What to Look For in a Radiology AI Platform
With increased global accessibility, artificial intelligence (AI) is rapidly becoming a part of long-term transformation plans within healthcare. However, AI still has only just begun penetrating into radiology clinical practice. Integrating the right AI into your radiology workflow greatly enhances the speed and efficiency of clinical decision-making, improving the standard of patient care. Here we touch on some of the questions many have when selecting an AI vendor to improve radiology workflows.
Why are radiologists hesitant to integrate AI into their practices?
Practices cite several concerns behind their hesitance to migrate to AI-powered radiology solutions:
- Fear of AI replacing radiologist expertise
- Difficulty integrating AI solutions into existing applications
- Limited understanding of the role AI can play in supporting radiology workflows
- Bias concerns for AI
- Uncertain clinical or business gain
- Lack of IT personnel/infrastructure to support AI
Despite these concerns, the radiology practice is transforming to be more data driven and more productive, and AI is playing a major role in that.
What should you look for in a radiology AI vendor?
When deciding which AI vendor best suits the needs of your practice, it helps to look for products that streamline clinical decision-making and improve the overall efficiency of healthcare delivery. Here are some of the essential considerations:
Minimal disruption to existing workflow
Integrating AI into a radiology workflow should not complicate things by adding more steps. Rather, AI integration should simplify a radiologist’s workflow to increase productivity.
You should seek radiology AI vendors who provide tools that seamlessly integrate into existing workflows, complementing radiologist expertise by standardizing medical image analysis and interpretation for faster diagnostic decision-making. An AI medical imaging platform should be able to integrate into your entire radiology IT ecosystem, across PACS, EMR, RIS or Dictation systems.
Clinical usefulness to radiologists
For an AI-powered radiology tool to be effective, it should enable faster data analysis and report generation as a minimum without creating extra burdens on already overburdened radiologists. Your preferred AI vendor's solution should extract actionable insights from scanned patient images fast and efficiently, minimizing the existing bottlenecks to existing workloads.
Faster clinical decision-making is critical to enhance your efficiency with heavy patient loads. Ideally, the workflow and outputs of AI should match the clinical needs of your radiology practice. Many AI models are constructed in academic environments or by engineers that do not fully understand the needs of existing clinical practice. Although the models seem novel, they are not clinically useful or relevant. Development of AI/machine learning algorithms should be driven by feedback from industry experts within radiology practice and healthcare as a whole.
Leading AI vendors automate data generation to support your diagnostic decisions and embed into your existing workflow. It is also required that the algorithms driving the automated data generation are approved by appropriate regulatory bodies - FDA, CE Marked, Health Canada, etc.
Increased productivity for radiologists
Your radiology AI vendor should also provide a solution that helps you complete a higher volume of work efficiently. A robust AI radiology tool should reduce or eliminate (where possible) the manual, repetitive tasks that often contribute to radiologist burnout. For example, nuclear cardiology scans currently require visual scoring of perfusion. However, visually scoring is time-consuming and subject to intra- and inter-observer variability, even when performed by experienced radiologists.
Automation of image analysis and interpretation can help radiologists report scans faster, consistently, and more effectively. When incorporated across the cardiac MR workflow, Arterys’ Cardio AI provides radiologists with fast access to automated, quantitative cardiac MR image analysis. Combined with customizable reporting, Cardio AI significantly reduces the tedium of operator-dependent manual tasks and saves up to 25 minutes per study.
The efficiency of automated analysis also extends to Lung CT workflows, where our Lung AI product automatically analyzes CT scans and takes the tedium of operator-dependent tracking, measurement, and reporting away from radiologists. On average, the Lung AI saves 44% of time spent on each study. Similarly, Breast AI identifies potentially malignant lesions in digital breast tomosynthesis exams, assisting radiologists in prioritizing caseload, making clinical decisions, and reducing time spent reading images by up to 52.7%.
Performance and consistency
An AI radiology vendor should improve physician experience, the accuracy of diagnosis and treatment, operational efficiency, and outcomes that matter to patients and providers. Strong performance is critical for an AI product to optimize radiologist productivity, especially when the product loads images fast and provides analysis support. The AI product should also be engineered to accelerate the performance of tasks such as:
- 3D file rendering
- Loading large multidimensional data
- Running multiple AI applications simultaneously
- Pre-fetch and image caching
- Aggregating AI results across multiple AI models and vendors into a single report
A combination of the above features will help radiologists at your practice read and analyze images faster when integrated in a browser-based zero footprint viewer.
Arterys’ Breast A product supports clinical decision-making and estimates short-term, breast cancer risk based on 2D or 3D mammograms, with the highest AUC available (AUC = 0.83)*. Compared to other risk assessment models, our product offers greater accuracy.
Our Cardio AI product also provides automated, editable ventricular segmentations with comparable accuracy to those performed manually by expert radiologists.
For a product to be used in clinical practice, it must generally receive clearance from a country specific regulatory body. Here are some examples of regulatory bodies:
- Food and Drug Administration (FDA, U.S.)
- CE Mark (Europe)
- Pharmaceuticals and Medical Devices Agency (PMDA, Japan)
- Ministry of Food and Drug Safety (MFDS, South Korea)
Currently, only 57 vendors have received regulatory clearance for 77 machine learning algorithms for medical imaging. Our products are regulatory cleared in over 100 countries worldwide, including countries that do not regulate software as a medical device. Some of the products that have received Class II 510(k) clearance include:
- 4D Flow Visualization and Quantification
- Cardiac MRI Deep Learning
- Oncology AI – Visualization and Quantification with ML Segmentation
How can Arterys help?
Finding the right AI radiology vendor can help your practice increase diagnostic efficiency and improve the overall effectiveness of healthcare delivery. A team of physicians developed 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!
*2. iCAD data on file. FDA filing: K203822. Standalone performance varies by vendor. FDA Cleared and CE Mark Pending.