The Expanding Role of Artificial Intelligence In Transforming Care Delivery

Artificial Intelligence adds value to the healthcare industry beyond diagnostic accuracy, with better connectivity, transparency and remote diagnosis; thus improving overall patient experience.

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In today’s scenario, there is a considerable rise witnessed in usage of Artificial Intelligence (AI) in our daily lives, right from language recognition on smart phones, humanoid robots to self- driven cars. 

In the coming five to ten years, AI will also transform diagnostic imaging with new methods of machine learning, particularly “deep learning” which is emerging much more powerful and aims to transform data into knowledge for better care. This new deep learning technology would enable us to automate complex diagnostics and support optimal treatment. 

Although intelligent algorithms have been used for quite some time in segments of the imaging field, they have the potential to raise analysis and interpretation of digital medical images to a whole new level compared with the older algorithms. As a result, it paves the way for quantitative, standardized yet also personalized diagnostics, while helping prevent errors in diagnosis. 

Exploring the Clinical Value of AI 

The rising demand for diagnostic imaging makes it imperative for radiologists to be equipped with tools that could help them meet this demand and actively shape the transformation of radiology into a data-driven research discipline. Hence, we created AI algorithms to help speed up clinical workflows and prevent diagnostic errors with reduced missed billing opportunities; enabling sustained productivity increase. 

Siemens Healthineers, has developed a pattern recognition algorithm for its 3D diagnostic software “syngo.via,” which automatically detects anatomical structures, independently numbers vertebrae and ribs, and also aids in precisely overlaying different examination dates along with different modalities. Restricted 

In totality, methods of artificial intelligence could integrate diagnostic radiology even more into outcome-oriented clinical decision-making. 

From Speeding up Workflows to Better Diagnostics and Financial Gains 

In the longer term, particularly in areas like cardiac imaging that is already quantitatively oriented; AI-based image analyses would be along with reproducible characteristic measurements and lab results. These results will not only be analyzed graphically, but will also be annotated textually; favoring the transformation of semi-automated drafting in radiology reports to a data-driven research discipline. 

Moving ahead, implementation of AI could further improvise in identifying high-risk patients, help prevent unnecessary treatments, thus involving diagnostic radiology more closely in outcome oriented clinical decisions. 

AI application also offers fascinating prospects for personalized diagnostics and treatment in healthcare, in turn transforming care delivery and expanding precision medicine. 

Challenges in implementing the AI Transformation 

Firstly, there is a discrepancy between the number of doctors trained in radiology, in comparison to the ever increasing demand for diagnostic services. Currently, the demand for diagnostic imaging outstrips the supply of experts in the workforce evidently, which increases the workload, added with a complex process in managing diagnostics and their treatment. 

Secondly, as we see volatile changes in improving the image resolution of today’s scanners, results in an increase of data. We know for the fact that the estimated overall medical data volume, doubles every three years; making it harder for radiologists to make good use of the available information without additional help from computerized digital processing. 

On the other hand, Diagnostic errors have been an unresolved problem for long now, where studies show that erroneous interpretations occur in about 4% of all radiology diagnoses, with the error rate varying individually and heavily depending on the procedure. For example, In abdominal and pelvic CT scans, it is further well known that radiologists differ not only from one another in image Restricted 

interpretations, but even the very same examiner may come to different conclusions when a reading is repeated. Considering an error analysis, If only images that actually show pathological changes are considered, the error rate rises as high as around 30%. 

Having understood all the challenges, we need to further improvise the power of AI, to provide diagnostic experts and physicians with new set of tools that can handle large volumes of medical data quickly and accurately. This would allow for more objective treatment decisions based on quantitative data and tailored to the needs of every patient. 

It is also desirable, both in radiological research and in clinical diagnostics, to be able to quantitatively analyze this largely unexploited wealth of data and, utilize new measurable imaging biomarkers to assess disease progression and prognosis. 

Artificial intelligence would likely help to overcome all these challenges with algorithms that could prove an indispensable aid for efficient data-based image analysis and serve diagnostic results with minimal errors. 

A Framework for the Future 

As we look forward at the upcoming developments in AI, it will provide physicians with highly accurate tools to detect diseases, stratify risks in an easy-to-understand manner and also optimize patient-specific treatments. 

The technological advantages of artificial intelligence need to be backed with manifold learning parameters and extensive training data which could enable actual development of “intelligence”. Siemens Healthineers, for example has invested in a dedicated advanced reading and annotation team, building a database which now contains more than 100 million curated images, reports, and clinical and operational data which are fed into, and used to train, algorithms. 

AI is strongly seen promising higher automation, productivity and standardization, along with unprecedented use of quantitative data; beyond the limits of human cognition in medical imaging. Here on, it is clear that the implementation of AI in practice will require interdisciplinary collaboration in which radiology experts have a significant role to play.

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