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Design criteria for AI-based IT systems.

Heinz U Lemke1, Franziska Mathis-Ullrich2

  • 1International Foundation for Computer Assisted Radiology and Surgery - IFCARS, Küssaberg, Germany. hulemke@cars-int.org.

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Summary

Designing transparent and reproducible artificial intelligence (AI) systems for clinical decision-making is crucial. This involves moving from data to wisdom, ensuring quality across system, information, and service for healthcare applications.

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Area of Science:

  • Focuses on the intersection of artificial intelligence (AI), software engineering, and clinical decision-making.
  • Explores the design principles for AI-based IT systems in healthcare.

Background:

  • Addresses the need for transparent and reproducible AI systems in clinical decision-making.
  • Discusses the application of software engineering principles to AI system development in radiology and surgery.

Discussion:

  • Highlights the increasing complexity of AI algorithms like large language models (LLMs) and generative pre-trained transformers (GPTs).
  • Emphasizes the necessity of rigorous examination for AI transparency and comprehensibility among healthcare stakeholders.

Key Insights:

  • Proposes a design cycle for AI-based IT systems, emphasizing system, information, and service quality.
  • Suggests a stakeholder-specific AI system design framework using a modest GPT based on a small language model.
  • Illustrates a conceptual GPT system design moving from data to wisdom-based decision-making.

Outlook:

  • Presents a fundamental question on societal preference for AI-driven data intelligence versus human-curated model intelligence in healthcare.
  • Announces planned CARS workshops and think tanks to explore future directions for AI in medical disciplines like radiology and surgery.