Is Artificial Intelligence Replacing Our Radiology Stars? Not Yet!

Abstract

Artificial intelligence (AI) is here to stay and will change health care as we know it. The availability of big data and the increasing numbers of AI algorithms approved by the US Food and Drug Administration together will help in improving the quality of care for patients and in overcoming human fatigue barriers. In oncology practice, patients and providers rely on the interpretation of radiologists when making clinical decisions; however, there is considerable variability among readers, and in particular for prostate imaging. AI represents an emerging solution to this problem, for which it can provide a much-needed form of standardization. The diagnostic performance of AI alone in comparison to a combination of an AI framework and radiologist assessment for evaluation of prostate imaging has yet to be explored. Here, we compare the performance of radiologists alone versus a combination of radiologists aided by a modern computer-aided diagnosis (CAD) AI system. We show that the radiologist-CAD combination demonstrates superior sensitivity and specificity in comparison to both radiologists alone and AI alone. Our findings demonstrate that a radiologist + AI combination could perform best for detection of prostate cancer lesions. A hybrid technology-human system could leverage the benefits of AI in improving radiologist performance while also reducing physician workload, minimizing burnout, and enhancing the quality of patient care.

Patient summary: Our report demonstrates the potential of artificial intelligence (AI) for improving the interpretation of prostate scans. A combination of AI and evaluation by a radiologist has the best performance in determining the severity of prostate cancer. A hybrid system that uses both AI and radiologists could maximize the quality of care for patients while reducing physician workload and burnout.

Keywords: Artificial intelligence; Deep learning; Machine learning; Multiparametric magnetic resonance imaging; Performance; Prostate cancer; Radiology; Radiomics.

Fig. 1

Hierarchical summary receiver operating characteristic (HSROC) curves for studies assessing the performance of (A) radiologists alone and (B) a combination of artificial intelligence algorithms and radiologists (5 studies, 6 contingency tables) in the detection of any prostate cancer using multiparametric magnetic resonance imaging. CI = confidence interval; PI-RADS = Prostate Imaging-Reporting and Data System.