How will AI get to the reading room?
Radiology can only continue to have a critical role in the screening, diagnosis, and treatment of disease. While the field has undergone important changes to digitize the process, the fundamentals are still the same. A case is read by a radiologist based on (digital) imaging data and other information. The end product is a (digital) report, which is shared with the referring clinician and stored. The images are also (digitally) stored. Advances in technology have enabled improvements in image quality, scan time, data sharing and storage. But there has been no drastic shifts in the underlying paradigm.
The current software delivery model in healthcare … simply will not serve current and future advances in medical image analysis.
Many say this is about to change. Digital imaging data is a veritable treasure trove for the quickly developing field of computer vision, led by recent advances in machine learning. It is a data-rich opportunity to develop and apply new algorithms with a compelling value proposition. Therein lies a true opportunity to increase accuracy, save lives, time, and of course, money. While some would suggest that there is an aggressive charge to replace radiologists, I would agree with those who suggest the pace will be more timid and with the result being a collaborative effort between man and machine. But perhaps not for the same reasons. There is a strong focus on the performance, accuracy and clinical relevance, as there should be. But my concerns are with respect to the current software delivery model in healthcare: it simply will not serve current and future advances in medical image analysis.
Fields which have been revolutionized by software have done so by enabling a tremendous pace of change. While the rate of innovation in medical image analysis has accelerated due to advances and access to software, the current paths to market for new software technologies limit adoption. Existing vendors with comparatively deep market reach have slow development cycles that match the long procurement cycles, while simultaneously locking technologies within proprietary frameworks.
Meanwhile, independent startups are able to focus on their technology and move much more quickly. However, the resulting implementation usually takes the form of a standalone application or web portal. These solutions are not typically integrated into current workflows of time-pressed clinicians and their installation may be met with resistance by those responsible for the IT infrastructure. Therefore, spreading the solution to many hospitals is difficult and clinical adoption is once again slowed.
These medical image analysis technologies, often simply referred to as algorithms, are not very broad in nature, but typically developed for very specific use cases. There are already dozens of applications in the works and we may see hundreds become available in the coming years as competing algorithms vie for the same clinical applications. Within the current framework, the majority would end up housed in silos, like much of medical data. This would restrict clinical flexibility and institutional mobility. And what about transparency? Will the field be able to compare proprietary algorithms to each other, including revised versions or those which continuously update themselves?
…the anticipated marriage of AI and radiology is unprecedented in medicine.
Currently, a common method to gain clinical adoption is for new entrants to partner with incumbents. While an improvement, given the above, we can see this as a sub-optimal solution. We have to remember that machine learning and computer vision all originated well outside the field of medicine. While applications from computer science (and many other fields!) have been improving healthcare for decades, the anticipated marriage of AI and radiology is unprecedented in medicine. Even large tech companies that had to date stayed at the periphery of healthcare are now joining the charge. At the other end of the spectrum, university researchers without access to direct clinical collaborators are still able to push the field forward with their contributions. Large open data sets will further this trend by removing one of the major barriers to the development of algorithms.
Based on all these reasons, we see that the current innovation-to-adoption framework for software in healthcare will not serve the influx of AI-enabled technologies. Between the proprietary frameworks of the incumbents and the immense hurdles faced by new entrants, we need to think about how we can remove these barriers to progress.
The introduction of AI-enabled technologies in radiology will result in new challenges and opportunities in the interpretation of medical images. It may appear as a primarily logistical challenge at first glance. But, once we delve into some specific aspects, we reveal implications which suggest that an entirely new delivery model must be conceptualized and built. And this can only be achieved with the participation of all stakeholders and the benefit of our patient population in mind.
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