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Dedicated AI Expert System vs Generative AI With Large Language Model for Clinical Diagnoses.

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Large language models (LLMs) and diagnostic decision support systems (DDSSs) showed similar performance in diagnosing unpublished clinical cases. Including laboratory results significantly improved diagnostic accuracy for all systems, suggesting potential for hybrid approaches.

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Area of Science:

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

Background:

  • Large language models (LLMs) have emerged as powerful tools, but their diagnostic capabilities compared to traditional systems are not well-established.
  • Diagnostic decision support systems (DDSSs) are established tools, yet their performance against newer LLMs on novel clinical data requires evaluation.

Purpose of the Study:

  • To compare the diagnostic performance of two leading LLMs (ChatGPT-4 and Gemini 1.5) against a traditional DDSS (DXplain).
  • To evaluate system performance with and without the inclusion of laboratory test results using unpublished clinical cases.

Main Methods:

  • A diagnostic study involving 36 unpublished general medicine cases from three academic medical centers.
  • Physician reviewers identified relevant clinical findings; blinded data entry into LLMs and DDSS was performed with and without laboratory data.
  • Performance was assessed by the presence and rank of the correct diagnosis within the top 25 differential diagnoses generated by each system.

Main Results:

  • Without laboratory data, the DDSS showed a trend towards higher diagnostic accuracy (56%) than LLM1 (42%) and LLM2 (39%), though not statistically significant.
  • Inclusion of laboratory test results improved diagnostic accuracy for all systems, with the DDSS achieving 72%, LLM1 64%, and LLM2 58%.
  • All systems successfully listed the correct diagnosis within the top 25 for most cases when laboratory data was provided.

Conclusions:

  • Current LLMs and a traditional DDSS demonstrate comparable, though not statistically significant, performance on unpublished clinical cases when laboratory data is omitted.
  • The inclusion of laboratory test results substantially enhances the diagnostic accuracy of both LLMs and DDSSs.
  • A hybrid approach integrating LLM's linguistic strengths with DDSS's deterministic capabilities may offer synergistic benefits for clinical decision support.