3) the experimental group will report improved satisfaction and self-efficacy, decreased anxiety and stress than the control groups upon completion of the intervention. The ASCC will be developed using a co-design approach. A pilot randomized controlled trial will then be conducted in the three oncology wards of collaborative hospitals. Seventy-eight sarcoma patients will be recruited and randomized to the experimental group (n=39) and the control group (n=39). The experimental group will use the ASCC available 24/7 via voice or text for disease-related questions for one month while the control group will receive usual care.

Eligibility criteria:
Inclusion Criteria:

* (1) diagnosed with soft tissue sarcoma;
* (2) receiving active treatment at any stage;
* (3) aged≥18 years;
* (4) able to speak Cantonese, Mandarin and English;
* (5) access to an electronic device enabling ASCC use

Exclusion Criteria:

* (1) patients with unstable physical or mental conditions;
* (2) inability to communicate logically;
* (3) using chatbots (e.g., ChatGPT, Deepseek) for sarcoma information in the last month;
* (4) mental health disorders documented by the medical system

Outcomes:
Screening rate; Eligibility rate; Consent rate; Randomization rate; Attendance rate; Retention rate; Completion rate; Proportion of missing data; Adverse events
The Application of Artificial Intelligence in Screening Adrenal Nodules in Low-dose Chest CT
Trial ID: NCT07198152
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-09-01
Completion date: 2027-01-30
Sponsor: Peking University First Hospital
Conditions: Adrenal Incidentalomas; Thoracic CT-scan
Interventions: None intervention
Locations: Peking University First Hospital
Countries: China
Enrollment: 600
Age range: 18 Years - None
Sex: ALL

Summary:
By collecting chest low-dose spiral CT plain scan images, extracting effective features from the adrenal gland regions, training and validating an artificial intelligence-based automatic diagnostic algorithm for adrenal nodules, we will ultimately develop an artificial intelligence software with independent intellectual property rights that is specifically designed for screening adrenal nodules in chest low-dose CT scans.

Eligibility criteria:
Inclusion Criteria:

1. The age of the population is over 18 years old, regardless of gender;
2. Images from chest low-dose CT plain scan during routine physical examination;

Exclusion Criteria:

1. Patients with a history of malignant tumors;
2. Imaging images with poor quality, artifacts, etc., which make analysis impossible.

Outcomes:
Overall Diagnostic Accuracy
Multimodal Radiology Report to Improve Patient-centered Radiology
Trial ID: NCT07159438
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-12
Completion date: 2028-12
Sponsor: Harvard Medical School (HMS and HSDM)
Conditions: Health Literacy; Diagnostic Imaging; Imaging Results; Artificial Intelligence
Interventions: AI-Generated Video Radiology Report Explanation
Enrollment: 200
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this study is to learn if AI-generated video explanations help people better understand their radiology reports. The main question it aims to answer is:

Do AI-generated videos help participants understand their medical imaging results better than written reports alone? Participants will send their own radiology images and written reports to the research team; receive a personalized AI-generated video that explains their results in easy-to-understand language; watch their video explanation (about 1-5 minutes long); and complete 15-minute online survey about how well the video helped them understand their results.

Eligibility criteria:
Inclusion Criteria:

* Age 18+ adults who have taken a radiology examination.

Exclusion Criteria:

* N/A

Outcomes:
Change in Patient Understanding of Radiology Report After AI-Generated Video Explanation; Change in Patient Understanding of Radiology Images After AI-Generated Video Explanation
Transforming Rehabilitation: Personalised Care for a Better Quality of Life (PREPARE, 1st Part)
Trial ID: NCT06249958
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-01-08
Completion date: 2027-01-08
Sponsor: I.R.C.C.S Ospedale Galeazzi-Sant'Ambrogio
Conditions: Osteoarthritis Secondary; Osteoarthritis Primary; Osteoarthritis, Knee; Osteoarthritis, Hip
Interventions: Rehabilitation; Verticalisation
Locations: IRCCS Ospedale Galeazzi Sant'Ambrogio
Countries: Italy
Enrollment: 2000
Age range: 18 Years - None
Sex: ALL

Summary:
General aim of the project: building and validating an Artificial Intelligence (AI)-based prediction model on rehabilitation outcomes (total joint replacement - TJR - of the hip and knee for primary and secondary osteoarthritis)

Purpose of this specific approval request: identifying data from patients admitted for total joint replacement surgery (hip and knee) at Istituto di Ricerca e Cura a Carattere Scientifico (IRCCS) Istituto Ortopedico Galeazzi (IOG) in 2019 and subsequently discharged to inpatient rehabilitation in the same institution.

Eligibility criteria:
Inclusion Criteria:

* Patients admitted for inpatient surgery at IRCCS Galeazzi Orthopedic Institute in 2019 (January-December) and subsequently discharged to inpatient rehabilitation in the same structure.
* Ages ≥18 years
* International Classification of Disease, Ninth Revision, Clinical Modification (ICD-9CM) Diagnoses: 715.15, 16, 25, 26 (primary and secondary osteoarthritis of the hip and knee)
* ICD-9CM Surgical procedures: 81.51, 81.54 (total joint replacement of the hip and knee).

Exclusion Criteria: N/A

Outcomes:
Barthel Index Total score; Functional autonomy measure (total score); Standard hospital evaluation of patient gait; Standard hospital evaluation of walking aids needed by the patient; Standard hospital evaluation of place of discharge; Standard hospital evaluation of care burden at discharge; Patient-reported outcomes; Functional autonomy measure subdomains
AI Chatbot-Enabled ePRO Symptom Monitoring and Self-Management for Early Cardiovascular-Kidney-Metabolic Syndrome
Trial ID: NCT07577284
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-05-30
Completion date: 2029-07-31
Sponsor: Taipei Medical University
Conditions: Cardiovascular Kidney Metabolic Syndrome
Interventions: AI chatbot-enabled nurse-led program.; Usual Care
Locations: Taipei Medical University
Countries: Taiwan
Enrollment: 72
Age range: 40 Years - None
Sex: ALL

Summary:
This randomized clinical trial aims to evaluate whether an AI chatbot-enabled, nurse-led electronic patient-reported outcome (ePRO) intervention can improve symptom management and self-care in adults with early-stage cardiovascular-kidney-metabolic (CKM) syndrome. The study will also assess the safety and feasibility of the intervention. Researchers will investigate whether the program reduces symptom burden, improves patient activation and health literacy, and lowers healthcare utilization such as emergency department visits and hospitalizations compared with usual ePRO monitoring alone. A total of 72 participants will be randomly assigned to either an intervention group, which receives weekly AI chatbot-supported symptom monitoring combined with nurse-led feedback and self-management support, or a control group receiving ePRO monitoring without feedback. Participants will complete PROMIS-29 assessments weekly and attend study evaluations at baseline, 3 months, and 6 months, while clinical outcomes and healthcare use are tracked through electronic medical records.

Eligibility criteria:
Inclusion Criteria:

* Adults ≥40 years
* Classified as having stage CKM syndrome (stage 1-2)
* Receiving outpatient care
* Able to communicate in Mandarin/Taiwanese, and
* Having access to a smartphone/tablet

Exclusion Criteria:

* End-stage kidney disease requiring dialysis or are listed for kidney transplantation
* Have terminal illness (\<6 months life expectancy)
* Have significant cognitive impairment
* Currently enrolled in another interventional study targeting cardiovascular-kidney-metabolic syndrome or multimorbidity

Outcomes:
PROMIS-29 v2.1
Wearable Augmented Prediction of Burnout in Nurses: A Synergy of Engineering, Bioethics, Nursing and Wellness Sciences
Trial ID: NCT05481138
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2022-08-15
Completion date: 2025-03-01
Sponsor: Mayo Clinic
Collaborators: National Institute of Nursing Research (NINR)
Conditions: Burnout, Healthcare Workers
Locations: Mayo Clinic in Florida; Mayo Clinic in Rochester
Countries: United States; United States
Enrollment: 531
Age range: 20 Years - 62 Years
Sex: ALL

Summary:
The purpose of this study is to develop a technology to predict burnout in RNs by measuring workplace, psychological, and physiological factors experienced by nurses.

Eligibility criteria:
Inclusion Criteria:

* Employed as a registered nurse at Mayo Clinic Rochester or Mayo Clinic Florida.
* Primary work assignment is one of the following: medical-surgical units, operating and recovery rooms, emergency room, intensive/cardiac care units, or other special care units (including neonatal intensive care units and newborn nurseries).
* Proficient in written and spoken English.
* Able to provide written informed consent.

Exclusion Criteria:

\- Any exception to above criteria.

Outcomes:
Change in Maslach Burnout Inventory-Human Services Survey (MBI-HSS)
Development and Validation of a New Artificial Intelligence System for Automated Detection of Colorectal Polyps During Colonoscopy
Trial ID: NCT04359355
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2020-01-01
Completion date: 2020-05-31
Sponsor: University of Erlangen-Nürnberg Medical School
Conditions: Adenoma Colon; Polyp of Colon
Interventions: Artificial Intelligence System for Detection of colorectal polyps
Locations: University Hospital Erlangen
Countries: Germany
Enrollment: 40
Age range: 18 Years - 85 Years
Sex: ALL

Summary:
In this observational pilot study, we assess the diagnostic performance of an artificial intelligence sytem for automated detection of colorectal polyps.

Eligibility criteria:
Inclusion Criteria:

* Screening or surveillance colonoscopy

Exclusion Criteria:

* known or suspected inflammatory bowel disease
* uncontrolled coagulopathy
* known polyps or referral for polypectomy

Outcomes:
Feasibility to use the AI System in vivo during colonoscopy
Evelopment and Validation of a Prediction Model for the Transition From Mild to Moderate Form of COVID-19, Using Data From Chest CT
Trial ID: NCT04481620
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2020-08-31
Completion date: 2021-05-04
Sponsor: University Hospital, Bordeaux
Collaborators: Programme Hospitalier de Recherche Clinique Inter-Régionale (PHRC-I)
Conditions: COVID-19
Locations: CHU Bordeaux; Clinique Bordeaux Nord; Clinique Saint Augustin; CHU de Grenoble Alpes; Hôpital Arnaud-de-Villeneuve CHU de Montpellier; Hôpitaux de Brabois CHU de Nancy; Hôpital de la Milétrie CHU de Poitiers
Countries: France; France; France; France; France; France; France
Enrollment: 1329
Age range: 18 Years - None
Sex: ALL

Summary:
Only 5% of patients infected with COVID-19 develop severe or critical Coronavirus disease 2019 (COVID-19) and there is no reliable risk stratification tool for non-severe COVID-19 patients at admission.

Finding a way to predict which patients with an initial mild to moderate presentation of COVID-19 would develop severe or critical form of COVID-19 according to CT-scan data, simple clinical and biological parameters is challenging. In this multicentric study, the study aims to construct a predictive score for early identification of cases at high risk of progression to moderate, severe or critical COVID-19 combining simple clinical and biological parameters and qualitative, quantitative or artificial intelligence (AI) data from the initial CT from non-severe patients.

Eligibility criteria:
Inclusion Criteria:

* First chest CT, assessed for respiratory symptoms, without injection of contrast agent for respiratory symptoms, and whose results of the CT subjective visual analysis are compatible or typical of COVID-19
* biological diagnosis of COVID-19 (RT-PCR) or clinical suspicion (cough and / or dyspnea and / or fever and / or need to use oxygen therapy as part of routine care) at the time of the examination
* Authorization of the patient for the processing of his personal data, except CNIL exemption

Exclusion Criteria:

* Patient with a moderate (oxygen between 3 and 5 L / min to achieve saturation greater than 97% and a respiratory rate \<25 / min without the need for invasive ventilation), severe form (oxygen therapy\> 5L / min to obtain a SpO2\> 97%) or critical form (need to resort to ventilation and / or orotracheal intubation) at the date of the first chest CT
* Age \< 18 years old
* Patient deprived of liberty by judicial decision

Outcomes:
occurrence of significant clinical degradation
Testing the Efficacy of an Artificial Intelligence Real-Time Coaching System in Comparison to In Person Expert Instruction in Surgical Simulation Training of Medical Students - A Randomized Controlled Trial
Trial ID: NCT05168150
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-01-05
Completion date: 2022-05-03
Sponsor: McGill University
Conditions: Surgical Education
Interventions: Experimental: Experimental Group - Intelligent Continuous Expertise Monitoring System group; Experimental Group In-person expert-mediated instruction group
Locations: Montreal Neurological Institute and Hospital
Countries: Canada
Enrollment: 98
Age range: 18 Years - None
Sex: ALL

Summary:
Background:

Trainees learn surgical technical skills through apprenticeship model working closely with surgeons and given increased responsibility in patient cases under expert supervision. However, factors such as surgeons' busy schedule, number of available patient cases, patient safety and lack of objectivity and standardization in training pose strong limitations. Virtual reality surgical simulators integrated with artificial intelligence (AI) systems provide a standardized realistic simulation environment and detailed performance data that allows accurate quantitation of surgical skills and tailored feedback. These platforms make repetitive practice of surgical skills possible in a risk-free environment.

The Intelligent Continuous Monitoring System (ICEMS), a deep learning application integrated in NeuroVR simulation platform, was developed to assess surgical performance continuously in 0.2 second intervals and provide coaching and risk detection. Although a predictive validity for assessment module was provided previously, the effectiveness of real-time coaching and risk detection ability with this AI system remains to be explored.

The objective of this study is to compare the error-oriented intelligent feedback provided by the ICEMS to in-person expert instruction in surgical simulation training by monitoring the improvement of medical student technical skills on a series of virtual reality tumor resection tasks.

Eligibility criteria:
Inclusion Criteria:

* Medical students who are actively enrolled in medical school in any Canadian institution who do not meet the exclusion criteria.
* Premedical students who are actively enrolled in medical school in any Canadian institution who do not meet the exclusion criteria.

Exclusion Criteria:

* Participation in previous trials involving the NeuroVR (CAE Healthcare) simulator.

Outcomes:
Change in performance; Transfer of learning
Radiomics-Based Visualization and Quantitative Validation of Isocitrate Dehydrogenase 1 Heterogeneity in Gliomas
Trial ID: NCT05969691
Status: ACTIVE_NOT_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2019-03-15
Completion date: 2026-12-15
Sponsor: Mingge LLC
Collaborators: Huashan Hospital; Fudan University
Conditions: Glioma, Malignant; Computer-Assisted
Interventions: Validation of IDH1 mutations from the radiomics model
Locations: Huashan Hospital, Fudan University
Countries: China
Enrollment: 18
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
The goal of this clinical trail is to non-invasively visualise and quantitatively validate an radiomics model of genetic heterogeneity in adult patients with diffuse glioma to help clinicians better guide surgical resection and treatment options. It aims to answer are:

1. To overcome the limitations of the existing genetic diagnostic process in terms of equipment and technology requirements, high costs and long timelines, and to enable quantitative studies of isocitrate dehydrogenase 1 (IDH1) mutations, thus allowing refined patient stratification and further exploration of the role of molecular markers in improving patient prognosis.
2. To achieve non-invasive diagnosis of gene mutations within tumours by taking advantage of artificial intelligence and medical images, and to test the clinical feasibility of the model through typical target puncture, gene sequencing and quantitative gene expression analysis.

Participants will read an informed consent agreement before surgery and voluntarily decide whether or not to join the experimental group. They will undergo preoperative magnetic resonance imaging, intraoperative brain puncture of typical tumour sites, and postoperative genotype identification. Their imaging data, genotype data, clinical history data, and pathology data will be used for the experimental study.

Eligibility criteria:
Inclusion Criteria:

* Patients aged 18-70 years, male or female;
* Patients with complete preoperative magnetic resonance imaging and were initially diagnosed with glioma based on the magnetic resonance imaging results;
* Patients who were proposed for craniectomy without contraindications to surgery and who had signed an informed consent form by themselves or their proxy;
* Patients with confirmed diagnosis of glioma by postoperative pathological diagnosis.

Exclusion Criteria:

* Patients to be admitted for a simple puncture biopsy;
* Patients with a combination of serious medical conditions and those who cannot tolerate surgery;
* Patients with a bleeding tendency or abnormal coagulation function, or those who have not discontinued long-term use of anticoagulants such as aspirin and clopidogrel;
* Pregnant and breastfeeding women;
* Patients participate in other clinical trials during the same period.

Outcomes:
accuracy