Session Item

Artificial Intelligence is becoming more introduced in the clinical routine practice for various steps of the workflow in radiotherapy. Automatic segmentation tools for normal tissues (or even target volumes) may help streamlining the contouring workflow where the output of the AI algorithm can be supervised afterwards. But also more advanced or 'black-box' algorithms providing an optimized treatment planning workflow to finally AI/data driven outcome prediction models are currently investigated. Whereas the maturity level of the AI tools in all the different workflow steps differ for every tool, wherever an AI algorithm is used one needs to carefully investigate the added value of the tool. This added value can be present on various levels. In this talk I will identify and show these various levels ranging from efficiency improvements (time gain of the clinical workflows in a busy clinic), towards educational aspects for residents in training, to quality improvement of for example treatment planning or contouring accuracy. Finally financial and health-economics aspects will be discussed. AI tools typically demand a financial investment whereas the financial return of benefit might not always be in the same department making the introduction of such tools also complicated and possibly different from reimbursement models for 'standard' drug or treatment developments.