Artificial intelligence applications in histopathological diagnostics are rapidly expanding, with particular promise in complex hematological malignancies where diagnostic accuracy remains challenging and subjective. This study undertakes a scoping review to systematically map the extent of research on artificial intelligence applications in histopathological diagnostics of leukemias, examine geographic distribution and methodological approaches, and assess the current state of AI model performance and clinical readiness. A comprehensive search was conducted in the Scopus database covering publications from 2018 to 2025 (as of 12 July 2025), using five targeted search strategies combining AI, histopathology, and leukemia-related terms. Following a three-stage screening protocol, 418 publications were selected from an initial pool of over 75,000 records across multiple countries and research domains. The analysis revealed a marked increase in research output, peaking in 2024 with substantial contributions from India (26.3%), China (17.9%), USA (13.8%), and Saudi Arabia (11.1%). Among 43 documented datasets ranging from 80 to 42,386 images, studies predominantly utilized convolutional neural networks and deep learning approaches. AI models demonstrated high diagnostic accuracy, with 25 end-to-end models achieving an average accuracy of 97.72% compared to 96.34% for 20 classical machine learning approaches. Most studies focused on acute lymphoblastic leukemia detection and subtype classification using blood smear and bone marrow specimens. Despite promising diagnostic performance, significant gaps remain in clinical translation, standardization, and regulatory approval, with none of the reviewed AI systems currently FDA-approved for routine leukemia diagnostics. Future research should prioritize clinical validation studies, standardized datasets, and integration with existing diagnostic workflows to realize the potential of AI in hematopathological practice.

This review summarises potential applications of artificial intelligence (AI) in histopathological diagnostics of leukemias reported in the available literature. It compares existing AI models focused on analysing image-based cellular morphology, trained on datasets of either blood smears or bone marrow slides. Key findings indicate a rising trend in research output and literature on that topic, with models achieving high accuracy rates — up to over 95% in leukemia detection and subtype classification. The implications suggest that AI can significantly enhance diagnostic precision, reduce subjectivity, and streamline workflows in hematopathology. Possible limitations and difficulties of introducing AI to routine diagnostics are also elaborated on. Overall, integrating AI into leukemia diagnostics holds promise for improving early detection, supporting clinical decision-making, and advancing treatment in haematological malignancies.

Background: Artificial intelligence (AI) shows promising results in lymphoma detection, prediction, and classification. However, translating these findings into practice requires a rigorous assessment of potential biases, clinical utility, and further validation of research models. Objective: The goal of this study was to summarize existing studies on artifi- cial intelligence models for the histopathological detection of lymphoma. Design: This study adhered to the PRISMA Extension for Scoping Reviews (PRISMA-ScR) guidelines. A systematic search was conducted across three major databases (Scopus, PubMed, Web of Science) for English-language articles and reviews published between 2016 and 2025. Seven precise search queries were applied to identify relevant publications, accounting for variations in study modality, algorithmic architectures, and disease-specific terminology. Results: The search identified 615 records, of which 36 articles met the inclusion criteria. These studies presented 36 AI models, comprising 30 diagnostic and 6 prognostic applica- tions, with Convolutional Neural Networks (CNNs) being the predominant architecture. Regarding data sources, 83% (30/36) of datasets utilized Hematoxylin and Eosin (H&E) stained images, while the remainder relied on diverse modalities, including IHC stained slides, bone marrow smears, and other tissue preparations. Studies predominantly utilized retrospective, private cohorts with sample sizes typically ranging from 50 to 400 patients; only a minority leveraged open-access repositories (e.g., Kaggle, TCGA). The primary appli- cation was slide-level multi-class classification, distinguishing between specific lymphoma subtypes and non-neoplastic controls. Beyond diagnosis, a subset of studies explored advanced prognostic tasks, such as predicting chemotherapy response and disease progres- sion (e.g., in CLL), as well as automated biomarker quantification (c-MYC, BCL2, PD-L1). Reported diagnostic performance was generally high, with accuracy ranging from 60% to 100% (clustering around 90%) and AUC values spanning 0.70 to 0.99 (predominantly >0.90). Conclusions: While AI models demonstrate high diagnostic accuracy, their translation into practice is limited by unstandardized protocols, morphological complexity, and the "black box" nature of algorithms. Critical issues regarding data provenance, image noise, and lack of representativeness raise risks of systematic bias, hence the need for rigorous validation in diverse clinical environments.

Background: Artificial intelligence (AI) shows promising results in lymphoma detection, prediction, and classification. However, translating these findings into practice requires a rigorous assessment of potential biases, clinical utility, and further validation of research models. Objective: The goal of this study was to summarize existing studies on artificial intelligence models for the histopathological detection of lymphoma. Design: This study adhered to the PRISMA Extension for Scoping Reviews (PRISMA-ScR) guidelines. A systematic search was conducted across three major databases (Scopus, PubMed, Web of Science) for English-language articles and reviews published between 2016 and 2025. Seven precise search queries were applied to identify relevant publications, accounting for variations in study modality, algorithmic architectures, and disease-specific terminology. Results: The search identified 612 records, of which 36 articles met the inclusion criteria. These studies presented 36 AI models, comprising 30 diagnostic and six prognostic applications, with Convolutional Neural Networks (CNNs) being the predominant architecture. Regarding data sources, 83% (30/36) of datasets utilized Hematoxylin and Eosin (H&E)-stained images, while the remainder relied on diverse modalities, including IHC-stained slides, bone marrow smears, and other tissue preparations. Studies predominantly utilized retrospective, private cohorts with sample sizes typically ranging from 50 to 400 patients; only a minority leveraged open-access repositories (e.g., Kaggle, TCGA). The primary application was slide-level multi-class classification, distinguishing between specific lymphoma subtypes and non-neoplastic controls. Beyond diagnosis, a subset of studies explored advanced prognostic tasks, such as predicting chemotherapy response and disease progression (e.g., in CLL), as well as automated biomarker quantification (c-MYC, BCL2, PD-L1). Reported diagnostic performance was generally high, with accuracy ranging from 60% to 100% (clustering around 90%) and AUC values spanning 0.70 to 0.99 (predominantly >0.90). Conclusions: While AI models demonstrate high diagnostic accuracy, their translation into practice is limited by unstandardized protocols, morphological complexity, and the “black box” nature of algorithms. Critical issues regarding data provenance, image noise, and lack of representativeness raise risks of systematic bias, hence the need for rigorous validation in diverse clinical environments.

Menisci play a crucial biomechanical, stabilizing, sensory, and nutritional role in the knee joint, and their limited vascularization influences healing potential and treatment strategies. Meniscal tears are common, especially alongside anterior cruciate ligament (ACL) injuries, with characteristic tear types including bucket-handle, radial, flap, longitudinal, and horizontal lesions differing in frequency, treatment difficulty, and prognosis. Elite athletes are particularly vulnerable to meniscal injuries due to high mechanical loads, pivoting movements, contact mechanisms, and sport-specific demands; the risk further increases with concomitant ACL rupture and delayed reconstruction. Diagnosis requires a combination of clinical assessment and imaging, with MRI remaining the gold standard, and arthroscopy indicated in complex or uncertain cases. Treatment prioritizes meniscal preservation through repair whenever possible, as it offers better long-term joint protection despite longer rehabilitation compared to partial meniscectomy. Rehabilitation in elite athletes follows phased protocols emphasizing early protection, progressive loading, neuromuscular training, and functional testing to guide return-to-sport decisions. Preventive strategies focus on proprioceptive training and load management. Overall, optimal outcomes in athletes depend on accurate injury characterization, tissue-preserving treatment, and individualized rehabilitation aimed at restoring full function and minimizing long-term joint degeneration.

Swimming is a comprehensive form of physical activity that provides significant physiological and psychological benefits. This review analyzes data from studies conducted between 1990 and 2024 concerning the effects of swimming on cardiovascular and respiratory function, musculoskeletal health, metabolism and mental well-being. Regular swimming enhances cardiac output, pulmonary function and muscle strength while supporting favorable metabolic adaptations such as improved lipid profiles and insulin sensitivity. Although it has a limited effect on bone mineral density compared to weight-bearing activities, it contributes to overall musculoskeletal stability and functional fitness. Furthermore, swimming reduces stress, anxiety and depressive symptoms, and supports cognitive health by preserving hippocampal volume in older adults. Long-term engagement in swimming is associated with improved mobility, reduced fall risk and lower all-cause mortality, confirming its role as an effective strategy for promoting longevity.