Issue highlights—July 2024

This is the authors' abstract. We don't add key points for this paper.

The integration of artificial intelligence (AI) into hematology diagnostics represents a significant advancement, particularly within the context of flow cytometry.AI enhances flow cytometry by applying advanced algorithms and machine learning to identify patterns in cellular data that may not be detectable through traditional methods.This integration is expected to improve diagnostic accuracy and efficiency, offering more personalized patient care.This issue focuses on the application of AI in enhancing flow cytometry for hematology diagnostics.The articles highlight current advancements and the potential of AI to set new standards in the diagnostic accuracy and management of hematological disorders, emphasizing the importance of this technology in facilitating more effective and tailored treatment strategies.The manuscript "Recommendations for using artificial intelligence in clinical flow cytometry" by Ng et al. is a high-level review of basic terminology and general applications of AI and machine learning in the clinical flow cytometry laboratory.The authors list a set of preliminary and general considerations regarding development, validation, quality assurance, explainability, and regulatory considerations.They posit AI could be used in clinical flow cytometry to improve efficiency, assist in the prioritization of cases, reduce errors, and highlight previously unrecognized immunophenotypes underlying biological processes.Currently, the interpretation of clinical flow cytometric data relies on manual gating approaches and the subjective assessments of two-dimensional dot plots (Wood, 2020).AI software can process large amounts of data quickly and accurately.The authors argue it will be used to aid in the interpretation of clinical flow cytometry data by leveraging its high dimensional pattern recognition and decisionmaking capabilities, improving efficiency, and reducing errors.They point out that the rigor for algorithm validation should be proportional to the comparative impact on clinical workflows.Low-risk situations, such as ordering additional antibody panels or identification of cases for an urgent review, should require a less stringent validation process than a high-risk situation, such as the autonomous, human out-of-theloop process for the interpretation and/or sign-out of cases.That withstanding, they insist review by an experienced hematopathologist should always be required.The authors emphasize the importance of collaboration and data sharing among all stakeholders involved in diagnostic flow cytometry to ensure the successful integration of AI.The success of AI in the clinical flow cytometry laboratory will likely rely on the availability of large amounts of annotated data sets (Lee et al., 2008).At present, however, such clinical data sets are not widely accessible for a variety of reasons.To maximize the effectiveness of AI in our field, a shift in mindset will be required.By sharing cases and files, stakeholders can collectively contribute to building robust AI models, improving accuracy in diagnosis, and ultimately enhancing patient care outcomes.