Head-to-head evaluation of ChatGPT, DeepSeek, and Perplexity on acid–base disorder case clinical management and drug treatment: Accuracy, domain performance, and response consistency assessment - Takeaways - MDSpire

Head-to-head evaluation of ChatGPT, DeepSeek, and Perplexity on acid–base disorder case clinical management and drug treatment: Accuracy, domain performance, and response consistency assessment

  • By

  • Moteb Khobrani

  • Asaad Ahmed Asaad Khalil

  • Salman Ashfaq Ahmad

  • Azfar Athar Ishaqui

  • June 8, 2026

  • 0 min

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  • 1

    The study evaluated the performance of ChatGPT, DeepSeek, and Perplexity on 75 acid-base disturbance cases using expert-written multiple-choice questions.

  • 2

    Accuracy and consistency of responses were assessed across models, highlighting the importance of reliable outputs in clinical contexts.

  • 3

    LLMs demonstrated varying performance in domain-specific tasks, emphasizing the need for context-specific benchmarking in medical education.

  • 4

    The study aimed to compare overall accuracy and domain-level performance for key interpretive steps in acid-base disorders.

  • 5

    Findings suggest that prompt structure and instruction style significantly influence the accuracy and reliability of LLM outputs.

Original Source(s)

Head-to-head evaluation of ChatGPT, DeepSeek, and Perplexity on acid–base disorder case clinical management and drug treatment: Accuracy, domain performance, and response consistency assessment

  • by Moteb Khobrani, Asaad Ahmed Asaad Khalil, Salman Ashfaq Ahmad, Azfar Athar Ishaqui

  • June 8, 2026

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