Adoption of AI-driven automation and adaptive radiotherapy in clinical practice: results from a national survey.

PURPOSE: To characterise the current clinical adoption of AI-driven automation and adaptive radiotherapy (ART) in Italy.

MATERIALS AND

METHODS

A structured, modular questionnaire was distributed to 133 Italian radiotherapy and medical physics departments. The survey captured: centre characteristics, clinical use of automation tools (synthetic-imaging, autosegmentation, autoplanning, motion management, and QA automation), ART implementation (offline/online approaches, triggers, dose summation, and patient-specific QA) and perceived benefits and barriers. Responses were analysed including the maturity index (MI), defined as a weighted sum of 14 binary implementation items spanning key workflow domains.

RESULTS

Sixty-one centres responded (46.0%). Routine autosegmentation was reported by 45/61 centres (73.8%) and autoplanning by 27/61 (44.2%). Only 5 centres reported having performed a dedicated FMEA for automation-related processes; heterogeneous QA approaches were reported by 21/61 (34%). A broad definition of ART (including offline re-planning prompted by interfractional changes) was reported by 56/61 centres (91.8%); however, online inter-fraction and intra-fraction adaptation remained limited and largely confined to centres with dedicated ART-ready platforms. Mean (median) MI was 4.99 (5.00) on a theoretical maximum of 9.334, with a weak positive correlation with the number of installed LINACs (r = 0.25, p = 0.049).

CONCLUSIONS

Among responding centres, automation tools are widely available and ART is frequently practised, predominantly through offline approaches. The results suggest that broad penetration of adaptive practices does not yet correspond to homogeneous end-to-end maturity. Standardisation of QA, clearer operational definitions of ART triggers, formal risk analysis, and validated dose accumulation workflows appear as key priorities to support broader and safer scaling of online adaptive strategies.