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AI assistance in tumor multidisciplinary teams.

R J Geukes Foppen1, M Morkūnas2, A Traverso3,4

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Summary

Artificial intelligence (AI) can optimize cancer care by addressing inefficiencies in multidisciplinary teams (MDTs). AI tools, including natural language processing (NLP) and large language models (LLMs), streamline data synthesis and decision-making for better patient outcomes.

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Area of Science:

  • Oncology
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Multidisciplinary teams (MDTs) in cancer care face operational inefficiencies due to manual data synthesis and fragmented decision-making processes.
  • Current methods for preparing patient data for MDTs are time-consuming, prone to information overload, and hinder systematic learning.
  • Clinical decisions are often stored in static documents, preventing the capture of rationale crucial for research and continuous improvement.

Purpose of the Study:

  • To propose artificial intelligence (AI), specifically natural language processing (NLP) and large language models (LLMs), as integrated solutions to enhance MDT operational efficiency.
  • To explore how AI can facilitate the seamless integration of multimodal patient data (imaging, histopathology, genomics, clinical data) for improved cancer care.
  • To address the challenges associated with AI implementation, including validation, ethical governance, and regulatory oversight in clinical settings.

Main Methods:

  • Utilizing a tiered AI approach, from small NLP models for data extraction to foundational generative NLP for evidence synthesis.
  • Integrating diverse datasets including imaging, histopathology, genomics, and clinical information.
  • Developing validated frameworks and addressing ethical and regulatory considerations for AI adoption in heterogeneous operational environments.

Main Results:

  • AI integration promises to enhance diagnostic accuracy and personalize treatment plans by synthesizing complex patient data.
  • Streamlined data management and decision-making processes are expected through AI-powered tools.
  • Establishment of feedback loops through AI can support continuous learning and research in cancer care.

Conclusions:

  • AI, particularly NLP and LLMs, offers a transformative potential to overcome MDT inefficiencies in cancer care.
  • Successful AI integration requires addressing validation, ethical governance, and regulatory challenges through international collaborative efforts.
  • AI implementation can significantly improve patient outcomes and optimize resource utilization in the complex cancer management journey.