Artificial Intelligence all set to Virtualize Radiology Brains

Artificial Intelligence all set to Virtualize Radiology Brains

Mr. Prashant Akhawat’s interview with Live Mint

  • AI and automation will not replace radiologists, but will act as an assistant to radiologist in every step of the imaging detection, diagnosis and prognosis
  • There is an increase in interest and enthusiasm for AI among the radiology community as the discussion is moving upwards on from considering AI as a threat

Accelerating with an exponential growth, artificial intelligence (AI) is all set to move from experimental stages to live industry implementations and all is set to mark its presence across all industry verticals. AI is all about virtualizing human cognitive functions in the form of software brains. For organizations, harnessing AI is not optional, albeit it is critical to stay competitive. Gartner in its recent study (2018), predicts the business value derived from AI to reach $3.9 trillion by 2022. With the disruptive potential, the investments in AI are ever-increasing. It is redefining industries with automation processes, and personalization. The healthcare industry has been one of the foremost adopters of the AI amongst all others. The advancement, the healthcare industry is reaping with the use of AI to maintain medical records, do mundane tasks more accurately and faster, to design care pathways, digital consultations, medication management, drug creation, image analytics, bigdata analytics, medical robotics, health monitoring and host of other things.

Powering Radiology with AI

Today, in India, there is a growing demand for improved medical care. We see an increase in insurance penetration, the rise in chronic disease and an aging population — all this demand for better imaging diagnoses and treatment. Radiology industry is facing its own set of ongoing challenges – the shortage of radiologists topping the list. The radiology industry is a functional domain for automation by virtualizing domain intelligence into an intelligent software. The quantum leap in medical imaging technology has led to exponential growth of medical imaging data stored digitally. Deep Learning algorithms and Image Analytics can help in improving medical diagnosis and aid radiologists with better reporting efficiencies.

Nowadays, with the growing incidence of lifestyle diseases, the requirement of frequent imaging and getting multiple scans at a higher resolution has increased. There are scenarios where the hospitals have X-Rays, MG, CT and MRI equipment’s but sans radiologist to read a report. To bridge the gap where AI can play a pivotal role by performing specific tasks such as image recognition – nodule detection, hemorrhagic or ischemic stroke detection, fracture detection, breast cancer analysis with prior case analysis and other narrow tasks requisite identify potential findings in medical images, which is one set of tasks performed by radiologists. This gives a window to the radiologists to focus more on image-guided medical interventions, defining clinical parameters of imaging examinations, relating findings from images with medical records and test reports, consult physicians for treatment based on the diagnosis, discussing procedures and results with patients. Artificial Intelligence is taking over image reading and interpretation so that radiologists can read more images in a short span with better accuracy as the number of images has increased more in the last decade than the number of radiologists.

Radiologists need to adapt themselves to new skill sets and technologies for attaining better productivity by integrating AI with radiology practice. AI and automation will not replace radiologists, but will act as an assistant to radiologist in every step of the imaging detection, diagnosis and prognosis and will also help in prior analysis and comparison. AI Algorithms will have a stronghold in medical imaging and will become an integral part of RIS-PACS, as frequently we hear some or the other algorithm is developed to detect tumors, lesions, fractures and host of other things.

With the help of RIS-PACS workflow enabled with AI, a radiologist sitting at any location can do the reads for any healthcare center located remotely, which addresses the accessibility challenges, subspecialty reads, point in time care with reduced costs. Further helping them to optimize results without compromising on the accuracy of diagnosis. For emergency reporting (trauma and stroke cases) AI can assist the radiologist with a preliminary report with which they can conclude with their final interpretation of the case within the stipulated time. AI Enabled RIS-PACS can prioritize stroke or stat cases and reduces turnaround time in turn will improve patient care. The algorithms are trained to assist the radiologists with various detections, diagnosis, staging, sub classification of different medical conditions. Hence, deep learning is a shoulder to the increasing workload in radiology.

Global Market for AI

Artificial intelligence’s complexity is not a deterrent to its adoption instead it is as disruptive as the internet was. The future of investment is AI, and it has not just been observed, but also proved that AI helps in improving the radiologist’s productivity, efficiency and quality diagnosis, hence this would help them reduce costs and increase return on investments. In the healthcare industry, AI is rapidly rising in the medical imaging domain. Globally, the AI market in medical imaging is forecasted topping US$ 2 billion by 2023. Notably, there is an increase in interest and enthusiasm for AI among the radiology community as the discussion is moving upwards on from considering AI as a threat. Also, clinical applications have shown improved clinical results with the use of AI.

All the research trends underline how AI is revolutionizing radiology in the long run. AI-based companies have learned to warm-up radiologists. Realizing the technological potential of AI, radiology practitioners are partnering with AI ventures to have a seamless RIS-PACS workflow. AI is going to augment the way care is provided by healthcare practitioners.


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