moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
MAP THE SMA: a Machine-learning Based Algorithm to Predict THErapeutic Response in Spinal Muscular Atrophy
Trial ID: NCT05769465
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2023-04-01
Completion date: 2026-04-01
Sponsor: Fondazione Policlinico Universitario Agostino Gemelli IRCCS
Conditions: Spinal Muscular Atrophy
Interventions: disease modifying treatments
Locations: Fondazione Policlinico Universitario Agostino Gemelli IRCCS
Countries: Italy
Enrollment: 247
Age range: None - None
Sex: ALL

Summary:
Spinal Muscular Atrophy (SMA) is caused by the homozygous loss of the Survival Motor Neuron (SMN) 1 gene, which leads to degeneration of spinal alpha-motor neurons and muscle atrophy. Three treatments have been approved for SMA but the available data show interpatient variability in therapy response and, to date, individual factors such as age or SMN2 copies,cannot fully explain this variance.

The aim of this project is:

* collect clinical data and patient-reported outcome measures (PROM) from patients treated with nusinersen, risdiplam, onasemnogene abeparvovec,
* identify novel biomarkers and RNA molecular signature profiling,
* develop a predictive algorithm using artificial intelligence (AI) methodologies based on machine learning (ML), able to integrate clinical outcomes, patients' characteristics, and specific biomarkers.

This effort will help to better stratify the SMA patients and to predict their therapeutic outcome, thus to address patients towards personalized therapies.

Eligibility criteria:
Inclusion Criteria:

* confirmed genetic diagnosis of SMA (5q)
* clinical phenotype of type I or II or III;
* able to provide (patient/caregiver) written informed consent

Exclusion Criteria:

* None

Outcomes:
Collect clinical data and patient-reported outcome measures (PROM) from patients treated with nusinersen, risdiplam, onasemnogene abeparvovec; Identify novel biomarkers and RNA molecular signature profiling; Develop a predictive algorithm using artificial intelligence (AI) methodologies based on machine learning (ML), able to integrate clinical outcomes, patients' characteristics, and specific biomarkers
Trunk and Upper Extremities Program Effect in Decreasing Compensation Patterns in Unilateral Cerebral Palsy Children: Randomized Control Trial
Trial ID: NCT06069765
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-09
Completion date: 2027-07
Sponsor: Fundacio Aspace Catalunya
Collaborators: Universitat Internacional de Catalunya
Conditions: Cerebral Palsy Infantile; Chronic Pain; Upper Extremity Dysfunction
Interventions: TUPEX
Enrollment: 40
Age range: 8 Years - 16 Years
Sex: ALL

Summary:
The goal of this clinical trial is to test the effectiveness of a trunk and upper extremities exercise program. The main questions it aims to answer are:

Whether the program decreases compensation patterns in unilateral cerebral palsy children Whether the program decreases pain in unilateral cerebral palsy children Whether the program improves bimanual ability in unilateral cerebral palsy children Participants will undertake and 8-week program exercise at home If there is a comparison group: Researchers will compare with the control group to see if this program decreases compensation patterns.

Eligibility criteria:
Inclusion Criteria:

* Unilateral cerebral palsy diagnostic
* Level I or II of the Manual Ability Scale Classification

Exclusion Criteria:

* Have received any intervention with botulinum toxin or shock waves in the previous six months or have them scheduled during the intervention
* Are undergoing treatment with intensive therapies.
* To have known alteration in the hips
* Have very serious attention or comprehension difficulties that prevent them from following instructions.
* Suffer uncontrolled epilepsies
* Suffer from severe visual problems.

Outcomes:
Rate of variance of the Compensation patterns
Structured Handoff Using Intelligent Framework for Transitions (SHIFT) Trial: A Randomized Controlled Trial of AI-Assisted End-of-Rotation Handoff Between Hospitalists
Trial ID: NCT07251907
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-12-15
Completion date: 2026-07
Sponsor: University of Pennsylvania
Conditions: Electronic Medical Record; Transitions of Care; Physician Workflow; Artificial Intelligence (AI); Large Language Model
Interventions: LLM tool to draft handoff
Locations: Hospital of the University of Pennsylvania and Penn Presbyterian Medical Center
Countries: United States
Enrollment: 90
Age range: 18 Years - None
Sex: ALL

Summary:
Inpatient general medicine attendings will be randomized to have an LLM feature turned on to provide a draft of an off-service handoff within Carelign (an EHR-adjacent provider communication tool). Providers who have access to this feature will be clearly instructed that if they use the LLM-generated draft, they must review and edit it as necessary before finalizing. The study will assess measures of documentation burden (as it relates to writing handoff) - including time spent writing handoff - and work exhaustion in both intervention and control groups.

Eligibility criteria:
Inclusion Criteria:

* General medicine attending physicians at HUP (Medicine, Solid Oncology) or PPMC (Medicine) services.
* Scheduled for ≥5 consecutive days on service.

Exclusion Criteria:

\- Jeopardy attendings and moonlighters

Outcomes:
Time spent drafting end-of-rotation handoff
Improving Personalised Glioblastoma Care by Stem Cell Analysis, Omics (Including Immunomics) and Artificial Intelligence Approaches
Trial ID: NCT05941234
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-02-29
Completion date: 2026-12-30
Sponsor: Fondazione Policlinico Universitario Agostino Gemelli IRCCS
Collaborators: Oslo University Hospital; Istituto Superiore di Sanità; Luxembourg Institute of Health; Hospital Donostia; University Medical Center Goettingen; National Research Council
Conditions: Glioblastoma, IDH-wildtype
Interventions: Biological biomarker analysis
Locations: Fondazione Policlinico Universitario A. Gemelli IRCCS
Countries: Italy
Enrollment: 120
Age range: 18 Years - None
Sex: ALL

Summary:
The study aims at:

1. Perform a multilayer analysis relying on tight integration of in-depth multi-omics approaches with clinical data to discover immune markers, with attention to age and sex differences, predicting prognosis and defining key life/environmental elements, to guide AI-driven personalised treatments and ensure improved care and QoL of glioblastoma patients.
2. To deepen glioblastoma knowledge through the study of glioblastoma stem cell cultures and to assess the sensitivity of glioblastoma stem cell cultures to a number of chemotherapeutics in different experimental conditions.
3. To create a comprehensive, stakeholder-generated guidelines for the ethical use of patient data for artificial intelligence-assisted prediction systems in glioblastoma, including an online, easily accessible patient information brochure to increase patient empowerment in the field.

Eligibility criteria:
Inclusion Criteria:

To be enrolled in the study patients must:

1. Have a radiological diagnosis of supratentorial glioblastoma, or
2. Have a radiological diagnosis of first recurrence of a primary supratentorial glioblastoma (for which a formal histopathologic diagnosis of glioblastoma had made at first surgery), according with RANO criteria;
3. Be a candidate to neurosurgery for glioblastoma at the Operational Unit of Neurosurgery Fondazione Policlinico Gemelli IRCCS;
4. Be of an age of 18 years or above;
5. Provide written informed consent for participation to the study.

Exclusion criteria

To be enrolled in the study patients must not:

1. Have not enough pathological material removed at surgery available both for mandatory routine histopathological diagnosis and for the present study, as judged by the Principal Investigator;
2. Have not a definitive pathological diagnosis of a primary supratentorial glioblastoma, according with 2021 World Health Organization classification.

Outcomes:
Identification of immune signatures in glioblastoma predictive of overall survival; identification of lifestyle/environmental signatures predictive of overall survival; Cancer stem cell-based chemosensitivity assay
Artificial Intelligence in Healthcare: Advanced Evaluation of Medical Online Content. Assessment of Health Knowledge and Related Educational Intervention.
Trial ID: NCT05960734
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-03-22
Completion date: 2023-11-01
Sponsor: University of Oviedo
Conditions: Cancer; Incontinence; Patient Education; Social Networks; Surgery
Locations: University of Oviedo
Countries: Spain
Enrollment: 160
Age range: None - None
Sex: ALL

Summary:
The aim of the study is to analyse the quality of information that patients and relatives of different types of cancer receive through social networks on the subject of incontinence after cancer surgery.

Eligibility criteria:
Inclusion Criteria:

* Anyone providing information about incontinence after cancer surgery.
* English language

Exclusion Criteria:

* Non-english language
* Duplicated
* Related with advertisements
* Nonrelated to incontinence

Outcomes:
Analisys of the information in social networks about incontinence after cancer surgery
Impact of Artificial Intelligence on Trainee Adenoma Detection Rate
Trial ID: NCT05423964
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-01-01
Completion date: 2025-09
Sponsor: University of Southern California
Conditions: Adenoma; Adenoma Colon; Colorectal Cancer
Interventions: AI use in Endoscopy Room; Non-AI use Standard of Care endoscopy room
Locations: LAC+USC Medical Center
Countries: United States
Enrollment: 25
Age range: None - None
Sex: ALL

Summary:
Adenoma detection rate (ADR) is a validated quality metric for colonoscopy with higher ADR correlated with improved colorectal cancer outcomes. Artificial intelligence (AI) can automatically detect polyps on the video monitor which may allow endoscopists in training to improve their ADR. Objective and Purpose of the study: Measure the effect of AI in a prospective, randomized manner to determine its impact on ADR of Gastroenterology trainees.

Eligibility criteria:
Inclusion Criteria:

* All Gastroenterology fellows at USC performing Endoscopies will be included in the study.

Exclusion Criteria:

* If fellows refuse informed consent they will be excluded.
* Procedures performed in the intensive care unit or the operating room will not be counted toward the study metrics as the AI system will only be available in the endoscopy unit.
* If procedures are performed only by faculty, in which the fellow is not the primary operator, they will not be used for study metrics.

Outcomes:
Average adenoma detection rate
Digital Pattern Recognition for Identification of Various Hypospadias Parameters Based on Digital Photography Using Artificial Neural Network
Trial ID: NCT05569863
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-11-14
Completion date: 2023-12-31
Sponsor: Indonesia University
Collaborators: University Ghent
Conditions: Hypospadias
Enrollment: 1000
Age range: None - 18 Years
Sex: MALE

Summary:
The goal of this observational study is to develop and validate a digital pattern recognition system based on artificial neural network to determine various parameters in hypospadias. The main question it aims to answer is:

How accurate is the digital pattern recognition system based on artificial neural network to determine various parameters in hypospadias?

Participants in this study are hypospadias patients aged \< 18 years old. The guardian (and the patient, if applicable) will be informed about the study and asked for consent. The digital picture of participants' penis will be taken from different angles according to the predetermined angle.

The clinical characteristics of the photographed penis are then inputted and used to train a customized artificial neural network (ANN). The machine is then used to predict various hypospadias parameters presenting at the patients' penis. The accuracy of the machine is then compared to the measurement done by pediatric urologists.

Eligibility criteria:
Inclusion Criteria:

* Children aged \<18 years old
* Suspected of having hypospadias (hypospadias group)
* Diagnosed as not having hypospadias (control group)

Exclusion Criteria:

* History of hypospadias repair
* Refusal to participate in the study

Outcomes:
Accuracy of The Digital Pattern Recognition Model
Optimization of Intraoperative Ultrasound Use in Brain Tumor Surgery Through Artificial Intelligence-Based Techniques
Trial ID: NCT07376304
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: PHASE3
Start date: 2026-03
Completion date: 2028-06
Sponsor: Hospital del Rio Hortega
Collaborators: University Hospital Bratislava
Conditions: Brain Tumor Adult
Interventions: BrainUS-AI real-time intraoperative ultrasound segmentation system
Enrollment: 100
Age range: 18 Years - None
Sex: ALL

Summary:
Intraoperative ultrasound is a versatile, low-cost imaging tool that has been shown to improve safety and efficacy in brain tumor surgery. However, its widespread adoption remains limited due to operator dependency, the complexity of image interpretation, the presence of artifacts, and a restricted field of view.

This project aims to prospectively evaluate, in a multicenter and non-randomized setting, a prototype real-time deep learning-based segmentation model for brain tumor delineation in intraoperative ultrasound. The model is designed to facilitate the identification of tumor tissue during surgery, potentially enhancing intraoperative decision-making and surgical precision.

By increasing the precision and accessibility of ioUS, this innovation is expected to enable safer and more complete resections, with the potential to improve both survival and quality of life for patients with brain tumors.

Eligibility criteria:
Inclusion criteria:

* Age ≥ 18 years.
* Scheduled for craniotomy and resection of a brain tumor with ioUS planned as part of the standard surgical workflow.
* Preoperative MRI available for surgical planning.
* Ability to obtain informed consent from the patient or legal representative.

Exclusion criteria:

• Inadequate ioUS image acquisition due to technical failure or intraoperative complications unrelated to the tumor.

Outcomes:
Diagnostic performance of BrainUS-AI for residual tumor detection at end of resection
AI-Simplified Patient Educational Materials: Investigating the Potential for Improved Patient Comprehension and Health Literacy
Trial ID: NCT07505719
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-02-13
Completion date: 2027-05
Sponsor: Hospital for Special Surgery, New York
Conditions: Artificial Intelligence (AI); Health Literacy; Patient Comprehension; Patient Educational Material
Interventions: AI Simplification; Unmodified Patient Educational Material
Locations: Hospital for Special Surgery
Countries: United States
Enrollment: 80
Age range: 18 Years - None
Sex: ALL

Summary:
Poor health literacy and patient comprehension have been associated with adverse health outcomes. Patient educational materials (PEMs) are articles that are intended to assist patients in their understanding of a given medical condition. Given that the average American adult reads at the 8th grade level, the American Medical Association and the Center for Disease Control recommend PEM be written at the 6th grade level. However, literature has found the majority of PEMs to be written significantly higher than the 8th grade level. In order to improve their readability, a number of studies have displayed the effectiveness of large language models (LLMs) such as ChatGPT to simplify the text of a given PEM. Despite the improvement in readability, the effectiveness of these simplified PEMs on improving patient comprehension of the AI augmented material has yet to be investigated.

The purpose of our study is to test whether the improvement in readability found in AI-simplified PEMs corresponds to a greater understanding of the material compared to the original PEM. Understanding if AI-simplified PEM truly improves comprehension could further support this use case for AI and aid providers and healthcare organizations in improving the health literacy of their patients.

This study aims to answer the following question:

Do AI simplified PEMs improve the comprehension of pediatric orthopaedic conditions?

Researchers will compare AI-simplified PEMs to their original, unmodified counterparts in order to see if there is any difference in post reading comprehension of the participants.

Participation in the study will include:

* A brief baseline survey (e.g. demographics and educational attainment)
* A randomly assigned reading of either the original PEM or the AI simplified version.
* A 10 question post-reading multiple choice quiz

Eligibility criteria:
Inclusion Criteria:

* Parents or guardians of pediatric patients receiving treatment at Hospital for Special Surgery

Exclusion Criteria:

* Non-English Speaking
* primary occupation is in healthcare
* participants with prior knowledge on the condition pertaining to the material (osteogenesis imperfecta)

Outcomes:
Post Reading Comprehension Scores
Patients' Perceptions and Comprehension of Original Versus AI-Generated Plain Language Summaries of Cochrane Oncology Systematic Reviews
Trial ID: NCT07140107
Status: ENROLLING_BY_INVITATION
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-08-14
Completion date: 2026-02-01
Sponsor: University of Split, School of Medicine
Collaborators: University Hospital of Split
Conditions: Non-Metastatic Breast Cancer
Interventions: AI-generated PLS; Original PLS
Locations: Department of Oncology and Radiotherapy, University Hospital Split
Countries: Croatia
Enrollment: 130
Age range: 18 Years - None
Sex: FEMALE

Summary:
The study explores how patients with non-metastatic breast cancer perceive and comprehend original Plain Language Summary and AI-generated Plain Language Summary (PLS) of two systematic reviews from the Cochrane Library. The systematic reviews address exercise during adjuvant therapy and psychological interventions for non-metastatic breast cancer. The study, which is randomized and double-blind, aims to assess whether AI-generated PLS and original PLS differ in their influence on patients' intention to undertake and apply the recommendation presented in the summary, as well as their overall understanding of the study results.

Eligibility criteria:
Inclusion Criteria:

* female patients with histologically confirmed non-metastatic breast cancer currently undergoing chemotherapy or radiotherapy:

  1. neoadjuvant chemotherapy (with or without immunotherapy or antiHER2 therapy),
  2. adjuvant chemotherapy (with or without immunotherapy or antiHER2 therapy),
  3. adjuvant radiotherapy (including local and locoregional radiation treatment with possible radiotherapy boost where indicated).
* patients need to be able to read and understand Croatian.

Exclusion Criteria:

* female patients with metastatic breast cancer.
* female patients receiving only hormonal treatment.

Outcomes:
Patients' level of intention for changing their behavior
Spotlight AQ Randomized Controlled Trial of the Spotlight AQ Survey Platform to Evaluate Its' Efficacy and Applicability in a Clinic Setting.
Trial ID: NCT06390371
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-05-27
Completion date: 2026-12
Sponsor: Children's Mercy Hospital Kansas City
Conditions: Type 1 Diabetes Mellitus
Interventions: Spotlight AQ Survey platform
Locations: Children's Mercy
Countries: United States
Enrollment: 60
Age range: 16 Years - 25 Years
Sex: ALL

Summary:
The goal of this clinical trial is to compare the Spotlight AQ survey platform to the standard of care (SOC) pre-clinic assessment in adolescents \& young adults (16-25 years old) with Type 1 Diabetes (T1D).

The main questions it aims to answer are:

* Does using the Spotlight AQ survey in clinic decrease the A1c in the people who use it?
* Do patients and healthcare providers like using the spotlight AQ survey?

Participants will use the Spotlight AQ survey before coming in to two standard of care T1D clinic visits Participants will fill out surveys describing how they feel about using the Spotlight AQ survey. Some participants may be asked to do an interview to talk about how they feel about the Spotlight AQ survey.

Eligibility criteria:
Inclusion Criteria:

* 16-25 years old
* Patients diagnosed with T1D for at least 6 months
* On any diabetes treatment
* Using a Continuous Glucose Monitor (CGM)
* Willing/ able to use Spotlight AQ tool
* Able to give verbal assent
* Patients \& parents/caregiver speak English/ Spanish fluently
* Patients in the Children's Mercy Kansas City Hospital or Boston Children's Hospital network

Exclusion Criteria:

* \<16 years old or \>25 years old
* Mental illness that could seriously reduce their ability to participate in the study
* Lack of capacity
* In another T1D intervention study

Outcomes:
HbA1c
Artificial Intelligence for Rapid On-site Evaluation (AI-ROSE) for Endoscopic Ultrasound-guided Fine-needle Aspiration (EUS-FNA) Biopsy of Pancreatic Solid Lesions: A Prospective Double Blinded Study
Trial ID: NCT05018663
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2021-07-21
Completion date: 2028-01-30
Sponsor: The University of Texas Health Science Center, Houston
Conditions: Pancreatic Solid Lesions
Interventions: Artificial Intelligence software ROSE
Locations: Memorial Hermann Hospital
Countries: United States
Enrollment: 50
Age range: 18 Years - 100 Years
Sex: ALL

Summary:
Purpose The primary objective of the study is to compare interpretation of EUS FNA/FNB samples for adequacy between ROSE and AI at bedside. To compare accuracy of preliminary diagnosis results between ROSE and AI at bedside versus final pathology report.

Research design This is a prospective single center study to compare performance characteristics in the interpretation of EUS FNA/FNB samples between AI and ROSE.

Procedures to be used Eligible patients will undergo EUS guided FNA/FNA of PSLs using standard of care. Sample slides are prepared by a cytopathologist at bedside and observed under a microscope. At the same time, the slides are scanned using a slide scanner and those images are saved for interpretation by AI at a later time.

Eligibility criteria:
Inclusion Criteria:

* Have EUS finding of a PSL;
* Do not have contraindications for FNA/FNB.

Exclusion Criteria:

* Inability to provide informed consent for the procedure;
* Contraindication for FNA/FNB eg coagulopathy, lack of avascular window for FNA.

Outcomes:
Detection the adequacy for diagnosis