258 Clinical Trials for Various Conditions
Lumen apposing metal stents are now being used to help patients who suffer from cholecystitis (infection of the gallbladder), especially in cases where patients are not candidates for surgery. Lumen apposing metal stents are effective for draining the gallbladder, however, placement is technically challenging. Scientists have developed an artificial intelligence to aid doctors in the deployment of these stents into the gallbladder. The aim of this study is test the performance of an artificial intelligence in providing physicians accurate information for gallbladder drainage.
A multicenter study will be conducted to assess the role of the AI/ML technologies of Origin Medical EXAM ASSISTANT (OMEA) in interpreting first-trimester fetal ultrasound examinations (11 weeks 0 days - 13 weeks 6 days). The performance of the AI-based system will be compared against the ground truth provided by an independent reading panel of maternal-fetal medicine physicians.
The goal of this clinical trial is to compare the use of the Vektor Computational ECG Mapping System (vMap®) with pulmonary vein isolation (PVI), to using PVI alone, to treat Atrial Fibrillation (AF) in adults. Participants will have a 50/50 or 1 out of 2 chance of being placed in the treatment or control arm. The control arm of the study involves PVI alone for ablation procedure(s). The treatment arm involves the use of vMap mapping in addition to PVI to plan ablation procedure(s).
The goal of this clinical trial is to compare patient-centered outcomes when 3D screening mammograms are interpreted with versus without a leading FDA-cleared AI decision-support tool in real-world U.S. settings and to assess patient's perspectives on AI in medicine. The main questions it aims to answer are: 1. Will AI use be associated with an increase in cancer detection and an initially higher recall rate as radiologists start using AI, followed by a recall rate comparable to that without AI (no more than 1.5 percentage-points higher) after a learning curve period? Will AI use will be associated with lower rates of missed breast cancers and similar rates of false alarms after a learning curve period? 2. Will improved patient outcomes with AI be most pronounced for exams on women who are White, older, and have less dense breasts, and on baseline exams? Will AI aid patient outcomes when the interpretation is by radiologists with less clinical experience, lower annual interpretive volume, and less tolerance of ambiguity? Yet, will there be greater automation bias (the tendency for humans to defer to a computer algorithms' results) noted among these radiologists? 3. What are patients' perspectives on AI in mammography, including their confidence in breast cancer screening when interpreted with vs. without AI? What are patients' perspectives on the importance of the study results? Researchers will compare patient-centered outcomes when 3D screening mammograms are interpreted with versus without a leading FDA-cleared AI decision-support tool in real-world U.S. settings. This trial will include all adult patients undergoing 3D mammography breast cancer screening at imaging facilities across University of California at Los Angeles and University of Washington health systems and all radiologists interpreting breast cancer screening. All screening mammograms at these facilities will be randomized to either intervention (radiologist with AI support) versus usual care (radiologist alone) to see if interpreting these mammograms with the AI tool's assistance improves patient outcomes.
This single-center, prospective study is being conducted at AABP Integrative Pain Care and Wellness in Brooklyn, New York, USA. The aim of this study is to collect ultrasound images from healthy volunteers and evaluate the performance of the Nerveblox software using this image dataset. Nerveblox is a software as a medical device which is designed to assist anesthesiologist for ultrasound-guided regional anesthesia, prior to needling procedure.
Ear infections are common in young children with cold symptoms, but they can be difficult to diagnose due to small ear canals, child movement, and limited viewing time. In this study, investigators will take photos of the eardrums of children 6-24 months of age with upper respiratory symptoms. The photos will be reviewed by imaging software enhanced with artificial intelligence (AI app) to determine whether the AI app changes how ear infections are diagnosed and treated. The AI app has undergone rigorous study and was found to be highly accurate; but how using this technology affects the diagnosis and treatment by clinicians has not been studied. This research may help improve diagnostic accuracy for ear infections and ensure antibiotics are prescribed only for those children who have definite ear infections.
The purpose of this study is to develop a new way to diagnose prostate cancer through the use of artificial intelligence. The goal is for this new method to reduce delays in diagnoses and to avoid invasive procedures such as biopsies.
Pharmacists currently perform an independent double-check to identify drug-selection errors before they can reach the patient. However, the use of machine intelligence (MI) to support this cognitive decision-making work by pharmacists does not exist in practice. This research is being conducted to examine the effectiveness machine intelligence (MI) advice on to determine if its impact on pharmacists' work performance and cognitive demand.
This is a three-arm pragmatic RCT of 238 outpatient physicians at a large academic health system, randomized 1:1:1 to one of two AI scribe tools or a usual-care control group. The two-month study will observe and compare the effects of each tool prior to system-wide roll out of selected tool (anticipated Spring 2025). We will use covariate-constrained randomization to balance the arms in terms of physician baseline time in notes, survey-measured level of burnout, and clinic days per week. The primary purpose of the initiative is to improve quality, efficiency, and business operations at University of California, Los Angeles (UCLA) Health, and this initiative is not being done for research purposes. The results of this operational initiative will inform the widespread roll out of AI scribe tools across all providers within the UCLA Health System. Nevertheless, the UCLA study team plans to rigorously examine and publish the impact of this intervention across the health system, which is why the study team pre-registered the initiative.
This study investigates whether AI-driven analysis of speech can accurately predict clinical diagnoses and assess risk for various mental or behavioral health conditions, including attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder, bipolar disorder, generalized anxiety disorder, major depressive disorder, obsessive compulsive disorder (OCD), post-traumatic stress disorder (PTSD), and schizophrenia. We aim to develop tools that can support clinicians in making more accurate and efficient diagnoses.
This study aims to investigate whether a novel artificial intelligence based screening strategy (AI-Based point of caRe, Incorporating Diagnosis, SchedulinG, and Education or AI-BRIDGE), which allows primary care providers to screen patients for vision-threatening diabetic eye disease in the primary care clinic, improves screening and follow-up care rates across race/ethnicity groups and reduces racial/ethnic disparities in screening.
This research study is being conducted to improve eye care by using artificial intelligence (AI) to make diabetic eye screenings faster and more accessible. AI technology mimics human decision-making, enabling computers and systems to analyze medication information. Specifically for this screening, AI examines digital images of the eye and based on that information, may identify if a participant has diabetic retinopathy. It can assist doctors in making decisions about a participant's diagnosis, treatment or care plans to improve patient care. This is a collaboration between San Ysidro Health (SYHealth), University of California, San Diego (UC San Diego), and Eyenuk. The Kaiser Permanente Augmented Intelligence in Medicine and Healthcare Initiative (AIM-HI) awarded SYHealth funds to demonstrate the value of AI technologies in diverse, real-world settings.
This will be an open-label, parallel-group, randomized trial. Patients will be randomized to review the patient-friendly discharge instructions at the time of discharge (intervention group) vs the standard of care. The intervention differs from the standard of care in that patients will be given additional medical documentation in the intervention group.
Based on prior studies, trainee and practicing gastroenterologists miss pre-cancerous polyps (adenomas and serrated polyps) during colonoscopy. The use of computer-aided detection (CADe) systems, a form of artificial intelligence (AI) has been shown to help identify colorectal lesions for practicing gastroenterologists. However, less is known how AI impacts polyp detection for trainees. The investigators are conducting a tandem colonoscopy study wherein a portion of the colon is examined first by the trainee and then the attending physician. For each procedure, randomization will occur which will determine whether or not the trainee will utilize AI for their examination of the colon. At the end of the study, the investigators will determine whether AI helps trainees miss fewer polyps during colonoscopy. The investigators will also conduct interviews with trainees to understand how AI impacts colonoscopy training.
The purpose of this study is to investigate the efficacy of a novel artificial intelligence (AI) device designed to assist in Ultrasound guided regional anesthesia (ScanNav Anatomy Peripheral Nerve Block; ScanNav), in the teaching and training of anesthesiology residents in the subspecialty of regional anesthesia.
Nearly 23,000 adults are diagnosed with primary central nervous system (CNS) malignancy yearly. An additional 200,000 adults are diagnosed with brain metastasis. There are significant variations in CNS tumor treatment. However, due to significant heterogeneity in patient baseline factors, identifying unwarranted variation is challenging. Ghogawala et al have previously demonstrated that, among patients undergoing surgical treatment of cervical myelopathy and lumbar degenerative spinal disease, an expert panel consisting of surgeon experts can identify variations in proposed surgical procedure and demonstrated superior patient outcomes when the surgery performed matched the procedure recommended by expert consensus. Expert panel surveys have not previously been used to identify variations in care among patients with CNS malignancy. The primary aim is to determine whether patient outcomes are superior when treatment aligns with recommendations made by a clinical expert neurosurgical panel. The study also seek to identify patient factors that predispose to variability in care. Our long-term aim is to determine whether predictive artificial learning algorithms can achieve the same outcomes, or better, as clinical expert panels, but with greater efficiency and greater capacity to be available for more patients. The investigators hypothesize that: * When a team of 10 medical experts has greater than 80% consensus regarding optimal treatment and when the doctor and patient select that specific treatment, the outcome is superior than when a patient and doctor select an alternative procedure. * When a team of 10 medical experts has greater than 80% consensus regarding optimal treatment, the structured data used by the experts can be processed and trained by computing algorithms to predict the pattern recognized by the experts - i.e. - the computer can predict how an expert panel would vote. Procedures include the following: 1. Chart review portion of study: Patients will be identified from case logs of the principal investigators from July 2017 through July 2023. Data will be collected retrospectively and will include age, non-identifier demographics, diagnosis details, operative/treatment characteristics, post-treatment characteristics, and follow-up characteristics. Images reviewed will include pre and post-treatment MRIs obtained as part of routine care. Data will be abstracted from the medical record (Epic/Soarian and PACS) and recorded in an excel database. 2. Survey portion of study: De-identified structured radiographic data and a brief clinical vignette without patient identifiers will be uploaded to Acesis Healthcare Process Optimization Platform (http://www.acesis.com/our-platform). A survey will be generated by Acesis and emailed to the subject experts/participants. This portion is prospective. 3. Cohort definitions: 1. Patients will be assigned to either "expert-treatment consensus" or "no expert-treatment consensus" arms based on whether greater than 80% consensus is achieved 2. Patients will be assigned to either "Expert consensus-aligned" or "Expert consensus - unaligned" arms based on whether expert survey results match actual treatment given. 4. Data will then be analyzed using appropriate packages with SAS statistical analysis software. Survival analysis will be performed to determine whether consensus predicts improved progression free survival (PFS). 5. The structured and de-identified radiographic images used by the experts in surveys will be used for training and development of an AI algorithm. The aim of this portion of the study is to determine whether standardized and structured imaging can be used to train an algorithm to predict whether expert consensus is achieved and the recommended treatment.
N = 264 patients (50% female) aged 75 years and above undergoing colonoscopy were enrolled. Patients were randomly assigned into one of the three intervention groups: the primary intervention arm (CADe in combination with the MED), the second group with MED alone, and the control group with WLE. All detected lesions were removed and sent to histopathology for diagnosis. The primary outcome was the adenoma detection rate. Secondary outcomes were adenoma detection in the left colon in our cohort of patients.
The goal of this project, Conversational Artificial Intelligence (AI) to Improve PeRiconception Care Access (CIRCA), is to engage patients in care early in pregnancy (before prenatal care starts) and safely triage concerns to PEACE to reduce unnecessary emergency department visits.
The overarching objective of this project is to transform access to assistive communication technologies (augmentative and alternative communication) for individuals with motor disabilities and/or visual impairment, for whom natural speech is not meeting their communicative needs. These individuals often cannot access traditional augmentative and alternative communication because of their restricted movement or visual function. However, most such individuals have idiosyncratic body-based means of communication that is reliably interpreted by familiar communication partners. The project will test artificial intelligence algorithms that gather information from sensors or camera feeds about these idiosyncratic movement patterns of the individual with motor/visual impairments. Based on the sensor or camera feed information, the artificial intelligence algorithms will interpret the individual's gestures and translate the interpretation into speech output. For instance, if an individual waves their hand as their means of communicating "I want", the artificial intelligence algorithm will detect that gesture and prompt the speech-generating technology to produce the spoken message "I want." This will allow individuals with restricted but idiosyncratic movements to access the augmentative and alternative communication technologies that are otherwise out of reach.
The study is working to identify actions of surgeons in the operating room that can contribute to work-related musculoskeletal disorders. This includes poor positioning and time spent in poor positioning while working in the operating room. The study is also looking to determine if fatigue plays a role in work-related musculoskeletal disorders and whether an education intervention will change ergonomic risk.
Observational study. The purpose of this study is to evaluate the use of real-time surgical navigation for the localization and surgical removal of soft tissue tumors. The goal is to collect information about the efficiency and effectiveness of the EnVisio Surgical Navigation for intraoperative guidance to obtain negative margin on initial specimen. Prospective Patient Study: 200 consecutive patients
Identifying eligible patients is a key process in the clinical trial enterprise. Currently, this process relies on time-intensive manual chart review, creating a rate-limiting step for trial participation. The integration of AI technology into the trial screening process has potential to improve participation rates. This study aims to assess the performance (accuracy, efficiency) of AI-augmented patient identification and inform optimal integration into clinical research screening processes.
The primary goal of this proposal is to validate a novel genomic and microbiome predictive model that may be used to assess a person's risk of developing opioid use disorder (OUD). The following will be tested: (1) MODUS (Measuring risk for Opioid use Disorder Using SNPs), which is a genomic panel consisting of a set number of proven single nucleotide polymorphisms (SNP) that utilizes machine learning to determine an individual's risk; and (2) MICROUD (MICRObiome for Opioid Use Disorder), which will be a novel microbiome prediction panel for OUD risk. MODUS and MICROUD will be developed using existing public datasets with genomic and microbiome data (e.g., All of Us, Human Microbiome Project). During development of these predictive models, in parallel, an external prospective validation cohort will be recruited consisting of subjects from the University of California, San Diego, Veteran Affairs of San Diego, and Veteran Affairs of Palo Alto (each site with separate IRB). The hypothesis is that MODUS and MICROUD will have high predictive potential for identifying high risk patients for OUD.
The aim of this study is to determine if oocyte sorting for group culture using an artificial intelligence image analysis tool (MagentaTM) increases the usable blastocyst yield and subsequent pregnancy in patients undergoing IVF.
The goal of this clinical trial is to learn about how Urogynecology patients use Artificial Intelligence (AI) Chatbots like ChatGPT, and how it affects healthcare decision making. The main question\[s\] it aims to answer are: * How does the AI Chatbot affect participants' understanding of diagnoses and participant satisfaction with a urogynecology consultation? * How accurate is the chatbot-provided diagnosis and counseling information? Participants will be asked to use the ChatGPT chatbot and ask it questions about the main problem the participant is seeing the doctor for, and will also be asked to fill out some questionnaires. Researchers will compare using the Chatbot before the visit, after the visit, or not at all to see if the way participants understand the information changes based on timing of use.
Behavioral health problems, such as depression and anxiety, are common yet often are not identified by emergency department doctors and nurses. These mental health conditions can be due to medical issues or can worsen medical problems. One way investigators hope to do a better job of learning about mental health is by training Artificial Intelligence (AI) software to detect anxiety and depression by analyzing facial expression and tone of voice. Participants are invited to participate in a study which may help improve emergency department care. An audio and video recording of the participant's responses to some simple, non-psychological questions will be analyzed by a computer to determine whether investigators can assess mood and anxiety by analyzing speech and visual patterns. The audio and video will not be listened to nor watched by study personnel, only analyzed by a computer. The investigator's hope is that it will help others in the future by aiding in the assessment of psychological state. This study is being conducted at CMC ED only.
Assessment of dietary intake in large, free-living populations is inherently challenging due to the complex nature of human diet. Advancements in traditional methods of dietary assessment (i.e., web-based dietary recalls or records) have aimed at improving data accuracy while reducing participant burden. Further utilizing food recognition technologies to capture real-time food intake may aid in overcoming limitations of existing methods. Keenoa, an artificial intelligence-enhanced, image-assisted tool, is a newly designed mobile application that may facilitate collection of dietary data. Primarily, the investigators will assess acceptability and usability of Keenoa compared with the traditional, web-based Automatic Self-Administered 24-Hour (ASA24) Dietary Assessment Tool in the Framingham Heart Study Third Generation-based cohorts at examination 4. The investigators will also determine the proportion of participants who complete all three days of dietary assessment, either through Keenoa or ASA24. Further, the investigators will relate dietary determinants of glycemic variability (e.g., percent carbohydrate, fiber intake, etc.), obtained from each dietary assessment tool, to the continuous glucose monitor (CGM)-derived outcomes. With a randomized block design, this study will take place as part of the Framingham Heart Study (FHS) glucose study (R01 DK129305). Currently participants from the Third Generation-based cohorts are asked at their fourth examination to wear Dexcom G6 Pro continuous glucose monitor on either their arm or abdomen for a duration of at least 4 days. During this time, participants are asked to complete 3 consecutive days of dietary record through ASA24. For this trial, the investigators will randomize the dietary assessment tool weekly between ASA24 and Keenoa, therefore, depending on the week of administration, participants will be randomized to either a 3 days dietary record via ASA24 or a 3-day dietary record through Keenoa. This trial will last a total of 6 weeks.
This study evaluates the impact of a fully digital, autonomous, and artificial intelligence (AI)-driven lifestyle coaching program on managing blood pressure (BP) among adults diagnosed with hypertension. Participants received a BP monitor and a wearable activity tracker to facilitate data collection. This data, along with responses from a questionnaire mobile app, were analyzed by an automated analytics engine employing statistical and machine learning techniques. The program delivered tailored lifestyle coaching directly to participants through a mobile app, aiming for precise and effective BP management.
Infantile spasms are a type of seizure linked to developmental issues. Unfortunately, they are often misdiagnosed, causing delays in treatment. The purpose of this study is to develop a computer program that can reliably differentiate infantile spasms from similar, yet benign movements in videos. This computer program will learn from videos taken by parents of study participants. Quickly recognizing and treating infantile spasms is crucial for ensuring the best developmental outcomes.
The purpose of this study is to evaluate the AI-ECG algorithm for HCM in detecting HCM and in differentiating it from athlete's heart using not only the standard 12-lead ECG, but also ECGs obtained with the Apple Watch and Alivecor KardiaMobile devices.