Today, the healthcare sector is transforming rapidly, driven by the need to cater to a growing and ageing population, while focusing on managing the challenges of the rising cost pressure,enhanced quality and time efficiency . The overarching goal of value-based care will be to deliver precise, effective and compassionate care in a cost-efficient and integrated manner.

Newer technologies and better practices are being continuously sought after to make diagnosis and treatment safer, more efficient and cost effective. Radiology is no exception to this trend. The increasing integration of digital technologies in imaging not only opens up new ways of working, but also brings in newer responsibilities. For decades, medical images have been generated and archived in digital form. Now, breakthroughs in computer vision also offer the possibility for their automated interpretation. In the future, radiology will be one of the fields which will be immensely benefitted with digitalisation. Artificial Intelligence (AI)-powered solutions have potential to address the major challenges that the healthcare sector is facing today. Currently, the demand for diagnostic services exceed the supply of experts in the workforce. While this gap is growing rapidly, diagnostics and treatments are also becoming more complex.

Also read: Electronic Health Records: Evolution of Indian healthcare sector

Developing solutions to manage this ever-increasing supply demand gap and complexity is critical to the healthcare sector. Diagnostic experts and radiologists need a new set of tools that can handle large volumes of medical data quickly and accurately. This would enable more objective treatment, based on the quantitative data tailored to the needs of every patient.

Siemens Healthineers has been a pioneer in AI development for more than two decades and the new deep learning technologies enable us to automate complex diagnosis and support optimal treatment.

One such example is the most recent introduction of Siemens Healthineers’ intelligent software assistant for radiology – AI-Rad Companion Chest CT. A software assistant that brings artificial intelligence (AI) to computed tomography (CT) and helps radiologists by speeding up workflow, increasing precision, reducing the time for interpretation and reporting, all this by integrating with the imaging interpretation workflow.

In a nutshell, AI-Rad Companion is a vendor-neutral, multi-organ augmented reading solution that automatically prepares clinical input to be interpreted by radiologists, pathologists and/or clinicians. Through automation, this solution aims to take away the burden of basic, repetitive tasks, so that experienced staff can focus on delivering value based care.

AI-based algorithms could soon establish themselves as virtual “second readers” thereby advancing radiology. With established AI expertise, future-oriented staff, vast medical data sets, and the exceptional computing power needed for creating algorithm-supported healthcare solutions; we are enabling healthcare organizations in their journey towards digital transformation and transforming care delivery.

Key features of AI-Rad Companion Chest CT

• Speeds up work flow – It unlocks the potential to reduce the time of interpretation and reporting through software that automatically performs measurements and prepares results for reports, thereby reducing the prolonged working hour pressure for the radiologists.
• Raises precision- It provides the possibility to increase accuracy in interpretation and reporting through software that automatically highlights abnormalities, characterizes anatomies, and matches results with reference values.
• Reduces time of interpretation and reporting -The AI-Rad Companion Chest CT automatically measures relevant anatomies and abnormalities and transfers results into the reporting environment.
• Integrates with your imaging interpretation workflow- With the AI-Rad Companion Chest CT seamlessly integrating into the department’s workflows and standards, images and supporting information can automatically be made available to any PACS, for individual reporting requirements.

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