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AI and personalized medicine to raise the bar in radiology reporting

From the January/February issue of DOTmed HealthCare Business News magazine

By Dr. Jamshid (Jim) Tehranzadeh and Nasser Hiekali

Radiology reporting is becoming more specialized. As a large group with more than 30 radiologists, physicians who use our services expect imaging exams to be read by subspecialists who are equipped with the additional knowledge and experience to enhance the quality of both the diagnosis and the radiology report.

At the same time, we believe that artificial intelligence (AI) and deep learning are vital tools that can be used to help make clinical decisions.
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For example, AI can expedite a comparative analysis of current plaques and other abnormalities with previous studies. AI can count the number of plaques, measure the size of each plaque and provide an analysis of its growth or reduction much faster than a radiologist. However it is the radiologist who determines the significance of the findings and makes the diagnosis of a patient’s condition.

AI also plays an important role by analyzing the increase or decrease of cancerous tumors and provides radiologists with the data needed to deliver an overall diagnosis that quantifies the progress or regression for each patient.

Improvement in any field requires research and development. AI offers an important tool that aids in text analytics and changes millions of bits of unstructured raw data into meaningful and helpful results and conclusions that can lead decision-makers to success in many endeavors.

AI technology also offers the ability to reduce costs, improve operational efficiency and accelerate productivity, despite an aging population. And governments or insurance agencies can use this data as a tool to drive programs that enhance population health and increase efficacy of diagnosis and treatment.

Dose tracking is another important element in population health. The Veterans Administration is now tracking radiation dose for each individual veteran during his or her lifetime to discover the cumulative effect of radiation and define the limits that should be imposed to avoid overexposure. This addresses the need to measure and identify the value and risk of radiation-based procedures.

Ambitious strides in technology
These ambitious strides in technology have advanced the practice of radiology. Deep learning is an enhancement and not a threat. We view this much like the promise that computers would reduce the use of paper. Today we are using computers to generate more paper than ever before.
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