View clinical trials related to Artificial Intelligence.
Filter by:The goal of this clinical trial is to evaluate the effect of LearnGuide, a custom GPT developed with ChatGPT for supporting self-directed learning (SDL) in medical students. The main questions it aims to answer are: How does LearnGuide influence SDL skills among medical students? Can LearnGuide improve critical thinking and learning flow as measured by Cornell Critical Thinking Test (CCTT) Level Z score and Global Flow Score (GFS)? Participants will: Undergo a two-hour introduction to LearnGuide. Engage in 12 weeks of SDL task-based training with LearnGuide's support. If there is a comparison group: Researchers will compare the group utilizing LearnGuide for SDL and the group without this tool to see if there is a significant difference in SDL skills, critical thinking, and learning flow experiences.
Our study presents a detection model predicting a diagnosis of jaundice (clinical jaundice and occult jaundice) trained on prospective cohort data from slit-lamp photos and smartphone photos, demonstrating the model's validity and assisting clinical workers in identifying patient underlying hepatobiliary diseases.
Rationale: To date, the diagnosis and subtyping of basal cell carcinoma (BCC) is verified with histopathology which requires a biopsy. Because this technique is invasive, new non-invasive strategies have been developed, including Optical Coherence Tomography (OCT). This innovative technique enables microscopically detailed examination of lesions, which is useful for diagnosing and identification of various subtypes of BCC. The diagnostic value of the VIVOSIGHT OCT in daily clinical practice, has not been established to date.
Slit-lamp images are widely used in ophthalmology for the detection of cataract, keratopathy and other anterior segment disorders. In real-world practice, the quality of slit-lamp images can be unacceptable, which can undermine diagnostic accuracy and efficiency. Here, the researchers established and validated an artificial intelligence system to achieve automatic quality assessment of slit-lamp images upon capture. This system can also provide guidance to photographers according to the reasons for low quality.
Fundus images are widely used in ophthalmology for the detection of diabetic retinopathy, glaucoma and other diseases. In real-world practice, the quality of fundus images can be unacceptable, which can undermine diagnostic accuracy and efficiency. Here, the researchers established and validated an artificial intelligence system to achieve automatic quality assessment of fundus images upon capture. This system can also provide guidance to photographers according to the reasons for low quality.
This is an artificial intelligence-based optical artificial intelligence assisted system that can assist endoscopists in improving the quality of endoscopy.