ChatGPT in Medical Education: A Precursor for Automation Bias?

Area of Science:

• Medical Informatics
• Artificial Intelligence in Healthcare
• Clinical Decision Support

Background:

• Artificial intelligence (AI) offers significant potential for accurate and efficient healthcare delivery.
• AI systems can function as 'black boxes,' presenting challenges in understanding the rationale behind their outputs.
• Ensuring physician validation of AI-generated results is critical for safe patient care.

Discussion:

• Automation bias, an over-reliance on AI, poses a risk as users may not critically evaluate AI outputs.
• Factors like inexperience and digital nativity can increase susceptibility to automation bias.
• A lack of comprehensive AI education in medical curricula contributes to automation bias among future physicians.

Key Insights:

• Physicians must exercise clinical judgment and validate AI findings, rather than blindly trusting automated results.
• Automation bias can lead to the acceptance of erroneous AI recommendations, potentially harming patients.
• Understanding the psychological factors influencing automation bias is essential for mitigation.

Outlook:

• Integrating AI education into medical school curricula is necessary to equip future physicians with critical AI evaluation skills.
• Restricting the use of specific AI tools, such as ChatGPT, in medical education to defined tasks can help prevent premature over-reliance.
• A proactive approach to AI education and tool implementation is vital to foster responsible AI adoption in healthcare.