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Justien Dingelstad1, Francisca Grommé1, Claartje Ter Hoeven2

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This study examines how a radiological classification algorithm integrated into multidisciplinary neuro-oncology meetings. Physicians debated the algorithm's role, considering its presentation and interpretation within diagnostic deliberations.

Keywords:
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Area of Science:

  • Medical Sociology
  • Health Informatics
  • Artificial Intelligence in Medicine

Background:

  • Radiological classification algorithms are increasingly used in clinical settings.
  • Understanding the integration of AI tools into diagnostic decision-making is crucial.
  • Multidisciplinary team meetings are key for complex case discussions.

Purpose of the Study:

  • To explore the integration of a radiological classification algorithm into diagnostic deliberations.
  • To analyze how physicians interacted with algorithmic output in neuro-oncology meetings.
  • To identify challenges and considerations for incorporating AI into clinical practice.

Main Methods:

  • A participatory ethnographic case study was conducted.
  • The algorithm's output was presented during weekly multidisciplinary neuro-oncology meetings.
  • The study analyzed physicians' discussions and reactions to the algorithmic output using a dramaturgical framework.

Main Results:

  • Physicians actively speculated on the algorithm's implications for collective diagnosis.
  • Discussions focused on the presentation and representation of the AI's output.
  • Some physicians embraced AI's perceived objectivity, while others sought to contextualize and question it.

Conclusions:

  • The integration of AI algorithms requires careful consideration of their role and representation.
  • A dramaturgical framework highlights key questions for fitting AI into diagnostic practices.
  • Addressing issues of representation and context is vital for successful AI adoption in healthcare.