Work

Prompt engineering and diagnostic accuracy of multimodal large language models in thyroid fine-needle aspiration cytology.

Year: 2025

Type: article

Abstract: Role of Large language models (LLMs) in fine-needle aspiration cytology (FNAC) image analysis remain uncertain. We evaluated two LLMs - Chat GPT-4o (OpenAI) and Claude 3.5 Sonnet (Anthropic) on 63 thyroid FNAC cases, each represented by eight microscopic images (Pap and MGG, 10x/40x), using generic and structured prompts. Structured prompts improved Bethesda concordance and near-match rates but inter-rater agreement remained poor (κ ≤ 0.09). Specificity reached 100% with structured prompts, but sensitivity dropped to ≤11.8% and misclassification persisted. LLMs show potential, but domain-specific training and validation are necessary for clinical use.

Source: PubMed

DOI: 10.6026/97320630021317

Authors: Bibhas Saha Dalal, Kaushik Mukhopadhyay, Dwaipayan Roy, Souvik Bhattacharya, Indranil Chakrabarti, Santosh Kumar Mondal

Institutions: All India Institute of Medical Sciences, Kalyani, All India Institute of Medical Sciences, Indian Institute of Science Education and Research Kolkata

Language: English

Topic: Artificial Intelligence in Healthcare and Education

Subfield: Health Informatics

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W4414422710

FWCI: 0.41

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Cited by: 2

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