The application and challenges of ChatGPT in laboratory medicine

In recent years, with the rapid development of artificial intelligence technology, chatbots have demonstrated significant potential in the medical field, particularly in medical laboratories. This study systematically analyzes the advantages and challenges of chatbots in this field and delves into their potential applications in disease diagnosis. However, the reliability and scientific nature of chatbots are influenced by various factors, including data quality, model bias, privacy protection, and user feedback requirements. To ensure the accuracy and reliability of output content, it is essential to not only rely on legal frameworks such as the EU AI Act for necessary protection but also to employ two assessment tools, METRICS and CLEAR. These tools are designed to comprehensively evaluate the quality of AI-generated health information, thereby providing a solid theoretical foundation and support for clinical practice.

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Publication Details

Journal
Advances in Laboratory Medicine / Avances en Medicina de Laboratorio
Published
2025-08-14
DOI
https://doi.org/10.1515/almed-2025-0080
Citations
1
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.65
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article

The application and challenges of ChatGPT in laboratory medicine

Zhili Niu, Juanjuan Chen, Xin Cai, Pingan Zhang et al.
1 citations
Advances in Laboratory Medicine / Avances en Medicina de Laboratorio
Artificial Intelligence in Healthcare and Education
0.65
article

The application and challenges of ChatGPT in laboratory medicine

Zhili Niu, Juanjuan Chen, Xin Cai, Pingan Zhang, Xiandong Kuang
article en
1 citations

Abstract

In recent years, with the rapid development of artificial intelligence technology, chatbots have demonstrated significant potential in the medical field, particularly in medical laboratories. This study systematically analyzes the advantages and challenges of chatbots in this field and delves into their potential applications in disease diagnosis. However, the reliability and scientific nature of chatbots are influenced by various factors, including data quality, model bias, privacy protection, and user feedback requirements. To ensure the accuracy and reliability of output content, it is essential to not only rely on legal frameworks such as the EU AI Act for necessary protection but also to employ two assessment tools, METRICS and CLEAR. These tools are designed to comprehensively evaluate the quality of AI-generated health information, thereby providing a solid theoretical foundation and support for clinical practice.

Advances in Laboratory Medicine / Avances en Medicina de LaboratorioVol. 6(4)
Wuhan University (CN), Renmin Hospital of Wuhan University (CN)
Openalex Percentile: Top 22%
Artificial Intelligence in Healthcare and Education
0.65
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