There are clearly many use cases for image-based AI in pathology. The ability of the pathology image management system to handle integration with the image-based AI tools is critical for successfully implementing AI applications in a clinical setting. Users should be able to access the tools without needing to log in to different systems, and the AI results should be stored and accessible within the image management system for a consolidated patient view. Depending on the results, worklists can be populated, and cases prioritized to orchestrate the workflow accordingly. Only then can image-based AI be part of the daily workflow.
An image management system must be designed to handle the large number of integrations needed with open interfaces (APIs) and the use of image standards. Sectra’s digital pathology solutions have this integration potential; users can run either certified or research applications depending on need. This applies not only to applications that are available now, but also to future developments in the field.
Standards throughout the workflow, from scanner to pathologist, are vital for ensuring interoperability. Scanning images in DICOM ensure that images can be read and interpreted by the desired applications now and in the future.
At the time of writing, there are CE-labeled image-based AI applications from five vendors in the global pathology market. Moreover, there are proprietary image analysis from scanners vendors, research products and home-built applications. My prediction is that more analyses will come, from both existing vendors and from new ones. Self-validation is a time-consuming task that requires a great deal of energy, dedication, and knowledge, which some institutions with in-house capabilities can manage. For most laboratories, however, selecting applications that have put in the required effort to ensure performance and quality and to guarantee future maintenance is crucial. The number of image-based AI applications that will be required has yet to be determined, but it is certain to be large. Use cases vary depending on different organ types, stainings, diseases and treatments and so forth. Selecting an open-format image management system may help you successfully implement all the desired image-based AI applications.
To reap the full benefits of digital pathology and adopt AI in the most efficient way, having the digital pathology infrastructure in place with a digital pathology image management system that is AI-ready and capable of digital workflow management is crucial. The image management system needs to be well integrated with scanners and LIS to enable such a workflow.
Once the infrastructure is in place and everyday reading has been digitized, you will be well equipped to take the next step. Only then will your lab leverage all the new technology and be able to deliver greater accuracy and efficiency, which will ultimately both improve the work situation for many pathologists and be of benefit for patient care.