New Medical Imaging technology in Radiology

Radiology Technology

   For the past few years, Information Technology and digital solutions have exploded within the Radiology industry. It is such a big issue that the Radiological Society of North America (RSNA) has transformed their annual conference to focus on imaging information technology solutions as opposed to only general devices.

    2018 was an enormous year for AI in radiology. The FDA accelerated its clearance process by approving over a dozen medical AI solutions. Tech giants like Philips and GE introduced their own AI technology platforms, promising to address the full process of building, maintaining, deploying and scaling AI solutions.

   In 2018, we discussed how AI was still in its early stages for healthcare provoking fear and a universal hype that, as of now, is starting to fade. Now that the relationship between artificial intelligence (AI) and radiology is more commonly accepted, we find ourselves asking ‘what should we expect? Where are we headed in the near future of 2019 and beyond?

    Since it was first introduced as a concept in the medical profession, artificial intelligence has been eyed with suspicion. Many professionals have been concerned that it will be used to replace their expertise and potentially negatively impact on patient care and results.

  The real value of AI lies in how it are often utilized in collaboration with the radiologist or medical professional. In how it can be used to enhance and support the professional by streamlining process, reducing the diagnosis burden, and improving workflow efficiency. Artificial intelligence in radiology may be a tool, not a sentient being. It is an investment into technology that permits for ongoing improvements to diagnosis and patient care by supporting the radiologist as they battle increasingly weighty workloads.

   Back in 2015, a survey published in Academic Radiology found that radiologists need to review one image every 3-4 seconds to keep up with their workloads. There’s little research highlighting how much heavier these workloads have become over the past five years, but it is very likely to be even more demanding today.

Why Add AI in Radiology ?

     Artificial intelligence in radiology is becoming an increasingly integral part of the profession and daily life. Often described as that extra team member, the one that never sleeps or gets tired and can sift through images without pause, AI adds immense value. It has become slowly embedded into numerous medical institutions globally, filtering into workflows and systems, and being customized to match radiologist requirements. 

    The algorithms have advanced to the purpose where they will absolutely support clinician deciding . Global Diagnostics Australia (GDA), was one of the first diagnostic imaging companies in Australia to include AI as part of its radiology workflow and it achieved notable results. The high-end algorithms were incorporated into the care management pathway to expedite patient diagnosis and treatment across the head, chest and neck. The AI solution was designed to prioritize patients according to their critical status, alerting the radiologist to urgent cases and reshaping their approach to workflow and diagnosis.  

Use of AI in Radiology

    AI has the ability to pick up enough of the image diagnosis weight so that the radiologist can focus on the complex cases that require their specialist attention. It can effectively triage the urgent cases and streamline the process in the face of increased pressure from both the medical facilities and regulatory institutions.

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