Table 1. Table showing practical implications of AI.

AI: Artificial intelligence; CT: Computed tomography; MRI: Magnetic resonance imaging; MR: Magnetic resonance; CNN: Convolutional neural network.

Author	Study	Conclusion
Hamm et al. [28]	Artificial intelligence and radiomics in MRI-based prostate diagnostics	This study concludes that AI targets on the identification and classification of prostate cancer, but also attempts to classify aggressive tumor nature according to the Gleason score.
Jermyn et al. [29]	Neural networks improve brain cancer detection with Raman spectroscopy in the presence of operating room light artifacts	This study states that by providing molecular information that distinguishes between a normal brain and cancer tissue to Raman spectroscopy, it can detect invasive brain cancer in glioma patients.
Charron et al. [30]	Automatic detection and segmentation of brain metastases on multimodal MR images with a deep convolutional neural network	This study demonstrated that a deep network approach is propitious for the spotting and the segmentation of brain metastases on multimodal MRI.
Rodriguez-Ruiz et al. [31]	Stand-alone Artificial Intelligence for breast cancer detection in mammography: comparison with 101 radiologists	This study concluded that the AI system's performance was statistically non-inferior to that of the 101 radiologists; it achieved a cancer detection accuracy comparable to an average breast radiologist in this retrospective setting
Ehteshami Bejnordi et al. [32]	Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer	This study demonstrated that some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow. The algorithm performance was comparable with an expert pathologist interpreting whole-slide images without time constraints.
Urakawa et al. [33]	Detecting intertrochanteric hip fractures with orthopedist-level accuracy using a deep convolutional neural network	This study concluded that the convolutional neural networks’ (CNN) performance exceeded that of orthopedic surgeons in identifying intertrochanteric hip fractures from proximal femoral radiographs under limited conditions. CNN has a considerable potential to be a useful tool for screening for fractures on plain radiographs, especially in the emergency room, where orthopedic surgeons are not readily available.
Liu et al. [34]	Clinical application of artificial intelligence recognition technology in the diagnosis of stage T1 lung cancer	This study states that the automatic learning of early lung cancer, chest CT images by artificial intelligence can achieve high sensitivity and specificity of early lung cancer detection and could assist doctors in the diagnosis of lung cancer.