ArticleIn vivo (Athens, Greece)
BREAST AI-PLAN: Prompt-Driven AI Assistance for Breast Surgery Planning - A Retrospective Single-center Study.
Article in In vivo (Athens, Greece). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applications of large language models in tumor boards: a systematic review.Oncology reviews · 2026Pooled it
- The role of artificial intelligence in precision medicine for breast cancer.Discover oncology · 2026Review
- Large Language Models as Decision-support Tools for Adjuvant Therapy Planning in Early-stage Hormone Receptor-positive Breast Cancer.Cancer diagnosis & prognosisArticle
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Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND/
aimPreoperative planning in oncoplastic and reconstructive breast surgery requires synthesizing patient- and tumor-specific variables with aesthetic considerations. Variability in documentation and expertise may hinder reproducibility across centers. Large language models (LLMs) can process structured data into consistent outputs. We assessed whether a prompt-engineered artificial intelligence (AI) assistant (BREAST AI-PLAN) could replicate multidisciplinary team (MDT) decisions and aesthetic scores from routinely collected clinical data. PATIENTS AND
methodsThis single-center retrospective study included 30 women undergoing breast cancer surgery. Each case was recorded in a standardized form covering demographics, comorbidities, breast anatomy, tumor biology, and staging. This form was used to generate structured prompts for the AI assistant. The AI returned eight outputs: 1) neoadjuvant therapy suitability; 2) planned surgery; 3) surgical approach; 4) reconstruction type; 5) direct-to-implant feasibility; 6) implant type; 7) adjuvant therapy; and 8) an aesthetic score (BCCT.core-aligned). Clinical MDT decisions were the reference standard. Agreement was measured using Cohen's κ, with linear and quadratic weights for aesthetics.
resultsMean age was 59.2±9.8 years; median body mass index (BMI) 24.2 (20.9-26.8). One patient carried a
conclusionBREAST AI-PLAN reproduced MDT planning elements and aesthetic assessments with moderate-to-very-good agreement. Transparent, auditable AI assistance may standardize surgical planning and support training. Prospective validation with imaging integration is warranted.
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