Artificial Intelligence in Radiology
Artificial Intelligence (AI) is quickly becoming an integral part of the healthcare industry, including the field of radiology. Due to the increasing demands for radiology services in clinical medicine, more companies are developing AI solutions designed to augment workflow of healthcare providers, hospitals and imaging centers. A recent article in Forbes mentioned that some reports have cited an expected 16.5% rise in the use of AI in radiology within the next decade.
AI is being used in radiology in many different ways. A common use case is to identify the onset of diseases in their early stages by providing a more thorough and accurate analysis of medical imaging, which can help providers identify effective solutions and treatments more quickly. AI technology has the capability to sift through millions of images and screen for potential abnormalities throughout the body, helping to significantly reduce the risk of misdiagnosis and oversight, which will also drive more efficient workflows for radiologists. More hospitals and imaging centers are also using different services such as machine learning, cloud computing, and analytics in order to provide more effective and accurate treatments for patients.
Outside of diagnostics and treatment plan, AI is being used to optimize the patient journey, from appointment booking, to effectively routing reads, to billing. Here are several companies in the healthcare space that are using artificial intelligence:
Aidoc.
Their advanced AI-based decision support software analyzes medical imaging to detect time-sensitive abnormalities and prioritizes them in the radiology workflow, helping radiologists to expedite patient care and improve health outcomes.
Braid Health.
The AI-powered software platform connects healthcare professionals with radiologists by providing access to quick and accurate diagnostic image interpretations within minutes.
Gleamer.
Their AI-powered software was developed to enhance radiologists’ productivity by assisting with X-ray image analysis and reporting of abnormalities.
Covera Health.
To help reduce misdiagnoses in radiology, they are empowering radiologists with powerful analytics to better define, measure, and deliver accurate diagnoses. Their applied clinical analytics generate comparative data on frequency, type, and sources of possible errors; in addition to providing access to years of medical records and related data points.
Nines.
The teleradiology company uses machine learning to help reduce the burden on radiology diagnostics departments by triaging critical and life-threatening case of intracranial hemorrhage and mass effect conditions in patients. Radiologists that use NinesAI are notified of a potential life-threatening finding within 15 seconds after a scan, which they can then prioritize to review for further study.
Enlitic.
Their medical software platform uses data to advance medical diagnostics by pairing world-class radiologists with data scientists and engineers, collecting and analyzing clinical comprehensive clinical data to enable doctors to diagnose sooner with more accuracy. The advanced technology integrates seamlessly into any existing health system infrastructure to improve workflow, efficiency, and quality at scale.
Arterys.
The cloud-based imaging software company offers various products that offer timely, accurate and consistent quantification of images of the heart, lungs, and chest X-rays; accelerate speed of results, and improve the quality of information offered to the diagnosing physician.
