AI assistance in tumor multidisciplinary teams

Abstract

Multidisciplinary teams (MDTs), the cornerstone of modern cancer care, are facing significant operational inefficiencies. These challenges include the laborious, manual synthesis of unstructured, multimodal patient data for case preparation, which is time-consuming and prone to information overload. Furthermore, heterogeneous operational processes across MDTs themselves compound these issues. Additionally, clinical decisions are often archived in static documents, preventing the systematic collection of decision rationales essential for continuous learning and research. We propose that artificial intelligence (AI), particularly natural language processing (NLP) and large language models (LLMs), can act as integrated partners to solve these problems. The capacity of AI to seamlessly integrate diverse datasets—including imaging, histopathology, genomics, and clinical data—may be instrumental in enhancing diagnostic accuracy, refining personalized treatment plans within a complex cancer management journey. This integration can be achieved through a tiered approach, utilizing models from small NLP for targeted information extraction to foundational generative NLP for complex evidence synthesis, while addressing key challenges in validation, ethical governance, and regulatory oversight. International initiatives are actively developing validated frameworks to facilitate the widespread and standardized adoption of these AI solutions, while taking into account heterogeneous operational processes. By improving data management, streamlining decision making, and establishing crucial feedback loops, AI integration promises to enhance patient outcomes and optimize resource utilization within cancer care.

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