BACKGROUND:Chronic lymphocytic leukemia (CLL) is a rare hematologic malignancy to occur in pregnancy, with an estimated incidence of 1 in 75,000 pregnancies. Pregnant women with CLL face increased susceptibility to infections, due to a weakened immune system. Higher risks of fetal malformations and death are associated with CLL treatment during pregnancy, emphasizing the need for careful consideration and management in these cases. SUMMARY:This review aimed to summarize the current evidence regarding the diagnosis, prognosis, and treatment of CLL in pregnant cases. A comprehensive search strategy was employed across multiple databases, yielding 14 case reports for inclusion. The cases were divided based on CLL diagnosis onset, either before or during pregnancy. Our results showed that patients diagnosed during pregnancy (n = 5) were mostly asymptomatic at diagnosis, with management ranging from supportive care to leukapheresis and transfusions. Postpartum treatment varied, with some patients requiring no additional therapy and others receiving chemotherapy. Pregnancy outcomes were generally favorable, with most neonates born healthy at term. However, one case of Richter transformation resulted in maternal death despite treatment. Among patients with pre-existing CLL (n = 9), the majority experienced an indolent course during pregnancy, with only supportive care required. A few cases necessitated treatment due to progressive disease or complications, including chemotherapy, leukapheresis, and splenectomy. KEY MESSAGES:This review highlights the heterogeneous nature of CLL in pregnancy and the importance of individualized management based on disease severity, gestational age, and maternal-fetal risks. Close monitoring, supportive care, and a multidisciplinary approach are essential for optimizing outcomes in this rare and complex clinical scenario.

Vascular smooth muscle cells (VSMCs) play a critical role in regulating vasotone, and their phenotypic plasticity is a key contributor to the pathogenesis of various vascular diseases. Two main VSMC phenotypes have been well described: contractile and synthetic. Contractile VSMCs are typically found in the tunica media of the vessel wall, and are responsible for regulating vascular tone and diameter. Synthetic VSMCs, on the other hand, are typically found in the tunica intima and adventitia, and are involved in vascular repair and remodeling. Switching between contractile and synthetic phenotypes occurs in response to various insults and stimuli, such as injury or inflammation, and this allows VSMCs to adapt to changing environmental cues and regulate vascular tone, growth, and repair. Furthermore, VSMCs can also switch to osteoblast-like and chondrocyte-like cell phenotypes, which may contribute to vascular calcification and other pathological processes like the formation of atherosclerotic plaques. This provides discusses the mechanisms that regulate VSMC phenotypic switching and its role in the development of vascular diseases. A better understanding of these processes is essential for the development of effective diagnostic and therapeutic strategies.

INTRODUCTION:Appendiceal mucinous neoplasms (AMNs) represent a rare and diagnostically challenging group of tumors. This systematic review aims to summarize the reported molecular and immunohistochemical markers (IHC) associated with AMNs and compare them with ovarian mucinous neoplasms (OMNs) and colorectal adenocarcinoma (CRC). METHODS:A comprehensive search was performed in PubMed/MEDLINE/PMC, Scopus, Embase, and Web of Science databases to identify studies looking at IHC and molecular markers in AMNs. Chi-squared and Fisher's exact tests were utilized to compare the marker expression across different tumor types. RESULTS:We identified 27 articles reporting several potential biomarkers for distinguishing between different subtypes of AMNs. Mutations in KRAS, GNAS, and RNF43 emerged as notable biomarkers, with KRAS mutations being the most prevalent across all subtypes. Additionally, p53 IHC overexpression was associated with higher tumor grades. When comparing AMNs with OMNs, we observed a higher prevalence of CK20, CDX2, SATB2, and MUC2 IHC expression, as well as KRAS and GNAS mutations, in AMNs. Conversely, CK7 and PAX8 IHC expression were more prevalent in OMNs. Comparing AMNs with CRCs, we found a higher prevalence of TOPO1 and PTEN IHC expression, as well as KRAS and GNAS mutations, in AMNs. Conversely, nuclear β-catenin IHC expression, as well as TP53, APC, and PIK3CA mutations, were more prevalent in CRCs. CONCLUSION:This systematic review identified possible markers for distinguishing AMNs and differentiating between AMNs, OMNs, or CRCs.

This review aimed to assess bleeding risks and explore management options in atrial fibrillation (AF) patients with immune thrombocytopenia (ITP), aiming to formulate an optimal therapeutic approach for improved patient prognosis. Employing MeSH terms, a comprehensive search strategy identified articles on bleeding risks and management guidelines in AF combined with ITP. Original research papers were included, while animal studies, reviews, and non-English articles were excluded. From four databases, 1891 articles were initially retrieved, resulting in 10 relevant full-text articles. Eight studies investigated the effectiveness of anticoagulants in managing concurrent AF and ITP, demonstrating reduced bleeding risk and promising outcomes. Two papers explored surgical interventions, particularly left atrial appendage closure, suggesting its safety for AF management in patients with primary hemostatic disorders, including thrombocytopenia. While the pathophysiological mechanisms of AF and ITP remain unclear, anticoagulation regimens exhibited promising reductions in bleeding risks. Larger studies are warranted to enhance understanding and investigate optimal treatments for AF and ITP.

Thrombocytopenia is a medical condition where blood platelet count drops very low. This drop in platelet count can be attributed to many causes including medication, sepsis, viral infections, and autoimmunity. Clinically, the presence of thrombocytopenia might be very dangerous and is associated with poor outcomes of patients due to excessive bleeding if not addressed quickly enough. Hence, early detection and evaluation of thrombocytopenia is essential for rapid and appropriate intervention for these patients. Since artificial intelligence is able to combine and evaluate many linear and nonlinear variables simultaneously, it has shown great potential in its application in the early diagnosis, assessing the prognosis and predicting the distribution of patients with thrombocytopenia. In this review, we conducted a search across four databases and identified a total of 13 original articles that looked at the use of many machine learning algorithms in the diagnosis, prognosis, and distribution of various types of thrombocytopenia. We summarized the methods and findings of each article in this review. The included studies showed that artificial intelligence can potentially enhance the clinical approaches used in the diagnosis, prognosis, and treatment of thrombocytopenia.

Introduction: Acute lymphoblastic leukemia (ALL) is an aggressive blood cancer that begins in the bone marrow. It is the most common childhood cancer and requires early and accurate diagnosis for optimal treatment outcomes. Through automated image analysis of peripheral blood smears and bone marrow biopsies, artificial intelligence (AI), particularly Deep Learning (DL), have opened new avenues for improving ALL diagnosis. While bone marrow testing is the gold standard for ALL confirmation, AI applications in diagnosing ALL from bone marrow images have received less attention in the literature than peripheral blood smears. This review aims to assess the current state of AI in ALL diagnosis using bone marrow aspirates and biopsies, with a focus on Deep Learning (DL) models such as Convolutional Neural Networks (CNNs). Methods: A comprehensive literature search, conducted on June 11th, 2023, covered major medical databases, including PubMed/MEDLINE, Scopus, Embase, and Web of Science. Relevant keywords like “acute lymphoblastic leukemia,” “deep learning,” and “neural network” were employed, without time frame restrictions. Articles were included if they assessed the metrics of AI applications for ALL diagnosis in bone marrow aspirates. Articles were excluded if they: (1) had different outcomes, (2) were reviews, (3) were abstracts only, or (4) only reported peripheral blood smear metrics. Results: The search yielded 496 articles. After eliminating duplicates (282), title and abstract screening on the Rayyan platform excluded 204 records, leaving 214 articles eligible for full-text screening. Ten relevant articles were ultimately included in the review. Diverse approaches were presented to enhance diagnostic accuracy and efficiency. One study achieved 100% accuracy in classifying leukemic cells using a unique Convolutional Leaky RELU with CatBoost and XGBoost (CLR-CXG) design. Another study employed transfer learning with CNNs, obtaining 95.3% accuracy for AML, ALL, and CML classification with DenseNet121. Furthermore, the novel “i-Net” model demonstrated 99.18% validation accuracy for white blood cancer segmentation and classification. Adaptive Multi-objective CAT algorithms achieved 99.45% accuracy in detecting bone marrow cancer cells. An AI-based system using deep learning exhibited 97.2% accuracy in diagnosing ALL, while a robust fuzzy logic algorithm combined with a radial basis function neural network achieved 82.93% accuracy in diagnosing ALL in developing countries. Additionally, AI technologies based on computer microscopy demonstrated 95% and 97.5% accuracy in diagnosing ALL and minimal residual disease, respectively. A computer-aided system achieved 97.78% accuracy in classifying ALL subtypes. Conclusion: The potential of AI-driven approaches, particularly deep learning models like CNNs, in improving ALL diagnosis from bone marrow images is highlighted in this review. Because of their superior sensitivity and specificity over peripheral blood smears, bone marrow samples are the gold standard for ALL confirmation. The morphological and cellular properties of bone marrow samples provide important information for disease classification and monitoring. While DL and CNNs have shown promise in diagnosing ALL from peripheral blood smears, their use in bone marrow samples has yet to be investigated deeply. More research and external validation are required to fully realize AI's potential in ALL diagnosis from bone marrow specimens.

Chronic lymphocytic leukemia (CLL) is a B cell neoplasm characterized by the accumulation of aberrant monoclonal B lymphocytes. CLL is the predominant type of leukemia in Western countries, accounting for 25% of cases. Although many patients remain asymptomatic, a subset may exhibit typical lymphoma symptoms, acquired immunodeficiency disorders, or autoimmune complications. Diagnosis involves blood tests showing increased lymphocytes and further examination using peripheral blood smear and flow cytometry to confirm the disease. With the significant advancements in machine learning (ML) and artificial intelligence (AI) in recent years, numerous models and algorithms have been proposed to support the diagnosis and classification of CLL. In this review, we discuss the benefits and drawbacks of recent applications of ML algorithms in the diagnosis and evaluation of patients diagnosed with CLL.

Myelodysplastic syndrome (MDS) is composed of diverse hematological malignancies caused by dysfunctional stem cells, leading to abnormal hematopoiesis and cytopenia. Approximately 30% of MDS cases progress to acute myeloid leukemia (AML), a more aggressive disease. Early detection is crucial to intervene before MDS progresses to AML. The current diagnostic process for MDS involves analyzing peripheral blood smear (PBS), bone marrow sample (BMS), and flow cytometry (FC) data, along with clinical patient information, which is labor-intensive and time-consuming. Recent advancements in machine learning offer an opportunity for faster, automated, and accurate diagnosis of MDS. In this review, we aim to provide an overview of the current applications of AI in the diagnosis of MDS and highlight their advantages, disadvantages, and performance metrics.

Thalassemia is an autosomal recessive genetic disorder that affects the beta or alpha subunits of the hemoglobin structure. Thalassemia is classified as a hypochromic microcytic anemia and a definitive diagnosis of thalassemia is made by genetic testing of the alpha and beta genes. Thalassemia carries similar features to the other diseases that lead to microcytic hypochromic anemia, particularly iron deficiency anemia (IDA). Therefore, distinguishing between thalassemia and other causes of microcytic anemia is important to help in the treatment of the patients. Different indices and algorithms are used based on the complete blood count (CBC) parameters to diagnose thalassemia. In this article, we review how effective artificial intelligence is in aiding in the diagnosis and classification of thalassemia.

Chronic myeloid leukemia (CML) is a myeloproliferative neoplasm characterized by dysregulated growth and the proliferation of myeloid cells in the bone marrow caused by the BCR-ABL1 fusion gene. Clinically, CML demonstrates an increased production of mature and maturing granulocytes, mainly neutrophils. When a patient is suspected to have CML, peripheral blood smears and bone marrow biopsies may be manually examined by a hematologist. However, confirmatory testing for the BCR-ABL1 gene is still needed to confirm the diagnosis. Despite tyrosine kinase inhibitors (TKIs) being the mainstay of treatment for patients with CML, different agents should be used in different patients given their stage of disease and comorbidities. Moreover, some patients do not respond well to certain agents and some need more aggressive courses of therapy. Given the innovations and development that machine learning (ML) and artificial intelligence (AI) have undergone over the years, multiple models and algorithms have been put forward to help in the assessment and treatment of CML. In this review, we summarize the recent studies utilizing ML algorithms in patients with CML. The search was conducted on the PubMed/Medline and Embase databases and yielded 66 full-text articles and abstracts, out of which 11 studies were included after screening against the inclusion criteria. The studies included show potential for the clinical implementation of ML models in the diagnosis, risk assessment, and treatment processes of patients with CML.

Aim : The objective of this research is to review the bleeding risks in patients with atrial fibrillation (Afib) and immune thrombocytopenia (ITP) and explore various management strategies to formulate an optimal therapeutic approach that improves the prognosis of these patients. Methods: In this study, we devised our search approach by utilizing PubMed's Medical Subject Headings (MeSH) terms and incorporating pertinent keywords extracted from article titles and abstracts. To ensure a comprehensive scope, we integrated terms associated with immune thrombocytopenia purpura, such as “Immune Thrombocytopenia,” and similar expressions. Additionally, terms related to atrial fibrillation, including “atrial fibrillation,” were included to identify articles discussing atrial fibrillation in the context of immune thrombocytopenia. To adapt the initial search strategy for Embase, Web of Science, and Scopus databases, we employed a polyglot translator. All identified studies resulting from the search strategy were imported into EndNote, where duplicate articles were meticulously removed. The remaining studies were then transferred to Rayyan for further duplicate elimination and to initiate the screening process. The study incorporates full-text articles, submitted abstracts, and conference abstracts. Excluded from this analysis were studies falling into the following categories: (1) animal studies, (2) reviews or non-original articles, and (3) non-English articles. Results: A total of 1891 articles were retrieved from four databases, and after careful selection, 14 relevant full-text articles were analyzed. Among these, 12 studies investigated the efficacy of anticoagulants in managing cases of concurrent Afib and ITP as well as their bleeding risks. Comparing bleeding risk between groups was usually done through measuring by CHA 2DS 2 VASc or HAS BLED scoring systems and survival was also compared by using Kaplan-Meier curves A few of them also compared the efficacy and safety of warfarin against Non-Vitamin K Antagonists which has shown that Non-Vitamin K Antagonists have a lower event rate of major bleeding, lower hazard ration of systemic embolism and higher survival rate compared to warfarin The remaining two papers explored the efficacy and safety of surgical interventions, specifically left atrial appendage closure. Many studies reported that anticoagulant therapies were associated with reduced bleeding risk and have shown promising outcomes in managing patients with Afib and ITP. Additionally, percutaneous left atrial appendage closure has been reported to be a potentially safe management option for atrial fibrillation in patients with primary hemostatic disorders, including thrombocytopenia. Conclusion: In essence, this review focused on investigating the bleeding risk associated with atrial fibrillation (Afib) in patients with immune thrombocytopenia (ITP) and examining available management strategies. While the precise pathophysiological mechanisms linking atrial fibrillation and ITP remain incompletely understood, the review emphasized the intricate interplay between thromboembolic risk and bleeding complications in this specific patient group. The findings suggest that certain anticoagulation regimens and also left atrial appendage closure have shown promising safety and effectiveness in Afib patients with thrombocytopenia. Nevertheless, further research with larger sample sizes and diverse populations is needed to enhance our understanding of the Afib-ITP relationship. Such studies will yield more definitive conclusions and valuable insights into optimal anticoagulation approaches and alternative therapies for patients with both Afib and ITP.

OBJECTIVES:This study aimed to identify the clinicopathological characteristics and prognostic value of CC chemokine receptor 7 (CCR7) expression in patients with head and neck squamous cell carcinoma (HNSSC).METHODS:The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed in this meta-analysis. Up to the 2nd of July 2022 a search was conducted using five databases: PubMed, Embase, Scopus, ProQuest, and Web of Science. The methodological standards for the epidemiological research scale were used to assess the quality of the included articles, and Stata software was used to synthesize the meta-analysis.RESULTS:We considered 13 of the 615 studies which included 1005 HNSCC patients. High expression of CCR7 increased the pooled odds ratio (OR) of advanced stage, tumor size, metastasis and recurrence by 2.82 [95% confidence interval (CI) 1.84 to 4.33], 2.48 (95% CI 1.68, to 3.67), 3.57, 95% CI 2.25 to 5.05) and 3.93 (95% CI 2.03 to 7.64), respectively. High CCR7 reduced overall patient survival [hazard ratio 2.62 (95% CI 1.59 to 4.32)].CONCLUSION:This study showed that high expression of CCR7 in HNSCC tumors was significantly associated with worse clinicopathological and survival outcomes, suggesting that CCR7 and its pathway could be potential therapeutic strategies for HNSCC.

Acute lymphoblastic leukemia (ALL) poses a significant health challenge, particularly in pediatric cases, requiring precise and rapid diagnostic approaches. This comprehensive review explores the transformative capacity of deep learning (DL) in enhancing ALL diagnosis and classification, focusing on bone marrow image analysis. Examining ten studies conducted between 2013 and 2023 across various countries, including India, China, KSA, and Mexico, the synthesis underscores the adaptability and proficiency of DL methodologies in detecting leukemia. Innovative DL models, notably Convolutional Neural Networks (CNNs) with Cat-Boosting, XG-Boosting, and Transfer Learning techniques, demonstrate notable approaches. Some models achieve outstanding accuracy, with one CNN reaching 100% in cancer cell classification. The incorporation of novel algorithms like Cat-Swarm Optimization and specialized CNN architectures contributes to superior classification accuracy. Performance metrics highlight these achievements, with models consistently outperforming traditional diagnostic methods. For instance, a CNN with Cat-Boosting attains 100% accuracy, while others hover around 99%, showcasing DL models’ robustness in ALL diagnosis. Despite acknowledged challenges, such as the need for larger and more diverse datasets, these findings underscore DL’s transformative potential in reshaping leukemia diagnostics. The high numerical accuracies accentuate a promising trajectory toward more efficient and accurate ALL diagnosis in clinical settings, prompting ongoing research to address challenges and refine DL models for optimal clinical integration.

The accurate diagnosis of small-cell lung cancer (SCLC) is crucial, as treatment strategies differ from those of other lung cancers. This systematic review aims to identify proteins differentially expressed in SCLC compared to normal lung tissue, evaluating their potential utility in diagnosing and prognosing the disease. Additionally, the study identifies proteins differentially expressed between SCLC and large cell neuroendocrine carcinoma (LCNEC), aiming to discover biomarkers distinguishing between these two subtypes of neuroendocrine lung cancers. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a comprehensive search was conducted across PubMed/MEDLINE, Scopus, Embase, and Web of Science databases. Studies reporting proteomics information and confirming SCLC and/or LCNEC through histopathological and/or cytopathological examination were included, while review articles, non-original articles, and studies based on animal samples or cell lines were excluded. The initial search yielded 1705 articles, and after deduplication and screening, 16 articles were deemed eligible. These studies revealed 117 unique proteins significantly differentially expressed in SCLC compared to normal lung tissue, along with 37 unique proteins differentially expressed between SCLC and LCNEC. In conclusion, this review highlights the potential of proteomics technology in identifying novel biomarkers for diagnosing SCLC, predicting its prognosis, and distinguishing it from LCNEC.

Introduction: Chronic lymphocytic leukemia (CLL) and small lymphocytic lymphoma (SLL) are closely related diseases with similar characteristics, classified as mature B cell neoplasms. They involve the accumulation of monoclonal B lymphocytes and are considered as one disease with different manifestations. CLL is more prevalent and is usually detected through routine blood tests, while SLL is identified by primary lymph node involvement. Although many patients with CLL/SLL are asymptomatic, some may experience lymphoma-related symptoms. The diagnosis involves blood tests and immunophenotypic analysis. CLL prognosis varies, and staging systems help in determining the appropriate treatment regimen. Machine learning (ML) is being increasingly used in hematology to aid in diagnosis, treatment, and risk-stratification. This review explores the application of ML in the classification and diagnosis of CLL, discussing the performance and limitations of existing models, with the goal of encouraging further research and integration into clinical settings to improve patient care. Materials and Methods: A literature search was conducted using the PubMed/MEDLINE and EMBASE databases. EndNote and Rayyan software were used to eliminate duplicate entries and for screening the articles. In addition, the references of the identified articles were manually screened to identify additional relevant studies. Articles with primary data pertaining to the use of ML in CLL/SLL diagnosis and classification were included without language or time restrictions. Results: A total of 169 articles were identified and duplicates were removed using Endnote® and Rayyan® software, resulting in 149 articles eligible for screening. The included articles met specific criteria: they utilized ML methods, reported conclusions on the method's reliability or accuracy, and focused on CLL diagnosis and classification. Excluded articles were non-English, animal/in vitro studies, abstracts, and review articles. After screening, 14 studies met the inclusion criteria. Studies examining the potential of ML algorithms in enhancing the diagnosis of CLL used various data sources like blood smears, flow cytometry, genetic data, and histopathological images. The current evidence suggests that ML can effectively predict CLL diagnosis, aid in screening, identify potential biomarkers, and explore the underlying molecular mechanisms. Many of the ML models evaluated in this review demonstrate high accuracy, ranging from 83% to 100%. Implementing AI and ML in hematology can automate tasks involved in patient workup, risk assessment, and treatment, allowing hematologists to focus on critical aspects of patient care and research. Despite the promising potential, some important considerations should be acknowledged. First, many reviewed models had limited sample sizes from single centers, limiting their generalizability. It's crucial to develop models using larger, diverse datasets from multiple centers to improve generalizability. Second, there is a lack of prospective studies and research on the impact of ML models on patient outcomes. Future investigations should focus on prospectively assessing the effect of ML models on CLL diagnosis, prognosis, and patient outcomes. Third, the integration of ML applications into direct patient care raises ethical and medico-legal concerns, including liability, data privacy, and doctor-ML application interaction. An ethical framework specific to ML applications in healthcare is needed, and doctors should receive training on ML applications to understand their capabilities and limitations. Addressing these considerations can lead to successful implementation of ML applications in the care of CLL patients, improving diagnosis and patient outcomes. Conclusion: Our findings demonstrate the promising potential of ML algorithms in accurately predicting CLL diagnosis, conducting CLL screening, and identifying genetic biomarkers associated with CLL. Future research endeavors should prioritize the development of large, standardized datasets to effectively train ML models, conduct prospective evaluations to assess their performance, explore their impact on patient outcomes, and establish ethical frameworks to govern their utilization.

Artificial intelligence (AI) is rapidly becoming an established arm in medical sciences and clinical practice in numerous medical fields. Its implications have been rising and are being widely used in research, diagnostics, and treatment options for many pathologies, including sickle cell disease (SCD). AI has started new ways to improve risk stratification and diagnosing SCD complications early, allowing rapid intervention and reallocation of resources to high-risk patients. We reviewed the literature for established and new AI applications that may enhance management of SCD through advancements in diagnosing SCD and its complications, risk stratification, and the effect of AI in establishing an individualized approach in managing SCD patients in the future. Aim: to review the benefits and drawbacks of resources utilizing AI in clinical practice for improving the management for SCD cases.

Purpose:The relationship between subclinical hypothyroidism and type 2 diabetes mellitus (T2DM) in Qatar is under-studied, despite the high prevalence of diabetes in the region. This study evaluates the potential association between subclinical hypothyroidism and T2DM in Qatar.Patients and Methods:A cross-sectional study used participants with and without T2DM from the Qatar Biobank (QBB). Logistic regression analysis was used to assess the association between subclinical hypothyroidism and T2DM, with multivariable logistic regression used to adjust for potential confounders.Results:The study found that subclinical hypothyroidism was significantly associated with a 2.82 increase in the odds of having T2DM (OR=2.82, 95% CI (1.13, 7.02), p=0.026) after adjusting for potential confounders. The proportion of subclinical hypothyroidism among individuals with T2DM in Qatar was 4.6%, significantly higher than in those without T2DM (2.8%, p=0.18).Conclusion:This study demonstrates a significant association between subclinical hypothyroidism and T2DM in Qatar. Further research is required to investigate the directionality of this association and its clinical implications.

Philadelphia-negative (Ph-) myeloproliferative neoplasms (MPNs) are a group of hematopoietic malignancies identified by clonal proliferation of blood cell lineages and encompasses polycythemia vera (PV), essential thrombocythemia (ET), and primary myelofibrosis (PMF). The clinical and laboratory features of Philadelphia-negative MPNs are similar, making them difficult to diagnose, especially in the preliminary stages. Because treatment goals and progression risk differ amongst MPNs, accurate classification and prognostication are critical for optimal management. Artificial intelligence (AI) and machine learning (ML) algorithms provide a plethora of possible tools to clinicians in general, and particularly in the field of malignant hematology, to better improve diagnosis, prognosis, therapy planning, and fundamental knowledge. In this review, we summarize the literature discussing the application of AI and ML algorithms in patients with diagnosed or suspected Philadelphia-negative MPNs. A literature search was conducted on PubMed/MEDLINE, Embase, Scopus, and Web of Science databases and yielded 125 studies, out of which 17 studies were included after screening. The included studies demonstrated the potential for the practical use of ML and AI in the diagnosis, prognosis, and genomic landscaping of patients with Philadelphia-negative MPNs.