Table 1.

Summary of opportunities for artificial intelligence (AI) technology in oncological imaging.

	Current clinical practice	Potential for AI technology
Screening/detection	Predominantly manual detection	May use automated or semi-automatic techniques to increase accuracy or efficiency
Diagnosis and risk stratification	Predominantly clinical “rules” based (TI-RADS, Lung-RADS, PI-RADS, etc.)	May improve accuracy
Tumour segmentation	Not typically done as it is highly labour intensive	May improve efficiency and reliability May allow for feature extraction for other uses of AI in oncological imaging
Precision oncology	Not typically done	May subtype tumours into known or new histopathology or genomic subtypes that can be targeted for therapy
Predicting prognosis and assessing treatment response	Tumours stage based on anatomy (local invasion and metastases) Treatment response typically size-based criteria (e.g., RECIST), limited by pseudoprogression or pseudoresponse	May subtype tumours by tumour biology/grade to determine appropriate treatment May use other features in addition to tumour size including tumour morphology to determine response and to distinguish from treatment effects

TI-RADS, Thyroid Imaging Reporting and Data Systems; Lung-RADS, Lung Reporting and Data Systems; PI-RADS, Prostate Imaging Reporting and Data Systems; RECIST, Response Evaluation Criteria In Solid Tumors.