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Foundational artificial intelligence models and modern medical practice.

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Summary
This summary is machine-generated.

Foundation artificial intelligence (AI) models offer promise in medicine, but careful consideration of data bias, interpretability, and resource limitations is essential for safe and effective clinical integration.

Keywords:
artificial intelligenceclinical adoptiondata biasdata scarcity and diversityfoundational modelsinterpretable AIlarge language modelslarge visual modelsmodern medicineprecision medicine

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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.