AI IN CLINICAL TRIALS (CT)
From Data to Discovery
The Next Generation of Evidence-Based Medicine :
The ability of Artificial Intelligence (AI) to perform tasks that humans do, albeit more effectively, quickly, and affordably, is becoming increasingly sophisticated. Robotics and artificial intelligence are already commonplace in our daily lives, but the field of healthcare is where they have the most potential.
Clinical trials are the most recent area of drug development to acknowledge AI's potential and allow it to positively disrupt it. Medical imaging is one of the most promising fields for the clinical application of AI.
Data collection, image reconstruction, analysis, and interpretation are just a few of the potential uses for this field.
The use of artificial intelligence (AI) in medical imaging is currently the subject of extensive research and has demonstrated remarkable sensitivity and accuracy in identifying imaging abnormalities.
The quality of the received images is evaluated to make sure that various elements, including the images, are acquired within the trial's parameters and study-relevant areas. The quality assessment process in the imaging workflow is carried out by imaging technologists with training in medical imaging or associates with training in the pertinent modality and trial protocol. Several factors are assessed during the image quality check, including the coverage, image characteristics, motion, artifacts, contrast, and noise.
AI also aids in the identification of patients, organ segmentation, removal of PHI, and image inspection.
AI for Quality Control and Data Management in Trails :
QC Process :
AI models to automate the QC process is that it will result in unmatched objectivity, scalability, and cost savings.
Instant Evaluations :
AI enables instant evaluations with a turnaround time.
Data Review :
AI allows data review before cloud upload.
Availability :
AI offers round the clock availability and zero human errors.
Data Management :
AI is a logical alternative for effectively managing and mining data due to data volume and complexity.