Where medical education meets artificial intelligence: 'Does technology care?'

Area of Science:

• Medical Education Technology
• Artificial Intelligence in Healthcare
• Clinical Practice Integration

Background:

• Deep learning artificial intelligence (AI) promises solutions but introduces practical, ethical, and pedagogical challenges.
• Technological advancements may shift focus from embodied, hands-on clinical work to disembodied, technology-enhanced scenarios.
• The historical 'body as machine' metaphor, amplified by AI, risks reducing complex patient experiences to linear, computational problem-solving.

Purpose of the Study:

• To critically examine the integration of 'cold' AI technologies with 'warm' hands-on medicine.
• To address the pedagogical challenges posed by AI's increasing role in medical practice.
• To explore the potential for AI to objectify patients and diminish the value of patient-physician dialogue.

Main Methods:

• Conceptual analysis of AI's impact on medical practice and education.
• Exploration of the philosophical underpinnings (epistemological, ontological, axiological) of AI and embodied medicine.
• Historical precedent analysis, referencing the shift from qualitative to instrumental medicine.

Main Results:

• AI offers diagnostic and treatment improvements but can lead to 'solutionism,' oversimplifying complex patient realities.
• The integration of AI and embodied medicine is not seamless and requires careful guidance.
• Over-reliance on AI may devalue essential hands-on skills and the nuanced understanding of patient experiences.

Conclusions:

• AI and hands-on medicine must be integrated thoughtfully, recognizing their differing foundations.
• Medical education needs to foster critical awareness of AI's limitations and potential pitfalls.
• A balanced approach is crucial to ensure technology enhances, rather than replaces, compassionate, patient-centered care.