Artificial intelligence in radiation oncology: A review of its current status and potential application for the radiotherapy workforce. (October 2021)

• Record Type: Journal Article
• Authors: Parkinson, C.; Matthams, C.; Foley, K.; Spezi, E.
• Is Part Of: Radiography. Volume 27(2021)Supplement 1
• Page Start: S63
• Page End: S68
• Publication Date: 2021-10
• Subjects: Artificial intelligence -- Advanced image processing -- Radiation oncology -- Radiography -- Machine learning -- Data science; Diagnostic imaging -- Periodicals; Radiotherapy -- Periodicals; Cancer -- Radiotherapy -- Periodicals; Diagnostic Imaging -- Periodicals; Neoplasms -- Periodicals; Radiotherapy -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Radiothérapie -- Périodiques; Cancer -- Radiothérapie -- Périodiques; Electronic journals; 616.0757
• DOI: 10.1016/j.radi.2021.07.012
• Languages: English
• ISSNs: 1078-8174

Abstract

Abstract: Objective: Radiation oncology is a continually evolving speciality. With the development of new imaging modalities and advanced imaging processing techniques, there is an increasing amount of data available to practitioners. In this narrative review, Artificial Intelligence (AI) is used as a reference to machine learning, and its potential, along with current problems in the field of radiation oncology, are considered from a technical position. Key Findings: AI has the potential to harness the availability of data for improving patient outcomes, reducing toxicity, and easing clinical burdens. However, problems including the requirement of complexity of data, undefined core outcomes and limited generalisability are apparent. Conclusion: This original review highlights considerations for the radiotherapy workforce, particularly therapeutic radiographers, as there will be an increasing requirement for their familiarity with AI due to their unique position as the interface between imaging technology and patients. Implications for practice: Collaboration between AI experts and the radiotherapy workforce are required to overcome current issues before clinical adoption. The development of educational resources and standardised reporting of AI studies may help facilitate this.