Clarifying the Path Toward Safe and Transparent Generative AI-Guided Patient Selection.

Area of Science:

• Artificial Intelligence in Medicine
• Medical Informatics
• Surgical Technology

Background:

• A prior article proposed a hybrid generative AI framework for patient selection in cosmetic surgery.
• A recent commentary highlighted critical considerations for AI deployment in clinical settings.

Purpose of the Study:

• To respond to commentary regarding a proposed AI framework for cosmetic surgery patient selection.
• To elaborate on the capabilities and requirements for safe and effective AI implementation in aesthetic practice.

Main Methods:

• Discussion of reasoning-capable large language models (LLMs) and specialty medical models.
• Integration of retrieval-augmented generation (RAG) pipelines for auditable assessments.
• Addressing the need for calibration, reporting, and decision pathways.

Main Results:

• Proposed AI framework can generate guideline-anchored suitability assessments.
• Acknowledged requirements for enhanced calibration, reporting, and workflow integration.
• Affirmed necessity of regulatory oversight, validation, privacy, and bias monitoring.

Conclusions:

• Shared commitment to developing ethical, calibrated AI for enhancing surgical judgment.
• AI systems must prioritize patient safety and support evidence-aligned care.
• AI in aesthetic practice requires rigorous validation and regulatory compliance.