Artificial intelligence in medicine: How it works, how it fails

Area of Science:

• Healthcare technology
• Medical artificial intelligence
• Clinical informatics

Background:

• Artificial intelligence (AI) is increasingly integrated into healthcare systems.
• Large language models (LLMs) represent a significant advancement in AI, with growing applications in medicine.

Purpose of the Study:

• To elucidate the fundamental principles of large language models (LLMs) for healthcare professionals.
• To highlight the practical capabilities and inherent limitations of LLMs in clinical settings.
• To promote the safe and effective adoption of AI tools in healthcare.

Main Methods:

• Explanation of AI principles focusing on LLMs.
• Utilization of practical clinical examples to illustrate concepts.
• Discussion of LLM functionalities and failure modes.

Main Results:

• AI, particularly LLMs, is a transformative force in healthcare delivery.
• Effective and safe use of LLMs requires a thorough understanding of their operational mechanisms.
• Clinical examples demonstrate both the utility and the constraints of current LLM technology.

Conclusions:

• Healthcare professionals must grasp the intricacies of LLMs for optimal integration.
• Awareness of LLM limitations is paramount for mitigating risks in clinical practice.
• Further education on AI in medicine is essential for advancing healthcare innovation.