Artifical Intelligence

This study evaluates whether large language models (LLMs) can reliably assess written clinical-reasoning case examinations completed by undergraduate physiotherapy students, compared with faculty assessment. In the course "Specific Methods in Physiotherapy" (third year of the Physiotherapy Degree), students solve complex clinical cases that require clinical reasoning, technical knowledge, and therapeutic decision-making. These cases are traditionally graded by faculty, a time-consuming process that may show inter-rater variability. A set of de-identified student case examinations will be assessed using the rubric currently applied in the course, which covers clarity and structure of clinical reasoning, integration of the biopsychosocial model (ICF and APTA frameworks), accuracy in identifying pain mechanisms, coherence between diagnosis, hypotheses, and treatment, originality and depth of analysis, and professional writing. Each examination will be scored independently by three LLMs (for example, Claude, ChatGPT, and Gemini), each receiving an identical standardized prompt that embeds the same rubric, and by faculty serving as the reference standard. To avoid overloading faculty, full double human grading may not be feasible; the human reference will therefore consist of expert faculty grading by one independent rater or, when resources allow, two independent raters. In contrast, paired assessment is fully implemented across the AI models: each examination is scored by several LLMs, and each model is queried in duplicate, allowing the study to estimate agreement between models and the test-retest stability of each model. The primary aim is to quantify agreement between LLM-generated scores and the faculty reference score. Secondary aims include agreement among the LLMs, test-retest reliability of each model, criterion-level agreement, the quality and usefulness of the qualitative feedback generated, the time and cost associated with each approach, and students' perceptions of the usefulness of human versus AI feedback. The findings will clarify the strengths and limitations of LLMs as supportive tools for formative assessment in health-professions education and will inform criteria for their responsible and effective use. No LLM output will affect students' official grades, which remain the sole responsibility of faculty.

This study aims to interpret the Bispectral Index (BIS) monitoring method, which we routinely use for monitoring in scoliosis surgery, with artificial intelligence (AI) tools and to determine the accuracy and reliability of AI tools in clinical practice by comparing this interpretation with the interpretations of two clinicians experienced in BIS.

This is a multi-center, cross-sectional study evaluating a smartphone-based artificial intelligence (AI) system for anterior segment eye disease screening. The system is designed to identify 16 clinically important anterior segment conditions from images captured using a standard Android smartphone. A core design feature of the system is that all image analysis is performed entirely on the smartphone itself, without requiring internet connectivity or cloud-based server infrastructure. The study is motivated by a structural challenge in the deployment of medical AI: systems that depend on cloud infrastructure for inference are non-functional in settings without reliable internet access, which disproportionately excludes populations in low-resource regions where the burden of preventable eye disease is highest. This study evaluates whether an on-device AI system, designed with operational constraints as a primary engineering objective, can deliver clinically acceptable diagnostic performance while remaining operable under real-world connectivity limitations. The study comprises five evaluation components. First, the diagnostic performance of the AI system is benchmarked against board-certified ophthalmologists of varying seniority on a standardized set of smartphone-captured anterior segment images. Second, the usability of the system is evaluated among non-medical users who perform self-administered screening with minimal instruction, with per-screening time recorded across consecutive attempts to characterize the learning curve. Third, a head-to-head field trial directly compares the on-device AI system against a functionally equivalent cloud-based deployment of the same model architecture across key operational dimensions including screening duration, diagnostic performance, and user acceptability. Fourth, population-level screening is conducted among consecutively enrolled community residents at two low-resource sites, with per-disease sensitivity and specificity calculated against reference-standard slit-lamp examinations. Fifth, pre-specified health-economic and environmental analyses compare the two deployment modalities in terms of per-person screening cost, cost-effectiveness, per-inference electricity consumption, and projected carbon emissions at scale. The reference standard for all diagnostic comparisons is slit-lamp biomicroscopic examination performed by board-certified ophthalmologists. The study is designed and reported in accordance with the DECIDE-AI reporting guideline for early-stage clinical evaluation of AI-driven decision-support systems.

Heart valve diseases are among the most serious cardiovascular conditions in older age. One of the most common forms is aortic valve stenosis, a narrowing of the valve opening between the left ventricle and the main artery. As the valve becomes tighter, the heart must work harder and harder to pump blood through the body. This process often develops slowly over many years and initially causes no clear symptoms. As a result, the condition is frequently detected only in advanced stages, when warning signs such as shortness of breath, chest pain, or dizziness appear. Without treatment, aortic valve stenosis can become life-threatening. If detected early, however, very effective treatment options are available today. Up to now, the disease has been reliably diagnosed mainly through echocardiography. Yet this method is complex, costly, and requires specialized medical staff. A simple, affordable, and broadly accessible screening option does not yet exist. The interdisciplinary clinical research project explores whether conventional smartphones could fill this gap. Almost all modern devices are equipped with sensors such as microphones, accelerometers, and gyroscopes. These can capture both heart sounds and subtle vibrations of the chest. The research team is investigating whether reliable diagnostic information for the diagnosis of aortic valve stenosis can be extracted from such recordings. To achieve this, the signals are processed with newly developed methods and analyzed using artificial intelligence. For the study, several hundred patients with and without valve disease will be examined. The smartphone results will be compared with established diagnostic standards, particularly echocardiography, to test accuracy and reliability. If successful, the approach could enable a straightforward, digital heart check at home using nothing more than a conventional smartphone. Such a tool would provide an accessible, low-cost, and widely available method for early detection, helping more people receive timely and potentially life-saving treatment.

Freezing of gait (FOG) is a debilitating symptom of Parkinson's disease increases the risk of falling. Despite being a common symptom, it is still difficult to evaluate freezing of gait quickly and accurately. Currently, the gold-standard method to determine the severity of FOG is a manual analysis of video footage by an experienced assessor, collected during standardized FOG-provoking walking tests. Because this is a very time-intensive process, where different assessors sometimes obtain different results, our team at KU Leuven have developed an artificial-intelligent (AI) algorithm trained to identify FOG episodes based on wearable inertial measurement unit (IMU) sensor data. The AI algorithm has already undergone initial validation during laboratory testing, yielding promising results. The aim of this study is to investigate whether the AI algorithm can accurately detect FOG episodes in a less controlled environment, namely the home environment. In a second phase, the investigators will also use the collected data to improve the AI algorithm for automated FOG detection in the home. Finally, the investigators want to explore whether the AI algorithm can detect FOG in real-time.

The global increase in cancer cases has made breast cancer the second most common cancer after lung cancer and a primary health problem among women. Early diagnosis is the most critical factor in improving survival rates and quality of life in breast cancer. Breast self-examination (BSE), which enables individuals to notice changes in their own breast tissue during the early diagnosis process, is a low-cost and effective awareness method. It is essential that nurses, who play a key role in raising public awareness on this issue, and nursing students, who are the future healthcare professionals, have sufficient knowledge and practical skills in BSE. However, the literature shows that even if students have theoretical knowledge, their application rates are low. In this context, the "AI-Supported Flipped Learning" model, which goes beyond traditional methods and supports active learning, personalized feedback, and digital literacy, has the potential to be an innovative solution in nursing education. Objective: This study aims to evaluate the effect of AI-supported flipped learning model and traditional education on the knowledge levels and performance skills of nursing students regarding BSE knowledge and skills.

The goal of this clinical trial is to develop and evaluate an Artificial Intelligence Assisted Prehabilitation Program (AI APP) for returning to work and cost-effectiveness analysis in patients with oral cancer (OC). The main questions it aims to answer are: * What kinds of needs are related to returning to work (RTW) in patients with OC from diagnosis to survival that we can incorporate into the development of AI APP to assist this population ? * How is the effect of the AI APP that based on findings from the first question for patients with OC on physical and psychological distress, fear of recurrence, self-efficacy in coping with cancer, communication, motor function, quality of life, and RTW? * How is the effect of the RTW AI prediction model to identify high-risk groups ? And how is the comprehensive cost effectiveness of benefits and quality of life of the AI APP for OC population? Researchers will compare patients without using AI APP to see if the AI APP works to assist with coping physical and psychological distress, communication, motor function, quality of life, and RTW issues for individuals with OC? Participants will: * Be asked to fulfill a structural questionnaire, or engage in a semi-structured one-by-one interview or a focus group to assess their physical, psychological, and social support needs in the first stage. * Be invited to participant the pilot testing of AI APP in the second stage. * Be provided and trained by 3-month AI APP for 3 months or cared as usual in the third stage. * Complete a structural questionnaire and follow up one year, including the baseline (before using the AI app) and at 1-2 weeks, 3 months, 6 months, 9 months, and 12 months after the baseline. * Engage in one-by-one interview or a focus group to assess user experiences of the AI APP.

Cardiovascular disease is the leading cause of death worldwide, and individuals with diabetes or other cardiometabolic conditions are at increased risk of adverse cardiovascular outcomes. Although advances in prevention and treatment have reduced cardiovascular events globally, cardiometabolic disease continues to represent a significant health burden, particularly in regions with high diabetes prevalence. In Qatar and other Gulf Cooperation Council countries, the prevalence of diabetes and obesity is increasing, contributing to a high proportion of participants presenting with acute coronary syndrome who have type 2 diabetes or prediabetes. This observational study will use electronic medical record data from patients hospitalized at the Heart Hospital with acute coronary syndrome and a concomitant diagnosis of diabetes or prediabetes. The study will assess trends in cardiovascular risk factors and cardiovascular events, including readmission and mortality. An artificial intelligence component will be used to develop and validate machine learning based risk prediction models to forecast adverse cardiovascular outcomes in participants with cardiometabolic disease. These models will integrate clinical, biochemical, imaging, and other non-invasive data routinely collected during participants care to identify predictors of cardiovascular events.

The integration of Artificial Intelligence (AI) in anesthesiology offers the potential to shift patient monitoring from reactive to predictive. Deep learning architectures, specifically Long Short-Term Memory (LSTM) networks, excel at processing complex, time-series data to forecast future clinical states. While standard PK/PD models (such as the state of the art Eleveld model for Propofol and Remifentanil) estimate target-site drug concentrations (Ce), they do not account for real-time, patient-specific dynamic responses. This study aims to deploy an AI framework designed to predict future physiological states.

The second-trimester morphology ultrasound is a key examination in obstetric monitoring that aims to assess fetal growth, identify any structural abnormalities, and inspect anexes such as placenta, umbilical cord, cervix,... Several studies suggest that a significant proportion of fetal malformations can be detected during this time frame if a complete morphological analysis is performed. However, the reliability of the screening depends on the quality of the equipment, the operator's level of expertise, and adherence to protocols that define the necessary scans. In France, since the first reports of the National Technical Committee on Prenatal Screening Ultrasound (2005), particular attention has been paid to standardizing practices. More recently, the French National Conference on Obstetric and Fetal Ultrasound (CNEOF) published new recommendations (2022, revised in 2023) including the development of reference silhouettes for the second-trimester examination, proposing 26 views (22 required and 4 additional). However, the CNEOF does not formalize quality criteria for evaluating the conformity of these images; this task has been taken over by the French College of Fetal Ultrasound (CFEF), which has established a scoring and validation grid for each fetal slice (see CFEF 2022 document). In parallel, artificial intelligence (AI) is gradually becoming established as a decision support and automation tool in medical imaging, particularly in ultrasound. Deep learning algorithms are capable of identifying anatomical structures, positioning measurement markers, and selecting the most optimal slice, reducing inter-operator variability and streamlining workflow. In the field of obstetric ultrasound, some companies have launched systems capable of detecting or annotating fetal structures in real time, potentially improving diagnostic reliability and reproducibility. Samsung has developed a system called Live View Assist, available on its latest generation ultrasound scanners, which uses AI to automatically recognize and freeze the required fetal slices in real time. The tool also offers automated validation: if the detected slice conforms to the expected standards, it is directly checked off on a checklist. This innovation promises time savings, a reduced risk of missing certain complex slices, and improved standardization. However, there is little data, particularly in France, regarding to the actual performance of this tool in a routine screening context. Before considering the integration of Live View Assist and AI into daily practice, it is therefore essential to evaluate the quality of the images it acquires, the feasibility of a complete examination assisted by AI, as well as the potential impact on examination time and improvement of the workload for sonographers. The aim of this study is to evaluate whether the quality of the 20 mandatory images automatically validated by Live View Assist is not inferior to that of the 20 mandatory images acquired and validated manually by an ultrasound technician, according to the CFEF quality criteria based on the silhouettes recommended by the CNEOF.

The goal of this prospective study is to to evaluate the prevalence of metachronous advanced colonic lesions in subsequent surveillance colonoscopies in patients who had previously undergone AI-assisted colonoscopy to conventional colonoscopy examinations. The main question it aims to answer is whether employing AI-assisted colonoscopy can decrease the likelihood of metachronous advanced colonic lesions during subsequent surveillance colonoscopies. Researchers will compare patient who undergo conventional colonoscopy in previous colonoscopy to see if AI-assisted colonoscopy can decrease the likelihood of metachronous advanced colonic lesions during subsequent surveillance colonoscopies. Participants will undergo surveillance colonoscopy to assess the presence of metachronous advanced colonic lesion

This clinical trial is testing whether plain language summaries made by artificial intelligence help people understand their eye doctor's notes better. Adults receiving eye care at the Jules Stein Eye Institute will get either the usual medical notes or a note with the addition of an AI-generated summary that explains the information in simple, everyday words. Participants will then answer a short survey and receive a follow-up call to share how clear the information was, how well they understood their diagnosis and treatment, and whether they feel more confident about their care. The goal is to find out if these plain language summaries can make it easier for people to understand their eye care and improve communication between patients and health care providers.

This study would answer the following question:Does AI application-based training improve cognitive function in Patients with Stroke? The aims of this study: To investigate the efficacy of AI application-based training on cognitive function in stroke patients.

This study will compare different combinations of fertility signs (cervical mucus (CM), luteinizing hormone \[LH\], pregnanediol glucuronide \[PDG\], and basal body temperature \[BBT\]) to determine which are most reliable for identifying ovulation and luteal phase length. Thirty existing Premom App users will track daily observations for three menstrual cycles. Participants will record mucus, perform urine tests, upload test strip photos to the Premom App, and measure BBT. Both participant readings and AI-assisted app readings will be analyzed. The main goal is to find which marker pairings give the most accurate picture of ovulation timing and luteal phase length. Secondary goals include understanding ease of use, the number of tests required, and whether the app improves accuracy.

This study seeks to validate the real-world accuracy of an AI-based algorithm for identifying the location of an accessory pathway from the 12-lead electrocardiogram

Retrospectively collect the clinical data, breast MRI images, breast ultrasound images and reports, laboratory indicators (such as CA199, CA153, CA125, CEA/AFP), pathological diagnosis results, HE staining images, and existing immunohistochemical results (including CD8A, KPT5, GFRA1, PFKP, ER/PR percentage, Her-2 expression, Ki-67 index, etc.) of patients pathologically confirmed with or excluded from breast cancer in our center between January 2019 and December 2024. For biopsy specimens from patients diagnosed with breast cancer and immunohistochemically confirmed as HR+/Her-2+ during the same period, additional immunohistochemical staining for CD8A, KPT5, GFRA1, and PFKP should be performed, with images and results collected. The collected basic clinical information, imaging data, pathological findings, and laboratory metrics of patients will serve as candidate inputs. Units of measurement will be standardized, and missing data will be imputed using the multiple imputation by chained equations algorithm. Data harmonization will employ the Box-Cox algorithm, while min-max scaling will be used for standardization. The adaptive synthetic sampling method with a balance ratio of 0.5 will address data imbalance. For the collected patient data, deep learning will be applied to screen features from the images, combined with clinical significance to identify malignant risk factors. A neural network classifier will be trained on the training set data, with independent variables including breast MRI/ultrasound images, CA199, CA153, CA125, AFP/CEA, etc., and dependent variables including breast cancer status and subtype. Pathological biopsy results will be set as the validation standard. Model tuning will be conducted on the validation set to construct a breast cancer prediction model. It should be noted that as a single-center study, the results have limited generalizability. The further optimization and evaluation plan for the model involves using breast disease screening data from external centers for validation and refinement, evaluating the model's practical impact on clinical decision-making, and continuously tracking and optimizing its performance.

The goal of this observational study is to evaluate the diagnostic accuracy of different deep learning models in detecting dental caries from intra oral images taken by a professional intra oral camera in children. The main question it aims to answer is: What is the diagnostic accuracy of different deep learning models in detecting dental caries from intra oral images taken by a professional intra oral camera in children compared to the conventional clinical visual examination?

The aim of this observational case-control study is to investigate, whether artificial intelligence can detect ultrasound-derived imaging characteristics typical for intensive care unit-acquired weakness. The main questions it aims to answer are: 1. Is the evaluation of specific parameters of neuromuscular ultrasound using AI-based image analysis suitable for detecting and monitoring critically ill ICU patients with ICUAW? 2. Do the results of AI-based ultrasound image analysis correlate with: (A) the severity of ICUAW (B) the visual grading of muscle echogenicity (C) the 30- and 90-day-outcome?

The objective of this prospective observational study is to rigorously examine the feasibility and efficacy of utilizing latent diffusion models for data augmentation in anti-nuclear antibody (ANA) Hep-2 cell immunofluorescence images. The main question it aims to answer is: Can the application of such models potentially enhance the data quality, increase sample diversity, or improve the accuracy and efficiency of subsequent analytical processes (like disease diagnosis and classification) when utilized with ANA-related images?