Radiologists outperformed AI in accurately identifying the presence and absence of three common lung diseases in a study of more than 2,000 chest X-rays.
The research conducted in four hospitals in Copenhagen, Denmark, in 2020 was published in Radiology, a journal of the Radiological Society of North America (RSNA).
Scientists compared the performance of four commercially available AI tools with a pool of 72 radiologists in interpreting 2,040 consecutive adult chest X-rays taken over a two-year period.
“Chest radiography is a common diagnostic tool, but significant training and experience is required to interpret exams correctly,” said lead researcher Louis L Plesner, MD.
The resident radiologist and PhD fellow in the Department of Radiology at Herlev and Gentofte Hospital explained that clinical use of deep-learning-based AI tools for radiological diagnosis is in its infancy.
“While AI tools are increasingly being approved for use in radiological departments, there is an unmet need to further test them in real-life clinical scenarios,” he said.
“AI tools can assist radiologists in interpreting chest X-rays, but their real-life diagnostic accuracy remains unclear.”
High sensitivity
During the research, the chest X-rays were assessed for three common findings: airspace disease, pneumothorax or collapsed lung, and pleural effusion or a buildup of water around the lungs.
The median age of the patient group in the study was 72 years. Of the sample chest X-rays, 669 (32.8%) had at least one target finding.
AI tools achieved sensitivity rates ranging from 72 to 91% for airspace disease, 63 to 90% for pneumothorax, and 62 to 95% for pleural effusion.
“The AI tools showed moderate to a high sensitivity comparable to radiologists for detecting airspace disease, pneumothorax and pleural effusion on chest X-rays,” Dr Plesner said.
“However, they produced more false-positive results (predicting disease when none was present) than the radiologists, and their performance decreased when multiple findings were present and for smaller targets.”
For pneumothorax, positive predictive values – the probability that patients with a positive screening test have the disease – for the AI systems ranged between 56 and 86%, compared to 96% for the radiologists.
“AI performed worst at identifying airspace disease, with positive predictive values ranging between 40 and 50%,” Dr Plesner said.
“In this difficult and elderly patient sample, the AI predicted airspace disease where none was present five to six out of 10 times. You cannot have an AI system working on its own at that rate.”
A complex process
According to Dr Plesner, the goal of radiologists is to balance the ability of finding and excluding disease, avoiding both significant overlooked diseases and over-diagnosis.
“AI systems seem very good at finding disease, but they aren’t as good as radiologists at identifying the absence of disease especially when the chest X-rays are complex,” he said.
“Too many false-positive diagnoses would result in unnecessary imaging, radiation exposure and increased costs.”
Most studies evaluate the ability of AI to determine the presence or absence of a single disease, which is a much easier task than real-life scenarios where patients often have multiple diseases, Dr Plesner said.
“In many prior studies claiming AI superiority over radiologists, the radiologists reviewed only the image without access to the patient’s clinical history and previous imaging studies,.
“In everyday practice, a radiologist’s interpretation of an imaging exam is a synthesis of these three data points.
“We speculate that the next generation of AI tools could become significantly more powerful if capable of this synthesis as well, but no such systems exist yet.”
He said the new study demonstrates that radiologists generally outperform AI in real-life scenarios where there is a wide variety of patients.
“While an AI system is effective at identifying normal chest X-rays, AI should not be autonomous for making diagnoses,” the researcher concluded.
