What is autonomous artificial intelligence, and is radiology ready for this new technology? In this paper, we explore one of the most exciting autonomous AI applications, ChestLink from Oxipit.
What is Autonomous AI?
Up to now, most interpretive AI solutions have focused on assisting radiologists with analyzing medical images. In this scenario, AI provides suggestions to radiologists and alerts them to suspicious areas, but the final diagnosis is the physician’s responsibility.
Autonomous AI flips the script by having AI run independently of the radiologist, such as by analyzing a large batch of chest X-ray exams for tuberculosis to screen out those certain to be normal. This can significantly reduce the primary care workload, where healthcare providers who offer preventive health checkups may see up to 80% of chest X-rays with no abnormalities.
Autonomous AI frees the radiologist to focus on cases with suspicious pathology – with the potential of delivering a more accurate diagnosis to patients in real need.
One of the first of this new breed of autonomous AI is ChestLink from Oxipit. The solution received the CE Mark in March 2022, and more than a year later it is still the only AI application capable of autonomous performance.
How ChestLink Works
ChestLink produces final chest X-ray reports on healthy patients with no involvement from human radiologists. The application only reports autonomously on chest X-ray studies where it is highly confident that the image does not include abnormalities. These studies are automatically removed from the reporting workflow.
ChestLink enables radiologists to report on studies most likely to have abnormalities. In current clinical deployments, ChestLink automates 10-30% of all chest X-ray workflow. The exact percentage depends on the type of medical institution, with primary care facilities having the most potential for automation.
ChestLink Clinical Validation
ChestLink was trained on a dataset with over 500k images. In clinical validation studies, ChestLink consistently performed at 99%+ sensitivity.
A recent study published in Radiology highlighted the sensitivity of the application.
“The most surprising finding was just how sensitive this AI tool was for all kinds of chest disease. In fact, we could not find a single chest X-ray in our database where the algorithm made a major mistake. Furthermore, the AI tool had a sensitivity overall better than the clinical board-certified radiologists,” said study co-author Louis Lind Plesner, MD, from the Department of Radiology at the Herlev and Gentofte Hospital in Copenhagen, Denmark.
In this study ChestLink autonomously reported on 28% of all normal studies.
In another study at the Oulu University Hospital in Finland, researchers concluded that AI could reliably remove 36.4% of normal chest X-rays from the reporting workflow with a minimal number of false negatives, leading to effectively no compromise on patient safety.
Safe Path to AI Autonomy
Oxipit ChestLink is currently used in healthcare facilities in the Netherlands, Finland, Lithuania, and other European countries, and is in the trial phase for deployment in one of the leading hospitals in England.
ChestLink follows a three-stage framework for clinical deployment.
- Retrospective analysis. ChestLink analyzes a couple of years worth (100k+) of historic chest x-ray studies at the medical institution. In this analysis the product is validated on real-world data. It also realistically estimates what fraction of reporting scope can be automated.
- Semi-autonomous operations. The application moves into prospective settings, analyzing images in near-real time. ChestLink produces preliminary reports for healthy patients, which may then be approved by a certified clinician.
- Autonomous operations. The application autonomously reports on high-confidence healthy patient studies. The application performance is monitored in real-time with analytical tools.
Are We There Yet?
ChestLink aims to address the shortage of clinical radiologists worldwide, which has led to a substantial decline in care quality.
In the UK, the NHS currently faces a massive 33% shortfall in its radiology workforce. Nearly 71% of clinical directors of UK radiology departments feel that they do not have a sufficient number of radiologists to deliver safe and effective patient care.
ChestLink offers a safe pathway into autonomous operations by automating a significant and somewhat mundane portion of radiologist workflow without any negative effects for patient care.
So should we embrace autonomous AI? The real question should be, can we afford not to?
