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Neurological Diagnosis: Artificial Intelligence Compared With Diagnostic Generator.

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A diagnostic generator program outperformed artificial intelligence (AI) tools like ChatGPT-4 and GLASS AI in accurately diagnosing neurological cases. The generator provided more comprehensive and reliable differential diagnoses compared to current AI applications.

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Area of Science:

  • Medical Informatics
  • Clinical Neurology
  • Artificial Intelligence in Medicine

Background:

  • Artificial intelligence (AI) is increasingly used in medicine for data interpretation and disease tracking, but its diagnostic accuracy requires validation.
  • While AI shows promise in imaging and data analysis, its clinical diagnostic capabilities, particularly in neurology, remain less understood.
  • This study addresses the need for comparative analysis of AI diagnostic tools in a clinical neurology context.

Purpose of the Study:

  • To compare the diagnostic performance of two AI programs (ChatGPT-4 and GLASS AI) against a dedicated neurological diagnostic generator (NeurologicDx.com).
  • To evaluate the accuracy, diagnostic capability, and source authentication of AI tools versus a specialized diagnostic generator in clinical neurology.
  • To assess the reproducibility and robustness of differential diagnoses generated by AI and a diagnostic generator.

Main Methods:

  • Four nonrandomly selected clinicopathologic case records from 2017-2022 were used for comparison.
  • Two AI programs, ChatGPT-4 and GLASS AI, were tested alongside the NeurologicDx.com diagnostic generator.
  • Diagnostic capability, accuracy, and source authentication were assessed for each tool.

Main Results:

  • The diagnostic generator (NeurologicDx.com) provided more differential diagnostic entities than the AI programs.
  • NeurologicDx.com achieved correct diagnoses in 4 out of 4 cases, while ChatGPT-4 had 0 out of 4, and GLASS AI had 1 out of 4.
  • AI program results varied based on query order and repetition, and lacked robust source authentication compared to the diagnostic generator.

Conclusions:

  • The NeurologicDx.com diagnostic generator demonstrated superior performance in generating differential diagnostic lists.
  • NeurologicDx.com offered higher diagnostic accuracy and better reproducibility than the evaluated AI programs.
  • The study highlights the current limitations of AI in clinical neurological diagnosis, emphasizing the need for further validation and development.