Why ‘Big Data’ Is Inevitable in Healthcare
Artificial intelligence (AI) has been transforming health systems. The present-day AI landscape has been merged with deep-learning technology and “big data” to increase predictability and precision. Any AI advancement relating to healthcare can greatly revolutionize the functionality and productivity of the system.1 Deep learning has revamped medical imaging applications, including image visualization and quantification, data assemblage, data summarization, and predictive analyses. Both the front and back ends of today’s healthcare operations have been greatly impacted by the advent of deep learning and big data analytics.
AI-Based Diagnostic Algorithms
The pressing data load and demanding timelines have impelled the advent of AI into the medical domain. The increasing flow of assorted data into the system, through enterprise imaging and multiple information systems, calls for standardized methodologies for big data.
Many practical challenges are encountered by bio-imaging clinicians when handling large quantities of imaging data. Because the proportion of imaging data load to trained readers (including radiologists and imaging technicians) is highly disparate, the workload of readers has grown substantially. As the data load continues to mount, conventional methods of analyzing and storing data need to be updated with effective systems coupled with AI or big data. Algorithms based on deep learning have been developed, which can read massive datasets of radiology images and interpret them with high precision and diagnostic accuracy. These specialized algorithms are “technologically trained” to accurately detect even minor abnormalities and rare complications. This allows radiologists to read the images more quickly, in turn permitting more quality time to be spent on patient care and counseling.2
Data Storage: Challenges and Solutions
Fluoroscopy and radiology are densely laden with giant-sized chunks of data that require robust technology landscapes and data archives to store and manage a trillion megabytes of patient records. Storing the massive-sized data records was an arduous undertaking in the past. But presently, big data has transformed the entire system, practice, and operations – through smart integration of healthcare information.3 AI-assisted storage methods have evolved to effectively store data and save data space for tasks such as intelligent file virtualization (allowing the grouping of multiple storage devices into a logical file mount); this helps to transition data without warping its content or distorting its application access. Moreover, these AI applications are intended to increase data usability, decrease backup durations, and reduce storage costs.4
Patient and Physician Safety
The evolution of medical imaging technology has led to a diverse variety and large quantity of computed tomography scans and complex fluoroscopy techniques, resulting in higher exposure to active radiation. However, AI-assisted dose-reduction programs can aid in reducing radiation exposure, for patients as well as radiologists. For example, the automated patient-positioning technique can decrease radiation exposure and produce quality imaging of high reproducibility. AI-generated contouring technology can define ideal patient position to optimize imaging geometry with the lowest level of radiation. Likewise, dose-reduction strategic plans accurately calculate the accumulated dose radiation in relation to the threshold dose during angiographic diagnosis. AI-assisted automated software helps in (1) determining personalized dose ranges after each radiation intervention and (2) comparing these data with reference levels to help patients check their level of radiation exposure and select further treatment accordingly. Similarly, platforms that monitor patients’ radiation levels help physicians and imaging technicians to track their own exposure levels by providing this information in real time.5
Maximizing the power of AI promises to be challenging yet rewarding. Health data will continue piling up, causing vast data traffic and, consequently, potential disruption of current systems. Data explosion from many sources, including hospital management electronic medical records, health insurance records, personal patient health trackers, and lab results will intensify the maze of data, some of which will be irrelevant and could pose the risk of data misinterpretation and eventually substandard diagnosis and treatment. The conventional practices of yester years will not work for today – and definitely not for the future – because of the dramatic effects of big data and AI on systems and processes. On the upside, AI and deep learning technologies will provide automated solutions for storing and analyzing healthcare data6 that may offset the potential disadvantages of data explosion. The horizons of AI in fluoroscopy are expanding vividly and widely as innovation continues to nudge the potentialities of AI and deep learning.
- Wilson A. Using big data and ai to improve imaging workflows and the revenue cycle. Radiology Business. https://www.itnonline.com/article/using-big-data-and-ai-improve-imaging-workflows-and-revenue-cycle. Accessed November 27, 2019.
- Why AI Is the Solution to Radiology Data Problem. Health Analytics. https://healthitanalytics.com/news/why-ai-is-the-solution-to-radiology-data-problem. Accesses November 27, 2019.
- Kansagra AP, John-Paul JY, Chatterjee AR, et al. Big data and the future of radiology informatics. Acad Radiol. 2016;23(1):30– Accessed October 17, 2019.
- Shen R. Enabling Flexibility with Intelligent File Virtualization. White Paper. 2011. https://www.f5.com/pdf/white-papers/intelligent-file-virtualization-wp.pdf. Accessed November 20, 2017.
- Seibert JA. Flat-panel detectors: how much better are they? Pediatr Radiol. 2006;36(2):173–181. Accessed October 19, 2019.
- Freiherr G. Ask what AI can do for you and your patient. https://www.itnonline.com/content/blogs/greg-freiherr-industry-consultant/ask-what-ai-can-do-you-%E2%80%94-and-your-patient. Accessed October 18, 2019.