The Fleischner Society, a multidisciplinary alliance for thoracic radiology, just held its 53rd annual meeting in Amsterdam, with the application of artificial intelligence (AI), topping workflows, diagnosis, and treatment discussions.
The Fleischner Society's goal to foster all aspects of teaching and research into chest radiology is reflected in their annual meetings. Their membership, representing a cross-section of disciplines worldwide, comes together at these meetings to delve into the latest science and practices in the field.
Sessions like Dr. Alexander Bankier's Application of Artificial Intelligence further members' aim to advance the study of chest diseases by using such technology to advance thoracic imaging.
Arterys was in attendance at this year's event, answering questions about our Lung AI's proven useful to detect nodules and pulmonary opacities, smart workflow prioritization, and scoring with Fleischner and LungRads recommendations.
The academic and clinical applications of artificial intelligence (AI) are vastly increasing the capabilities of and benefits to physicians.
AI highlights, as covered in the annual meeting, include:
Current AI tools offer solutions to the barriers to delivering the standardization of care, such as disparate clinical workflows and lack of adherence to standard-of-care guidelines.
Conversations at the Amsterdam meeting around this included:
Incorporating AI and deep learning technology (such as Lung AI) increases inter-radiologist agreement for the presence or absence of nodules. Leveraging AI and deep learning in medical imaging analysis and treatment decisions helps with adherence to the standardization of care that is often challenging because clinicians interpret patient data over time and across institutions.
Leveraging AI in lung/Chest CT imaging provides hospitals globally with an incredible opportunity to further streamline processes and workflows like never before. By incorporating practices driven by analytics and emerging tech, healthcare systems can meet the goal of delivering consistent, quality care.
One simple but critical benefit of AI is the ability to automate mundane, repetitive tasks in the workflow process. This not only saves time and cost, and dramatically increases capacity to help reduce physician burnout that can arise from repetitive tasks, end-to-end processes, and complex workflows.
A significant industry challenge results from how radiology practices are more distributed than ever. How Cloud AI technology can be applied to help include:
Not only are practices distributed, but hospitals are also currently consolidating into larger systems involving numerous locations. By pooling resources, researchers and experts from leading facilities can significantly improve patient outcomes as each location may operate according to different financial, operational, or clinical standards.
Not pooling resources jeopardizes everything from treatment time to standardization of care, threatening the original intent to benefit the patient. The stakes are even higher during critical care situations, where literal minutes can make the difference.
The growing democratization of healthcare is pushing providers to deliver convenient, patient-centered solutions that intentionally and effectively address patient needs.
Cloud technology provides access to subspecialists no matter the location. With the flexibility of a radiology cloud solution, patients can access high-quality care even in the most remote areas.
AI solutions and automating tasks offer hospitals and clinics a lifeline on radiologist burnout. How AI can help includes:
Without AI and deep learning, physicians experience productivity challenges and burnout from the large amounts of non-intellectual work, tedium, and monotony in their day-to-day. "Lots of mouse clicks," manual measurement calculation, and fumbling across various applications can now be an aspect of radiology work set far in the past.
Physician shortages and rising imaging volume, and study sizes are real problems. Part of the images per study increase is due to the more complex imaging we have now and postprocessing. AI technology is opening a path to sustainable cancer screening and more.
Some AI and deep learning increases productivity and decreases reporting time by incorporating AI across the Lung CT workflow. Lung AI, for an example, automatically analyzes CT scans, providing physicians with automated measured segmentations, longitudinal tracking, and customizable reporting. This all reduces tedious and operator-dependent manual tasks and produces an average time saving of 44% per study.
How does AI specifically benefit patients? Points raised at the meeting include:
Leveraging AI and deep learning in chest/lung CT medical imaging provides tools that can rapidly and accurately provide physicians with better data and intel allows for better decision making and, ultimately, leads to better patient outcomes.
An example is with Arterys' Lung AI, which helps physicians quickly analyze lung nodules by automatically processing Lung CT scans, delivering clear, easy-to-interpret customizable reporting that helps physicians reduce missed nodules detections by up to 70% -- facilitating more consistent and accurate diagnoses.
Finding the right AI radiology vendor can help your practice increase diagnostic efficiency and improve the overall effectiveness of healthcare delivery. We developed the Arterys platform to ensure our AI solutions are clinically useful. We provide the most effective clinical workflow available through a single integration point. Let us show you how!
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