The integration of multidisciplinary tumor boards (MTBs) is fundamental in delivering state-of-the-art cancer treatment, facilitating collaborative diagnosis and management by a diverse team of specialists. Despite the clear benefits in personalized patient care and improved outcomes, the increasing burden on MTBs due to rising cancer incidence and financial constraints necessitates innovative solutions. The advent of artificial intelligence (AI) in the medical field offers a promising avenue to support clinical decision-making. This review explores the perspectives of clinicians dedicated to the care of cancer patients—surgeons, medical oncologists, and radiation oncologists—on the application of AI within MTBs. Additionally, it examines the role of AI across various clinical specialties involved in cancer diagnosis and treatment. By analyzing both the potential and the challenges, this study underscores how AI can enhance multidisciplinary discussions and optimize treatment plans. The findings highlight the transformative role that AI may play in refining oncology care and sustaining the efficacy of MTBs amidst growing clinical demands.

Background/Objectives: Accurate target delineation is critical in radiotherapy for head and neck non-melanoma skin cancer (NMSC), where tumor depth and subclinical extension are often underestimated by clinical and dermoscopic assessment alone. While high frequency ultrasound has shown value in surface-based radiotherapy techniques, the role of ultra-high frequency ultrasound (UHFUS) within external beam radiotherapy (EBRT) workflows remains poorly defined. Methods: We conducted a single-institution observational feasibility study including all consecutive patients with head and neck NMSC treated with definitive or adjuvant radiotherapy between July 2022 and July 2023 using a structured multidisciplinary workflow integrating pre-treatment UHFUS. UHFUS was systematically performed prior to CT simulation and incorporated into radiotherapy planning. The primary endpoint was the impact of UHFUS on radiotherapy decision-making, predefined as modification of target delineation, treatment intent, or beam modality selection. Secondary endpoints included feasibility, early local control, and late toxicity (descriptive). Results: Thirty patients were included (median age 85 years; range 66–99). UHFUS influenced at least one decision endpoint in 13 patients (43.3%). In the definitive radiotherapy cohort (n = 18), UHFUS modified gross tumor volume delineation in eight patients (44.4%), with an increase in median GTV from 17.5 cm³ to 24.3 cm³. Among patients initially referred for adjuvant radiotherapy (n = 12), UHFUS identified macroscopic residual disease in two cases, leading to a change in treatment intent from adjuvant to definitive radiotherapy. UHFUS supported beam modality selection in three patients by enabling safe use of electron therapy for superficial lesions. After a median follow-up of 24 months (range 12–24), no local recurrences were observed. Late toxicity was limited to grade 1 cutaneous events. Conclusions: Integration of UHFUS into EBRT planning for head and neck NMSC is feasible and clinically informative. UHFUS acts as a decision-shaping tool, influencing target delineation, treatment intent, and modality selection within a multidisciplinary workflow. These findings support further prospective evaluation of UHFUS-guided radiotherapy planning to standardize decision algorithms and assess long-term clinical impact.