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Clarifying the Path Toward Safe and Transparent Generative AI-Guided Patient Selection.
1Department of Computer Applications, Sikkim University, Gangtok, Sikkim, India. ppray@cus.ac.in.
Aesthetic Plastic Surgery
|January 13, 2026
Summary
This response addresses AI in cosmetic surgery patient selection. It details a hybrid generative AI framework, emphasizing safety, ethical considerations, and clinical validation for AI-assisted surgical judgment.
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.
