Understanding generative AI to harness its potentials and mini- mize risks: A perspective

Area of Science:

• Medical Artificial Intelligence
• Natural Language Processing
• Computer Vision

Background:

• Generative AI (GAI) hype may overstate its current capabilities in medical diagnosis.
• Past overestimations of AI in radiology highlight the need for realistic expectations.
• GAI's interaction with natural language necessitates specific precautions in medical applications.

Purpose of the Study:

• To provide a realistic perspective on Generative AI in medicine.
• To explain the inner working mechanisms of Generative AI.
• To critically evaluate datasets and evaluation methods for GAI in healthcare.

Main Methods:

• Review of Generative AI development and deployment principles.
• Analysis of natural language processing in medical contexts.
• Critical assessment of data requirements and performance metrics for medical AI.

Main Results:

• Generative AI is best viewed as an assistive tool requiring human supervision.
• Understanding GAI's mechanisms is crucial for appropriate medical application.
• Current datasets and evaluation methods need careful consideration for GAI in medicine.

Conclusions:

• Generative AI can support medical advancements when developed and deployed responsibly.
• Human oversight is critical for the safe and effective use of GAI in healthcare.
• Further research into GAI's limitations and robust evaluation is necessary for clinical integration.