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<p>This correspondence responds to the recent commentary on my article proposing a transparent, hybrid generative AI framework for patient selection in cosmetic surgery. The commentary rightly emphasizes the importance of explicit task specification, external and temporal validation, and clear threshold-to-action mapping to ensure safe and clinically meaningful deployment. I elaborate on how reasoning-capable large language models, specialty medical models, and retrieval-augmented generation pipelines can produce auditable, guideline-anchored suitability assessments, while acknowledging the need for stronger calibration, stratified reporting, and workflow-linked decision pathways. I also affirm the necessity of regulatory rigor, independent validation, privacy safeguards, and bias monitoring as prerequisites for real-world adoption. This exchange highlights a shared commitment to developing calibrated, ethical, and clinically respectful AI systems that enhance surgical judgment, protect patients, and support proportionate, evidence-aligned care in aesthetic practice.</p><p><i>Level of Evidence V</i> This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors &#xa0;<a href="http://www.springer.com/00266">www.springer.com/00266</a>.</p>

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

  • 期刊论文

  • 发表日期：

    2026年1月13日

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  • Partha Pratim Ray

摘要

This correspondence responds to the recent commentary on my article proposing a transparent, hybrid generative AI framework for patient selection in cosmetic surgery. The commentary rightly emphasizes the importance of explicit task specification, external and temporal validation, and clear threshold-to-action mapping to ensure safe and clinically meaningful deployment. I elaborate on how reasoning-capable large language models, specialty medical models, and retrieval-augmented generation pipelines can produce auditable, guideline-anchored suitability assessments, while acknowledging the need for stronger calibration, stratified reporting, and workflow-linked decision pathways. I also affirm the necessity of regulatory rigor, independent validation, privacy safeguards, and bias monitoring as prerequisites for real-world adoption. This exchange highlights a shared commitment to developing calibrated, ethical, and clinically respectful AI systems that enhance surgical judgment, protect patients, and support proportionate, evidence-aligned care in aesthetic practice.

Level of Evidence V This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors  www.springer.com/00266.