Ai Artificial Intelligence

Rapid On-Site Evaluation (ROSE) of cytological slides obtained during EBUS-TBNA improves diagnostic yield by providing real-time adequacy assessment and preliminary diagnostic orientation after each needle pass. In centers without a dedicated cytopathologist, ROSE is performed by a second interventional pulmonologist acting as a dedicated ROSE operator (interventional pulmonologist-performed ROSE, IP-ROSE), a model associated with good but variable diagnostic performance compared to cytopathologist-performed ROSE. This study evaluates the feasibility and preliminary diagnostic performance of an investigational artificial intelligence prototype for digital ROSE. The prototype, developed in-house by the Principal Investigator, analyzes microscopic images of Diff-Quik stained cytological slides acquired through a dedicated digital microscope, together with basic clinical data, via API calls to a multimodal AI model. It produces two outputs: sample adequacy (appropriate/not appropriate) and malignancy suspicion (benign/malignant), each with a confidence score. The AI output is recorded in the study database for research purposes only and is not shown to the operator in real time; it does not influence clinical decisions during the procedure. The study is a prospective, monocentric, observational pilot study enrolling 65 adult patients undergoing EBUS-TBNA or peripheral TBNA with IP-ROSE at a single interventional pulmonology unit. The primary statistical unit is the individual ROSE slide, with an expected 130 to 160 evaluable slides. Co-primary endpoints are: (1) technical feasibility of the AI prototype, defined as the proportion of slides with valid AI output within 90 seconds; and (2) AI accuracy for sample adequacy assessment compared to the definitive cytopathological diagnosis, with an expected 95% confidence interval precision of ±5.5%. Secondary endpoints include AI accuracy for malignancy suspicion, agreement between the AI prototype and the IP-ROSE operator, and AI output latency. The AI prototype is not a commercially approved or CE-marked medical device. It was developed internally by the Principal Investigator for research purposes and is evaluated exclusively within this study. Data from this pilot study will inform the design of a subsequent confirmatory non-inferiority trial, which will be the subject of separate registration and ethical approval.

This study aims to evaluate the feasibility, safety, and acceptability of a newly developed artificial intelligence (AI)-assisted physiotherapy system for oromandibular and neck-shoulder range of motion (ROM) in patients who have undergone treatment for oral cancer. In this single-group, prospective, non-randomized pilot study, recruited participants will receive 4 to 6 weeks of AI-assisted physiotherapy. Participants will undergo a comprehensive clinical evaluation at baseline and post-intervention. During the intervention period, the AI system will perform a daily automated assessment to dynamically generate and adjust personalized exercise programs. Participants will perform these prescribed programs 4 to 6 times daily. Pre- and post-intervention changes, along with key feasibility parameters, acceptability, and safety metrics, will be statistically analyzed to inform future definitive trials.

This research plan aims to establish an effectiveness assessment system for promoting clinical communication, empathy, and emotion regulation in pediatric nursing students through a combination of AI communication simulation and human library narrative. This study sets five specific objectives: 1. To develop and implement a pediatric nursing teaching model that integrates AI communication simulation and human library narrative. 2. To evaluate the effectiveness of this teaching model in improving nursing students' clinical communication skills. 3. To examine the effectiveness of this teaching intervention in enhancing nursing students' empathy. 4. To explore the impact of this teaching intervention on nursing students' emotion regulation abilities. 5. To understand students' learning experiences, feelings, changes in emotion regulation, and suggestions for improvement regarding this integrated teaching intervention.

This study aims to develop a low-cost, marker-free intelligent wound assessment system that can analyze wound photos taken with a standard smartphone. By comparing wound images over time, the system will generate a quantifiable Wound Progression Index (WPI) to provide objective feedback on whether a wound is improving, stable, or worsening. The long-term goal is to support early detection of wound deterioration and improve wound care in both clinical and home settings.

The aim of this study is to develop and evaluate an artificial intelligence-based model capable of analyzing periapical radiographs of maxillary and mandibular molars to predict the difficulty level of non-surgical root canal retreatment. By integrating deep learning techniques with routinely acquired periapical radiographs, this study aims to enhance diagnostic support, improve clinical decision-making, and facilitate appropriate case selection or referral in endodontic practice.

The goal of this observational study is to learn if AI-assisted cardiac CT imaging can improve cardiovascular risk stratification and prediction of future coronary events in an adult population undergoing clinically indicated cardiac CT. The main questions it aims to answer are: * Can AI-enhanced cardiac CT accurately assess cardiovascular risk in a real-world adult population? * How do CT-derived plaque characteristics correlate with clinical, biochemical, and lifestyle risk factors? Researchers will compare subgroups (e.g., patients with different risk profiles, biomarkers, or imaging findings, and a subset undergoing OCT imaging) to see if differences in imaging and clinical parameters are associated with cardiovascular risk and plaque vulnerability. Participants will: * Provide informed consent and medical history/demographic information * Undergo blood sampling for cardiovascular and metabolic biomarkers * Have a resting ECG performed * Complete a detailed lifestyle and health questionnaire * Receive a non-invasive cardiac CT scan interpreted by an expert * Potentially receive heart rate-lowering medication (e.g., metoprolol) if required for imaging quality * Be referred for further clinical evaluation if clinically indicated 

This study aims to thoroughly assess the predictive accuracy of artificial intelligence-based nasal outcome simulations by comparing AI-generated preoperative predictions with objective postoperative nasal morphology using digital image analysis. To assess accuracy of AI-image measurement compared with imageJ software

The NEST study is a prospective, observational research study designed to collect clinical measurements and image data to develop and evaluate artificial intelligence (AI)-derived algorithms for estimating anthropometric parameters in neonates and young infants. The study focuses on infants from birth up to 6 months of age and aims to assess the accuracy of AI-based estimations of length, weight, and head circumference using photographs and/or video recordings captured during routine clinical care. These AI-derived measurements will be compared against standard clinical measurements obtained by trained healthcare professionals in neonatal and infant care settings.

The goal of this observational, retrospective and prospective study is to develop a noninvasive disease assessment system by leveraging artificial intelligence (AI) to comprehensively analyze multi-modal imaging features, including magnetic resonance enterography (MRE) and computed tomography enterography (CTE), for the diagnosis and prognostication of digestive diseases. To this end, the investigators retrospectively enrolled imaging, endoscopic, and clinical data from 21 centers across China to construct and iteratively optimize the AI model. The model's performance will be prospectively validated in two centers, and its accuracy in lesion localization will be verified through real-world deployment in endoscopy suites.

This study aims to evaluate the validity and reliability of a novel AI-based physiotherapy evaluation system for measuring oromandibular and neck-shoulder range of motion (ROM). Traditional ROM assessments rely on manual measurements, which may be influenced by rater experience and variability. The proposed AI system uses automated keypoint tracking to provide objective and standardized measurements. In this cross-sectional study, healthy adult participants will perform standardized ROM tasks. Measurements obtained from the AI system will be compared with those from two independent raters using conventional clinical tools. Repeated measurements will be conducted to assess intra-rater and inter-rater reliability. The agreement between the AI system and human raters will be evaluated to determine the system's clinical applicability.

Beacon is a digital platform that processes objective and subjective aggregated data provided by patients. Objective data is provided by standard wearables, while subjective data is provided by patient-reported outcome measures (PROMs), comprising written and vocal patient reporting. The ASPIRE.AI study is a prospective study evaluating the feasibility of clinicians' use of aggregated data that was provided by patients and analyzed through "Beacon", and its influence on advanced cancer patients' palliative symptoms management. Approximately 40 consecutive eligible ambulatory advanced cancer patients first attending the palliative unit in the Davidoff Center will be enrolled. The trial will continue for \~1 year, with each patient participating in this trial for a total of about 12 weeks. All participants will receive the intervention. The intervention comprises the palliative standard of care treatment along with the usage of the Beacon digital platform, which enables comprehensive data collection and aggregation regarding the patient's biopsychosocial status, and thus, the patient's symptom burden. Data collected and aggregated through Beacon includes Beacon data provided by the patients via wearables (smartwatch/sensors), smartphones, and written and recorded PROMs. Researchers will then evaluate physician engagement with the platform, Influence on treatment, and the physician user experience rating as well as patients' adherence, satisfaction with Beacon usage, and changes in patients' symptom burden and quality of life.

This is a cross-sectional study that primarily employs quantitative analysis, supplemented by qualitative assessment. The research is conducted in two stages: Phase I consists of a model performance comparison experiment, and Phase II involves an item quality evaluation experiment. The entire study adheres to the principles of single-blinding, randomization, and standardization to ensure scientific rigor and reproducibility. The single-blind design is implemented during the "standardized testing" phase, where the system intersperses AI-generated items with those authored by human experts. Participants remain blinded to the source of each item (AI-generated vs. human-authored) throughout the testing and scoring processes, thereby ensuring the objectivity of the evaluation results.

What is the study about? This study is testing "Dora", an AI-powered assistant that can make phone calls to patients, for use in the Fracture Liaison Service (FLS). The FLS is a clinic that helps prevent more bone fractures after an initial "fragility fracture" (a break that happens easily, usually due to osteoporosis). Why is this being done? FLS clinicians often have to spend a lot of time on routine phone calls for assessments and follow-ups. If Dora can safely and accurately collect patient information, it might save time for staff and still give patients a good experience. What will happen to patients in the study? Invitation and consent - Patients with a new fragility fracture who are eligible will be invited to take part after informed consent. Dora call - Patients will receive an automated phone call from Dora, at the start of their FLS pathway and at follow-up. At intake, Dora will ask about risk factors for bone problems (e.g., smoking, alcohol use, family fracture history). At follow-up, Dora will ask about medication use, side effects, falls, or new fractures. Clinician call - Soon after, patients will have their usual phone appointment with an FLS clinician, who asks similar questions. Surveys/interviews - Patients will be asked to complete a short questionnaire and take part in an optional interview to say how they felt about talking to Dora. What about clinicians? Clinicians involved in the FLS pathway will be asked to complete a short survey and to take part in an optional interview to understand how useful Dora's reports might be in their work. Who can take part? Patients - Age 50+, English-speaking, with a new fragility fracture, and able to use the phone. Clinicians - Those working in FLS or similar bone health services. How long will it take? Each patient might be involved for up to about 7 months. The whole study will take about a year.

In our previous study, based on the multi-center clinical big data collected from January 2012 to January 2025, we have completed the construction of a multimodal early warning model for the malignant transformation of uterine fibroids. The model was mainly based on T2WI and DWI sequences, and was trained and optimized by support vector machine (SVM) algorithm. In the retrospective study and internal validation, the model shows high sensitivity and specificity, which preliminarily proves that it has good application potential in identifying high-risk groups and predicting the risk of malignant transformation of uterine fibroids. However, there are still some limitations in retrospective studies and internal validation results, and its application value, universality and stability in real clinical environment have not been fully verified. Therefore, we plan to conduct a prospective validation study in consecutive patients enrolled after January 2025 to evaluate the clinical performance and generalization of the model in predicting the malignant tendency or risk of malignant transformation of uterine fibroids through practical application in the real population, and further analyze the operability in the actual diagnosis and treatment process and the potential value for patient management. This study will provide reliable evidence for early screening, follow-up management and individualized treatment of high-risk population, and has important clinical and public health significance for improving the early diagnosis rate, reducing the risk of malignant transformation and improving the prognosis of patients with uterine fibroids.

The complexity of healthcare services and technological advances today have necessitated the adoption of innovative approaches in nursing education. Among these innovative approaches, artificial intelligence (AI) has established itself as a technology that is increasingly present in nursing education processes, offering a supportive, personalized, and interactive learning experience. AI's contributions to nursing students' acquisition of fundamental competencies such as clinical decision-making, skill development, and critical thinking are rapidly increasing. Especially in high-risk, invasive, and clinically skill-intensive applications, AI-supported educational models both enhance learning quality and support patient safety. Intramuscular and subcutaneous injections are among the basic invasive skills that nursing students must learn. These applications require a high level of cognitive and psychomotor competence from students. Incorrect injection practices can lead to complications such as drug absorption problems, nerve damage, hematoma, or infection, making it critically important to teach these skills correctly and safely. In this context, AI-supported education systems stand out as an effective tool for teaching injection skills. Artificial intelligence-based chatbots provide students with both theoretical knowledge and practical guidance. For example, before injecting a muscle group, a student can learn about the anatomy of the muscle, determine the correct angle, and remember precautions against potential complications through the chatbot. Artificial intelligence also reinforces the learning process by instantly answering students' questions, preventing the acquisition of incorrect information. Recent studies emphasize that AI-supported learning tools positively influence students' attitudes toward learning, increasing their motivation and academic satisfaction levels. Accordingly, the integration of AI-based technologies in the process of training future nurses is no longer an option but a necessity. Particularly in complex and delicate skills such as intramuscular and subcutaneous injections, AI-supported chatbots can facilitate student learning, increase skill accuracy, and support clinical safety. Therefore, it is crucial for nursing education programs to combine artificial intelligence technologies with pedagogical foundations to provide student-centered, safe, and effective learning environments.

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.

To evaluate the efficacy of a corneal tomography Imaging model in predicting postoperative vault based on preoperative corneal topography in Implantable Collamer Lens (ICL) surgery.

This study (IDEAL 2) is a nationwide stepped-wedge cluster-randomized trial designed to prospectively enroll over 14,400 patients undergoing outpatient head CT angiography (CTA). The trial will be conducted across more than 72 regional hospitals in China. Clusters were randomly assigned to nine randomization groups. In accordance with the stepped-wedge design, clusters will sequentially transition from the control condition (standard human diagnosis) to the intervention condition (AI-assisted diagnosis) at regular intervals over a 10-month period, until all clusters receive the intervention. The primary outcome is the detection rate of intracranial aneurysms. Secondary outcomes include patient prognosis and clinical outcomes.

The goal of this nationwide, multicenter observational study is to develop and externally validate multimodal large models that can (1) predict the risk of brain metastases and (2) estimate long-term prognosis in patients with non-small cell lung cancer (NSCLC). The main questions it aims to answer are: * Can a multimodal large model that fuses imaging, pathology, genomic, and clinical data accurately identify NSCLC patients at high risk of developing brain metastases? * Can a multimodal large model reliably forecast intracranial progression-free survival, progression-free survival, and overall survival across diverse real-world treatment settings? (ie, patients receiving distinct treatment regimens, in different treatment lines and with or without intracranial local therapies). Because this is an observational study, there are no investigational treatments; instead, researchers will compare outcomes among patients who receive standard-of-care therapies (surgery, radiotherapy, systemic therapy) to determine how well the model's predictions align with observed events. Participants will: * Allow use of their routinely collected clinical information, imaging (chest CT, brain MRI), pathology slides, and molecular test results for model training and validation * Undergo standard-of-care follow-ups * Complete optional quality-of-life questionnaires during scheduled visits

This study is a multi-centre prospective observational cohort study recruiting patients with 5-30mm solid and part-solid pulmonary nodules that have been detected on CT chest scans performed as part of routine practice. The aim is to determine whether physician decision making with the AI-based LCP tool, generates clinical and health-economic benefits over the current standard of care of these patients.

This study aims to develop and evaluate an AI-driven Personalized Exercise Feedback Program (AI-PEF) to enhance exercise adherence and health outcomes in mTBI patients. Methods: AI-PEF integrates the transtheoretical model and self-determination theory with machine learning algorithms to provide real-time, personalized feedback. A phased randomized controlled trial will be conducted: Phase I evaluates feasibility and acceptability through Delphi methods with expert consensus and patient feedback; Phase II validates preliminary outcomes with 30 participants in a 2-arm randomized trial; and Phase III assesses the program's impact on adherence, sleep quality, depressive symptoms, and quality of life with 90 participants in a 3-arm randomized trial.