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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Medicine

Background:

  • Artificial intelligence (AI) has evolved significantly over five decades.
  • Large language models (LLMs) represent a major advancement in AI capabilities.

Purpose of the Study:

  • To compare the diagnostic performance of a modern LLM (GPT-4o) with an early AI model (1970 CAL).
  • To highlight the progress of AI in clinical decision-making and encourage physician adoption of LLMs.

Main Methods:

  • A clinical case of metabolic acidosis was presented to both GPT-4o and a 1970s decision-tree AI model (CAL).
  • Diagnostic reasoning, data interpretation, and management recommendations were recorded and compared.

Main Results:

  • GPT-4o provided a comprehensive analysis, identified causes, suggested tests/treatments, and offered a narrative explanation.
  • The 1970 CAL model correctly identified metabolic acidosis but had limited, sequential, rule-based guidance.
  • GPT-4o integrated data holistically, while CAL required sequential prompts and struggled with complex information.

Conclusions:

  • Modern LLMs like GPT-4o demonstrate transformative potential in clinical decision-making.
  • AI in medicine has progressed substantially, with current tools augmenting, not replacing, physician expertise.
  • Further validation and clinical trials are necessary for widespread adoption of LLMs in healthcare.