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This paper presents the partial implementation of an AI-driven enterprise architecture to optimise Multidisciplinary Team Meetings (MDTMs) in oncology. The architecture integrates a Generative Pretrained Transformer (GPT)-based decision support system, digitised MDTM sheets, and real-time data exchange using RESTful APIs and FHIR standards. Preliminary results show a 35% reduction in workflow time, a 50% increase in data interoperability, and an 85% concordance rate between AI recommendations and clinician decisions. While promising, further validation through pilot studies is required. This work provides a scalable framework for integrating AI into MDTMs, paving the way for improved decision-making and oncology care outcomes.

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Partial Implementation of AI-Driven Solution Architecture in Oncology

  • 会议论文

  • 发表日期：

    2026年

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  • Nassim Bout,
  • Hicham Belhadaoui,
  • Nadia Afifi,
  • Ghizlane Moukhliss,
  • Mounia Abik

摘要

This paper presents the partial implementation of an AI-driven enterprise architecture to optimise Multidisciplinary Team Meetings (MDTMs) in oncology. The architecture integrates a Generative Pretrained Transformer (GPT)-based decision support system, digitised MDTM sheets, and real-time data exchange using RESTful APIs and FHIR standards. Preliminary results show a 35% reduction in workflow time, a 50% increase in data interoperability, and an 85% concordance rate between AI recommendations and clinician decisions. While promising, further validation through pilot studies is required. This work provides a scalable framework for integrating AI into MDTMs, paving the way for improved decision-making and oncology care outcomes.