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<p>Existing AI solutions—like the XGBoost tool by Li et al.—show potential for preoperative screening but rely on fixed questionnaires and opaque feature weighting. We introduce a hybrid framework that combines reasoning LLMs (OpenAI o3, DeepSeek R1, Google Gemini 2.5, Anthropic Claude 3.7 Sonnet) with specialty medical models (Baichuan-M1, Zhipu AI GLM-4-9B-Chat, OpenBioLLM-Llama-70B, MedLLaMA3-v20, Med-PaLM 2, SurgeryLLM). Patient inputs—structured and free-text—are ingested via a secure mobile app and processed through a retrieval-augmented pipeline. Reasoning LLMs expose chain-of-thought steps for full transparency, while medical LLMs validate each risk factor against clinical guidelines. An ensemble then delivers a composite suitability score, complete with an audit trail of data points and citations. We address key hurdles—model recency, hallucination control, data privacy, and fairness—and recommend a medical-device regulatory approach with independent validation, ongoing bias monitoring, and co-design with multidisciplinary stakeholders.</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 <a href="http://www.springer.com/00266">www.springer.com/00266</a>.</p>

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Toward Transparent AI-Enabled Patient Selection in Cosmetic Surgery by Integrating Reasoning and Medical LLMs

  • 期刊论文

  • 发表日期：

    2025年6月25日

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  • Partha Pratim Ray

摘要

Existing AI solutions—like the XGBoost tool by Li et al.—show potential for preoperative screening but rely on fixed questionnaires and opaque feature weighting. We introduce a hybrid framework that combines reasoning LLMs (OpenAI o3, DeepSeek R1, Google Gemini 2.5, Anthropic Claude 3.7 Sonnet) with specialty medical models (Baichuan-M1, Zhipu AI GLM-4-9B-Chat, OpenBioLLM-Llama-70B, MedLLaMA3-v20, Med-PaLM 2, SurgeryLLM). Patient inputs—structured and free-text—are ingested via a secure mobile app and processed through a retrieval-augmented pipeline. Reasoning LLMs expose chain-of-thought steps for full transparency, while medical LLMs validate each risk factor against clinical guidelines. An ensemble then delivers a composite suitability score, complete with an audit trail of data points and citations. We address key hurdles—model recency, hallucination control, data privacy, and fairness—and recommend a medical-device regulatory approach with independent validation, ongoing bias monitoring, and co-design with multidisciplinary stakeholders.

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.