<b><i>Background:</i></b> The incorporation of digital pathology into routine pathology practice is becoming more widespread. Definite advantages exist with respect to the implementation of artificial intelligence (AI) and deep learning in pathology, including cytopathology. However, there are also unique challenges in this regard. <b><i>Summary:</i></b> This review discusses cytology-specific challenges, including the need to implement digital cytology prior to AI; the large file sizes and increased acquisition times for whole slide images in cytology; the routine use of multiple stains, such as Papanicolaou and Romanowsky stains; the lack of high-quality annotated datasets on which to train algorithms; and the considerable computer resources required, in terms of both computer infrastructure and skilled personnel, for computing and storage of data. Global concerns regarding AI that are certainly applicable to cytology include the need for model validation and continued quality assurance, ethical issues such as the use of patient data in developing algorithms, the need to develop regulatory frameworks regarding what type of data can be utilized and ensuring cybersecurity during data collection and storage, and algorithm development. <b><i>Key Messages:</i></b> While AI will likely play a role in cytology practice in the future, applying this technology to cytology poses a unique set of challenges. A broad understanding of digital pathology and algorithm development is desirable to guide the development of algorithms, as well as the need to be cognizant of potential pitfalls to avoid when incorporating the technology in practice.

Artificial intelligence (AI) technologies have the potential to transform cytopathology practice, and it is important for cytopathologists to embrace this and place themselves at the forefront of implementing these technologies in cytopathology. This review illustrates an archetypal AI workflow from project conception to implementation in a diagnostic setting and illustrates the cytopathologist's role and level of involvement at each stage of the process. Cytopathologists need to develop and maintain a basic understanding of AI, drive decisions regarding the development and implementation of AI in cytopathology, participate in the generation of datasets used to train and evaluate AI algorithms, understand how the performance of these algorithms is assessed, participate in the validation of these algorithms (either at a regulatory level or in the laboratory setting), and ensure continuous quality assurance of algorithms deployed in a diagnostic setting. In addition, cytopathologists should ensure that these algorithms are developed, trained, tested and deployed in an ethical manner. Cytopathologists need to become informed consumers of these AI algorithms by understanding their workings and limitations, how their performance is assessed and how to validate and verify their output in clinical practice.

Aims Plasmablastic lymphoma (PBL) occurs mainly in immunocompromised individuals, usually secondary to human immunodeficiency virus (HIV) infection. It classically occurs intraorally, but has been described in extraoral locations. The aim of this study was to define the immunophenotype and Epstein–Barr virus (EBV) status in a large single‐centre cohort of extraoral PBL (EPBL) in South Africa, a high‐prevalence HIV setting. Methods and results This retrospective study of 45 EPBLs included patients' age, gender, race, HIV status, and site. Cases were reviewed histologically, and classified morphologically as pure plasmablastic or plasmablastic with plasmacytic differentiation, and assessed immunohistochemically with antibodies against CD45, CD20, CD79a, PAX5, CD138, MUM1/IRF4, BLIMP1, VS38c, Ki67, bcl‐6, CD10, cyclin D1, and human herpesvirus‐8, by the use of standard automated procedures. EBV was assessed by the use of chromogenic in‐situ hybridisation. Tumours were assessed with a fluorescence in‐situ hybridisation (FISH) MYC break‐apart probe. Twenty‐seven PBLs showed pure plasmablastic morphology, and 18 showed plasmacytic differentiation. The male/female ratio was 1.5:1. The anus was the favoured extraoral site (31.1%), followed by lymph nodes (15.6%). All 29 patients with known HIV status were HIV‐positive. The immunohistochemical profile recapitulated that reported for oral PBLs and EPBLs in HIV‐positive and HIV‐negative patients. EBV was positive in 92.5% of PBLs. FISH analysis showed MYC rearrangement in 48% of cases. Conclusion This study showed a strong association of EPBLs with HIV and EBV infection, similarly to the previously described oral PBL. The strong EBV association together with other clinicopathological parameters and an immunohistochemical profile that includes CD45, CD20, MUM1/IRF4, CD138 and Ki67 may be used in distinguishing PBL from diffuse large B‐cell lymphoma and plasma cell myeloma.