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Use Cases of AI in Medical Imaging
Artificial Intelligence and machine learning are slowly infiltrating to the healthcare industry as they present with more accurate results. AI is increasingly helping to unfold medical imaging, one of the richest sources of information about the patient and often the most complex. Medical imaging is the visualization of body parts, tissues or organs for the use in clinical diagnosis. The imaging techniques consist of the field of radiology, nuclear medicine, optical imaging, and image-guided intervention. AI has already proven that it can accelerate the productivity and accuracy of these processes.
AI will play a key role by augmenting their ability to identify relevant data they need to care for a patient and present it in a concise, easily comprehensible format. In the case of a radiologist, AI will review the images and identify potential findings immediately by combining the image with the patient's treatment history. The top ways AI enhances radiology include
Advanced clinical decision support and diagnosis: AI can supplement and verify the work of clinicians particularly in imaging analytics. The meticulous evaluation of medical images to diagnose diseases is tedious. AI algorithms offer the opportunity to streamline decision making, allowing to review detailed data with improved accuracy and fewer errors.
Enhancing precision medicine: Extracting meaning from medical images through AI algorithms makes significant contributions to precision medicine. Developing AI algorithms can use warehouse data to design innovative tools and discover new ways of treatments.
Order scheduling and patient screening: AI can identify patients who are at high risk of missing their radiology care or missing appointments.
AI has the potential to aid in medical imaging with making informed clinical decisions and choosing effective treatments. With AI, the field of imaging analytics can allow clinicians to improve care delivery and patient outcomes.