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Summary
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Generative artificial intelligence (AI) can enhance clinical decision-making by analyzing data for better diagnoses and treatments. This paper proposes 11 verification paradigms and a framework for responsible AI integration to ensure reliability and patient safety.

Keywords:
abductionartificial intelligencebenchmarkingclinical intelligenceiterative processverification paradigms

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Clinical Decision Support

Background:

  • Clinical decision-making integrates evidence, judgment, ethics, and patient input for optimal outcomes.
  • Generative artificial intelligence (AI) offers advanced capabilities for disease diagnosis, treatment tailoring, and patient management.
  • Concerns exist regarding the reliability and accuracy of AI-generated clinical insights.

Purpose of the Study:

  • To propose methods for verifying the evidence-based nature of AI in clinical decision-making.
  • To introduce a model for clinically explainable, fair, and responsible AI.
  • To advocate for AI as an augmentative tool that enhances, rather than replaces, clinician judgment.

Main Methods:

  • Development of 11 distinct "verification paradigms" to assess AI's evidence-based contributions.
  • Framing the concept of "clinician-, expert-, and patient-in-the-loop AI" for collaborative decision-making.
  • Emphasis on transparency, comprehensibility, and ethical grounding in AI systems.

Main Results:

  • Proposed AI integration enhances diagnosis, treatment, and patient management through data analysis.
  • 11 verification paradigms are introduced to ensure AI reliability and evidence-based application.
  • A framework for responsible AI promotes transparency and collaboration between AI, clinicians, and patients.

Conclusions:

  • Generative AI shows significant promise for improving clinical decision-making and patient care.
  • Verification paradigms and responsible AI frameworks are crucial for harnessing AI's potential safely.
  • AI should augment clinical judgment, ensuring continuous learning, ethical compliance, and patient well-being.