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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Published on: October 10, 2018

When the Algorithm Speaks: A Case for Simulation-Based Training in Communicating Artificial Intelligence-Generated

Khizra Ahmad1

  • 1HealthMAIT - Health & Medical AI Training Ltd., London, UK. khizraahmad@healthmaituk.com.

Journal of Cancer Education : the Official Journal of the American Association for Cancer Education
|May 22, 2026
PubMed
Summary

Artificial intelligence (AI) tools in oncology present new communication challenges. Clinicians need training for AI-mediated patient consultations, as current frameworks are inadequate for this algorithmic encounter.

Keywords:
Algorithmic ConsultationArtificial IntelligenceInformational EquityOncology CommunicationPrognostic DisclosureSimulation-Based EducationTRACE Framework

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Published on: October 10, 2018

Area of Science:

  • Oncology
  • Medical Informatics
  • Medical Education

Background:

  • Artificial intelligence (AI) prognostic tools are increasingly integrated into routine oncology workflows.
  • These tools generate recurrence risk scores and survival estimates that clinicians must communicate to patients.
  • Existing communication frameworks are not designed for AI-mediated patient-clinician interactions.

Purpose of the Study:

  • To reflect on the clinical and educational consequences of AI integration in oncology communication.
  • To introduce the concept of the 'algorithmic consultation' and its unique communicative demands.
  • To propose a novel simulation framework for training clinicians in AI-assisted prognostic communication.

Main Methods:

  • Conceptual analysis of the 'algorithmic consultation' in oncology.
  • Identification of five specific communicative demands arising from AI prognostic outputs.
  • Proposal of the TRACE (Training, Reflection, AI, Communication, Education) simulation framework.

Main Results:

  • The integration of AI prognostic tools creates a triadic encounter (clinician-patient-AI) not addressed by current communication protocols like SPIKES.
  • Five distinct communicative challenges emerge when AI outputs mediate clinician-patient discussions.
  • The TRACE framework is proposed as a standardized simulation for postgraduate oncology training.

Conclusions:

  • Clinicians require specific training to effectively navigate AI-mediated prognostic communication with cancer patients.
  • The deployment of AI tools in low- and middle-income countries necessitates parallel investment in communication training.
  • Equipping clinicians to interpret and communicate AI outputs is crucial for patient care.