Abstract:
Chest X-rays are one of the most frequent imaging modalities and are consistently in
demand within the field of radiology. With the rise of innovation in AI, this study
evaluated the machine learning tool's performance as a clinical decision support tool to
help reduce the radiologists' workload and improve clinical workflow. The machine
learning focused on diagnosing the diseases cardiomegaly, pneumonia, and mass. A total
of 56 chest X-ray radiographs were analyzed by the machine learning tool and were
compared with those diagnoses made by a radiologist as the diagnostic gold standard.
Additionally, the acceptability of the tool was evaluated using a Likert scale focusing on
its functionality, reliability, usability, efficiency, and security. The results showed that the
MLT demonstrated a good performance in detecting pneumonia, but had poor accuracy in
detecting cardiomegaly and mass cases. Additionally, the acceptability survey tool
showed an overall neutral rating from the radiologist. While the machine learning tool
shows potential as a support, it is still unreliable and inaccurate for clinical use. This
suggests the need for further improvement of the MLT in its algorithm design and
training process to enhance its diagnostic accuracy and reliability.
