While millions of diagnostic examinations are carried out annually, chest X-rays play a vital role in diagnosing several diseases. But the usefulness of the same can be limited due to the challenges in interpretation that need thorough and rapid evaluation of 2D image depicting complex, 3D organs, and disease processes. Sometimes, major details can be missed by chest X-rays resulting in adverse outcomes for patients.
Recent efforts have improved lung cancer detection in radiology, differential diagnosis in dermatology, and prostate cancer grading in pathology. And, obtaining accurate clinical labels for the deep learning models for X-ray interpretation.