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 - Scorecard - 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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Clinical Scorecard: Comparative Analysis of ChatGPT, DeepSeek, and Perplexity in Managing Acid-Base Disorders

At a Glance

CategoryDetail
Condition
Key MechanismsEvaluation of diagnostic accuracy and consistency of responses from LLMs, including specific metrics used.
Target Population
Care Setting

Key Highlights

  • LLMs can reach or exceed passing thresholds on licensing exams.
  • Acid-base disorders are common in various medical specialties.
  • Performance of LLMs varies across specialized medical question sets.
  • Reliability of LLMs is critical for clinical decision-making, particularly in high-stakes environments.
  • Study evaluated three LLMs on 75 acid-base disturbance cases.

Guideline-Based Recommendations

Diagnosis

    Management

    • Identification of primary disorder and assessment of expected compensation, including examples such as respiratory compensation in metabolic acidosis.

    Monitoring & Follow-up

      Risks

        Patient & Prescribing Data

        Correct management requires detailed interpretation of clinical data, such as arterial blood gas results.

        Clinical Best Practices

        • Use structured clinical vignettes for educational strategies.
        • Ensure consistent responses from LLMs to avoid confusion.
        • Implement ongoing training and updates for LLMs to enhance their clinical accuracy.

        Related Resources & Content

        Original Source(s)

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