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Every year, medical imaging saves millions of lives by assisting doctors in detecting and diagnosing diseases ranging from cancer and appendicitis to stroke and heart disease because non-invasive early disease detection saves many lives, scientific funding increases.
Fremont, CA: Artificial intelligence (AI) has the potential to transform the medical imaging industry by quickly sifting through mountains of scans and providing providers and patients with life-changing insights into a variety of diseases, injuries, and conditions that would be difficult to detect without the supplemental technology.
Images are the most common data source in healthcare, but they are also challenging to analyze. Clinicians must now rely primarily on medical image analysis performed by overworked radiologists and occasionally investigate scans themselves. A medical expert mainly interprets medical data. Image interpretation by a human expert is completely limited due to subjectivity, the complexity of the image, the numerous variations that exist across different interpreters, and fatigue.
Despite constant advances in medical imaging, nearly every four patients have false positives on image readings. This can result in unnecessary invasive procedures and follow-up scans, adding cost and stress to patients' lives.
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While false negatives occur less frequently, the consequences can be disastrous. The surprisingly high rate of false positives is due, in part, to radiologists' concerns about missing a diagnosis. Unfortunately, late disease detection raises treatment costs and reduces survival rates significantly.
However, this is about to change as medical technology pioneers apply AI to image analysis. The most recent deep-learning algorithms allow automated analysis to produce accurate results that are delivered immeasurably faster than the manual process. As these automated systems become more prevalent in the healthcare industry, they may cause radical shifts in how radiologists, clinicians, and even patients use imaging technology to monitor treatment and improve outcomes.
Deep-learning algorithms and analytics are used in AI applications for radiology to systematically assess images for tumors or suspicious lesions and provide detailed reports on their findings in real-time. To detect anomalies, these systems are trained on labeled data. Then, when a new image is submitted, the algorithm uses its training to distinguish between normal and abnormal structures (for example, benign/malignant). As these tools become more sensitive, they may allow for earlier disease diagnosis because they will detect tiny variations in an image that the human eye would miss.