A special fall 2022 issue of Clin Chem Lab Med, focused all articles on the roles of AI in laboratory medicine.
This special issue "Artificial Intelligence and Big Data in Laboratory Medicine" explores the application of AI and big data in the field. Topics covered include machine learning in diagnostics, ethical considerations, AI's role in handling COVID-19, and specific applications in predicting sample turnaround times, diagnosing diseases, and more. The issue aims to foster understanding and collaboration to effectively and ethically leverage AI and big data in laboratory medicine.
An entire issue of major journal, Clinical Chemistry and Laboratory Medicine, was recently devoted to AI and Big Data issues in lab medicine. The volume, edited by Andrea Padoan and Mario Plebani, is titled "Artificial Intelligence and Big Data in Laboratory Medicine" and it aims to explore and present groundbreaking research, reviews, and opinion articles on the application of artificial intelligence (AI) and big data in the field of laboratory medicine.
Topics of the issue span a broad range of AI and big data applications, including machine learning in diagnostics, ethical considerations, and AI's role in handling the COVID-19 pandemic. The articles cover both theoretical aspects, such as the philosophical implications of computability theory in AI, and practical applications like the use of AI in predicting sample turnaround times, diagnosing hepatocellular carcinoma based on salivary protein glycopatterns, or even identifying patients at risk for cancer in a primary care setting.
The editorial article sets the stage for the entire issue by examining whether it is the right time for clinical laboratories to adopt AI. From there, articles delve into ethical considerations associated with AI and big data, giving the issue a holistic view on the subject.
Several articles focus on AI's role in the COVID-19 pandemic, discussing the use of machine learning models for diagnosing and prognosing COVID-19 patients, and the impact of AI in this global health crisis.
The issue also includes several specific applications of machine learning algorithms in laboratory medicine, like predicting low ferritin concentrations, surrogate searching for urine osmolality, and diagnosing diseases such as primary aldosteronism, liver cirrhosis and hepatocellular carcinoma.
The last article is a survey on the utilisation of AI and Big Data in Italian clinical laboratories, giving a geographical and cultural perspective to the issue.
Through this special issue, the editors aim to facilitate a deeper understanding of the potentials and challenges of AI and big data in laboratory medicine, fostering dialogue and collaboration among medical professionals and researchers. The goal is to help the field keep pace with technological advances and to ensure the potential benefits of AI and big data can be leveraged effectively and ethically.
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