Foundational artificial intelligence models and modern medical practice.

Area of Science:

• Medical Informatics
• Artificial Intelligence in Medicine
• Healthcare Technology

Background:

• The evolution of medical practices, from Hippocrates to modern AI, shares a commitment to comprehensive, individualized patient care.
• Foundation artificial intelligence (AI) models are generating excitement, particularly in medical imaging, due to their potential for data integration and personalized treatment.

Purpose of the Study:

• To critically evaluate the current state and future potential of foundation AI models in medicine, with a specific focus on medical imaging.
• To advocate for a measured approach in adopting these technologies, emphasizing the need to address inherent challenges before widespread clinical application.

Main Methods:

• This opinion piece analyzes the historical parallels between medical practice evolution and foundational AI principles.
• It identifies and discusses four major limitations hindering the adoption of AI in medical imaging: data bias and generalizability, model interpretability, data scarcity and diversity, and computational resource requirements.

Main Results:

• Widespread adoption of foundation AI models in medical imaging requires a critical and cautious approach.
• Addressing limitations such as data bias, interpretability, data diversity, and infrastructure is paramount to unlocking the true potential of AI in healthcare.

Conclusions:

• A culture of rigorous research and robust methodologies is necessary to ensure the development of trustworthy and impactful AI models for medicine.
• Prioritizing the resolution of core challenges will enable the responsible and effective revolutionization of medical care through AI.