Abstract
AIMS: Artificial intelligence (AI) is increasingly used in neuro-oncology for tumour detection and characterisation. However, its integration into patient workflows remains underdeveloped. This study aims to design an AI- driven synoptic reporting tool to assist multidisciplinary team (MDT) members in treatment decision-making for meningiomas, pituitary adenomas, and gliomas. The tool will synthesise key clinical and radiological data into a standardised structured report, optimising preoperative planning, surveillance imaging and surgical strategy formulation. METHODS: Firstly, a systematic literature review will examine current AI applications in synoptic reporting to ensure nov- elty and feasibility. Next, we propose a two-stage cross-sectional survey of a large tertiary neurosciences centre prior to dissem- ination to national and international neuro-oncology societies. Members of neuro-oncology, skull-base and stereotactic radiosurgery MDTs —including neurosurgeons, neuroradiologists, oncologists, and pathologists— will identify critical parameters for AI-driven synoptic reporting. Using a mixed-methods approach, partici- pants will assess essential reporting elements, desired functionalities, and the potential impact on clinical work- flow. Finally, process mapping will outline the current preoperative workflow, identifying key decision points and opportunities for AI integration. RESULTS: The survey is expected to reveal consensus on key elements required for AI-driven synoptic reporting, including tumour and patient specific radiological features. Process mapping highlighted workflow inefficiencies and socio-ethical concerns regarding AI and large-scale data usage. These insights will guide the development of an optimised reporting template, ensuring clinical relevance and practical applicability. CONCLUSION: This study will establish an AI-assisted synoptic reporting model designed to enhance treatment planning in neuro-oncology. By aligning with MDT priorities, this tool aims to improve workflow efficiency, surgical pre- paredness and surveillance imaging. Our findings inform the development of a gold-standard structured re- porting framework tailored to pre-operative treatment and surveillance imaging needs.