DOACT Algorithm Versus AI-Based Decision Models in Oral Anticoagulant Therapy for Vascular Patients

Cross-sectional, three-arm comparative validation study evaluating the accuracy and clinical utility of the DOACT algorithm versus standard clinical decision-making and large language model (LLM)-based decision tools.

Inclusion criteria

• Physicians with residency training in Vascular Surgery or official Board Certification in Vascular Surgery.
• Currently practicing clinical and/or surgical vascular care in Brazil.
• Completed the informed consent process (TCLE) and voluntarily agreed to participate.

Exclusion criteria

• Physicians without formal Vascular Surgery residency and without Board Certification.
• Physicians not performing vascular clinical or surgical care (e.g., exclusively administrative, academic, or non-assistance roles).
• Less than 1 year of professional experience after medical school graduation.
• Did not sign or did not fully complete the TCLE.

Large Language Models (LLMs)

• Inclusion Criteria
• Free-access LLMs available to the public at the time of data collection.
• All responses generated using the same standardized prompt.
• Capable of producing complete, text-based clinical answers relevant to vascular surgery decision-making.

Exclusion criteria

• Paid or subscription-based LLMs.
• LLMs requiring institutional licenses, restricted access, or proprietary tokens.
• Models unable to generate full responses to the standardized prompt.