Artificial intelligence in simulation-based training for Health Professions Education: Navigating the rabbit hole

Area of Science:

• Medical Education Technology
• Artificial Intelligence in Healthcare
• Simulation-Based Training (SBT)

Background:

• Simulation-based training (SBT) has evolved from low-fidelity to high-fidelity methods.
• Integration of artificial intelligence (AI) presents new opportunities for advancing SBT.

Purpose of the Study:

• To review the role of AI in augmenting and transforming simulation-based training.
• To identify challenges and barriers to AI implementation in SBT.
• To provide an overview of the future integration of AI in SBT.

Main Methods:

• Literature review on the application of AI in simulation-based training.
• Analysis of AI's impact on scenario design, realism, feedback, and engagement.
• Identification of challenges including AI bias, cost, and ethical considerations.

Main Results:

• AI successfully enhances simulation efficacy through improved scenario design, realism, and feedback.
• AI applications in SBT platforms have demonstrated increased training effectiveness.
• Identified challenges include algorithmic bias, 'black box' errors, cost, and ethical concerns.

Conclusions:

• AI offers significant potential to transform and improve simulation-based training in health professions.
• Addressing challenges related to bias, cost, and ethics is crucial for successful AI integration.
• Collaborative efforts among stakeholders are accelerating AI adoption in SBT.