Objective:

To discuss the methodological considerations and limitations of the study by Tezcan et al. regarding the use of ChatGPT for warfarin dose adjustment, particularly focusing on the implications for clinical practice.

Key Findings:

• The reference for ChatGPT is a product description, not a validated clinical study, limiting its applicability.
• The study's uniform INR target range does not account for patient-specific variations, which is critical for accurate dosing.
• Real-world dosing constraints may lead to discrepancies between clinician and LLM-generated recommendations, impacting patient safety.

Interpretation:

The findings of the study by Tezcan et al. are valuable for generating hypotheses but have significant methodological limitations that could affect their applicability in clinical settings.

Limitations:

• Use of a product description as a reference instead of peer-reviewed evidence, undermining the study's credibility.
• Uniform INR target range does not reflect individual patient needs, which is essential for effective anticoagulation management.
• Practical dosing limitations due to available tablet formulations that may not align with calculated doses.

Conclusion:

Clinician-integrated AI-supported clinical decision support systems may offer a more effective approach for anticoagulant dose adjustment in the future, and further research should explore their implementation in clinical practice.