Living in the modern era it is likely that you have heard of the recent technological advancements such as Artificial Intelligence (AI).
Although AI has been around for many years, recently it has received enormous attention concerning its uses in real-world scenarios. It has also made its way into the medical field with promising prospects.
A clinical trial
aimed to compare the diagnostic accuracy of chest radiographs interpreted by conventional methods versus AI-assisted interpretation for patients with acute respiratory symptoms in the emergency department.
Artificial intelligence is defined as the simulation of human intelligence processes by machines, especially computer systems. It is the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings i.e. Humans. There has been a growing interest in the use of AI for diagnosing various diseases in human beings. However, accurate clinical data in this field is lacking.
A clinical trial has measured the accuracy of AI in interpreting chest x-rays of patients with respiratory diseases and compared these findings with those derived from conventional practice i.e. Assessment by a radiologist.
The clinical study was a randomized clinical trial, meaning patients were randomly assigned to either the conventional or AI-assisted interpretation group. The clinical study involved 3576 participants. Of these, 1761 patients received AI-assisted chest x-ray interpretation while 1815 patients were diagnosed with conventional means.
The results of the clinical study showed that AI-assisted interpretation of chest radiographs had a similar diagnostic accuracy compared to conventional methods. In other words, the AI system was not better at correctly identifying potential respiratory issues in the chest radiographs of patients.
The clinical trial found that AI-assisted interpretation had a sensitivity of 67.2%, compared to 66% for conventional interpretation. Sensitivity refers to the ability of a test to correctly identify positive cases (in this case, patients with respiratory issues). Additionally, AI had 19.3% false positive cases compared to 18.5% in the conventional group. These results imply that AI-assisted chest x-ray interpretation had no additional benefits when compared with traditional methods.
The results of this clinical study are significant as they suggest that AI-assisted interpretation of chest radiographs provides no additional benefits for emergency departments as its accuracy rate is similar to the physician-interpreted x-rays.
In conclusion, the clinical trial found that AI-assisted interpretation of chest radiographs did not result in higher diagnostic accuracy and efficiency compared to conventional methods for patients with acute respiratory symptoms in the emergency department. These results emphasize that Artificial Intelligence cannot be used as a substitute for human doctors in diagnosing and treating patients.