Empowering Clinical Decisions with Artificial Intelligence (AI)
Artificial intelligence holds considerable promise for radiology and is by now starting to transform healthcare in many ways. From bridging the space between the demands of ever-increasing, tremendously complex data and the number of radiologists, to simplifying data interpretation through complicated AI algorithms and thus improving the analytical process. AI is a valuable tool that, when combined with the individual knowledge of radiologists and clinicians, offers enormous prospective to the healthcare industry. Key AI trends like informed decision making, integrated diagnostics, and digital twins, focus very much on how radiology plays a major role in the digital transformation of healthcare and how radiologists and clinicians can be empowered to formulate the accurate conclusion for each patient. Artificial Intelligence holds a vast amount of potential to transform aspects of the healthcare industry and is not something to fear; rather it is something to embrace. As per the report titled “Artificial Intelligence in Drug Discovery Market”; published by UnivDatos Market Insights. China has been a major investor for biotech companies in the United States over the past few years. These investments increased significantly in 2019, with USD 1.4 billion into US-based biotech and drug firms, compared to just USD 125.5 million in 2018.
How can AI be a breakthrough in the imaging segment?
The world of radiology has a transformed approach to the design of artificial intelligence. The challenge now becomes identifying opportunities for reducing inefficiencies in imaging segment through AI integration. Based on a current procedures and processes available, here is a breakdown of areas where AI can improve the practice of medical imaging.
- Detection and Prioritization: Detection is the poster child for artificial intelligence in healthcare, but there is even more the advanced technology can attach as a screening test. With computerized detection, radiologist’s examination images based on understanding priority which speeds reporting and advances patient results. With the adding of recovery services, the AI pulls parallel images from a folder for evaluation when it encounters abnormal or difficult cases.
- Segmentation: The operation of separating a field of interest in an imaging study remains a labor-intensive task and subject to inconsistency. Deep learning depicts the most potential to address this inadequacy. Given its ability to learn complex data representations, AI can help in the process of deep learning by detecting undesired variation, such as the inter-reader variability, and can hence be applied to a large variety of clinical conditions and parameters.
- Monitoring and Registration: Monitoring the development of a tumor requires the comparison of numerous images to track progress through image registration. While some change characteristics are directly identifiable by humans, such as moderately large variations in object size shape and cavitations, others are not. These could comprise of subtle variations in consistency and heterogeneity inside the object. Inferior image registration, production of numerous objects and physiological changes over time all contribute to more difficult change analyses. This is where AI helps in enhancing the quality of images for detailed analysis of the segment.
- Image Acquisition: In radiology, the accuracy of medical decision-making depends on the richness of information enclosed in an image. AI technology is balanced to assist address challenges to high-quality image attainment. The first is the difference in imaging protocols and modalities. There is a difference between advancements in image acquisition hardware and image-reconstruction software, a gap that can potentially be addressed by AI methods by suppressing the quantity and improving overall quality.
What does AI-powered imaging workflow look like?
Artificial Intelligence has helped transform the entire radiology and imaging outlook for the healthcare department. It has also brought the element of digitalization in the process which in turn is significantly contributing to better treatment by faster diagnosis and improved efficiency. AI-powered applications have the potential to improve every step of the imaging workflow process. The below mentioned steps is a glimpse into the process of establishing AI powered imaging network.
- Order/Schedule: Establishing AI-powered connection between patients and physicians for systematic workflow is the first step in the process.
- Preparation and Acquisition: AI-powered standardized, accurate patient positioning and planning of the procedure as well as acquiring the necessary equipments is the second step in the process.
- Post Processing/Quantification: AI-powered automatic lesion scoring, and automatic measurements are the post process results which help determine the status of the patient. This is the third step in the process of the imaging workflow.
- Interpretation/Report Generation: AI-powered automatic highlighting, characterization and quantification of anatomies and abnormalities is the most essential part of the process as it helps in identification of the irregularities in the patient. This is the last step of the workflow.
AI Becoming an Indispensable Part of Healthcare
In a study, published in Radiology in 2018, AI was able to detect Alzheimer’s disease in brain scans 6 years prior to diagnosis with 98% accuracy. Radiologists have utilized brain scans to identify Alzheimer’s by looking for lack of glucose levels in the brain. However, as the disease is a slow progressive disorder, the changes in glucose are very faint and complex to spot with the naked eye. Such instances have confirmed the need for AI in the medical imaging segment and have made it an indispensable part of it. Healthcare is one of the major ground-breaking fields in the world and radiology holds enormous potential for new AI-powered solutions. But each improvement is only as good as its implementation into the everyday routine. For healthcare, it means the new solutions require to be incorporated in the medical workflow and be economically viable. To ensure that our solutions integrate seamlessly in the clinical workflow, healthcare professionals need to work closely together with clinical collaboration from the very start of new developments.
Author: Neha Saxena
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