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AI (Artificial Intelligence) clinical trials

View clinical trials related to AI (Artificial Intelligence).

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NCT ID: NCT05963802 Completed - Clinical trials for Educational Activities

Evaluation of the Efficacy and Usability of Artificial Intelligence (ChatGPT) for Health Sciences Students

AIHSS
Start date: September 1, 2023
Phase: N/A
Study type: Interventional

Crossover Randomized Control trial, in which subjects are randomly assigned to one of two groups: one (ChatGPT) receiving the intervention that is being tested, and the control group receiving usual online resources.

NCT ID: NCT05389774 Recruiting - Lung Cancer Clinical Trials

DOLCE: Determining the Impact of Optellum's Lung Cancer Prediction Solution

DOLCE
Start date: March 23, 2023
Phase:
Study type: Observational

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.

NCT ID: NCT04527510 Active, not recruiting - Breast Cancer Clinical Trials

Remote Breast Cancer Screening Study

Start date: January 1, 2021
Phase:
Study type: Observational

A multi-center, prospective, cohort study to evaluate the efficiency of breast cancer screening based on Automated Breast Ultrasound (AB US) with remote reading mode.

NCT ID: NCT04489992 Recruiting - COVID-19 Clinical Trials

Experiment on the Use of Innovative Computer Vision Technologies for Analysis of Medical Images in the Moscow Healthcare System

Start date: February 21, 2020
Phase:
Study type: Observational

It is planned to integrate various services based on computer vision technologies for analysis of the certain type of x-ray study into Moscow Unified Radiological Information Service (hereinafter referred to as URIS). As a result of using computer vision-based services, it is expected: 1. Reducing the number of false negative and false positive diagnoses; 2. Reducing the time between conducting a study and obtaining a report by the referring physician; 3. Increasing the average number of radiology reports provided by a radiologist per shift.