INTELLIGENZA ARTIFICIALE IN AMBITO SANITARIO E FARMACEUTICO

1. The application of artificial intelligence in systemic lupus erythematosus: a bibliometric analysis of current trends and future directions

   CONCLUSION: AI has matured from methodological exploration into a robust clinical decision-support tool for SLE, particularly for lupus nephritis assessment and flare prediction. Future advancements rely on conducting prospective clinical validations in real-world cohorts, predicting personalized drug efficacies, and leveraging multi-omics to decode pathological mechanisms. These steps are essential to transition SLE management from traditional empirical approaches to data-driven precision...

2. Artificial intelligence in rheumatoid arthritis: current applications and future perspectives

   Rheumatoid arthritis (RA) is a highly prevalent systemic autoimmune disease characterized by a complex and partially understood pathogenesis. The substantial challenges in early identification and marked therapeutic heterogeneity pose a significant burden on affected patients. Despite notable advancements in diagnostic techniques and therapeutic interventions in recent years, optimal patient care remains hindered by several ongoing clinical challenges. To address these limitations, artificial...

3. Machine learning-assisted mRNA vaccine pharmacovigilance: a systematic review of multi-source real-world data

   CONCLUSION: ML-assisted pharmacovigilance enables a shift from passive to active, intelligent monitoring. Despite challenges in data quality, model interpretability, and regulatory approval, intelligent pharmacovigilance systems will become essential infrastructure for safeguarding public health.

4. Integrated meta-analysis and exploratory small-sample machine learning to evaluate curcumin against osteoporosis: a preclinical evidence-based study

   CONCLUSION: Curcumin exerts potent, multi-target osteoprotective effects that are associated with improved bone remodeling and oxidative stress-related indices. The exploratory integration of ML with meta-analysis suggested that biological characteristics and dosage may contribute to variability in treatment effects. However, because the ML component was constrained by the limited number of study-level observations, these model-derived findings should be regarded as hypothesis-generating signals...

5. Comment on: "A comprehensive landscape of AI applications in broad-spectrum drug interaction prediction: a systematic review" (Marzouk et al., 2025)

   Marzouk et al. reviewed 147 studies on artificial intelligence (AI) applications for predicting drug-drug, drug-disease, and drug-nutrient interactions, providing a broad overview of current machine learning and deep-learning approaches. However, several methodological and conceptual limitations reduce the reproducibility and interpretability of the review. The search strategy appears largely restricted to PubMed with title- and abstract-level filtering, while manual record removal is reported...

6. Artificial intelligence-driven prediction of neoadjuvant chemotherapy response in adult patients with gastric cancer: a systematic review and meta-analysis

   CONCLUSIONS: Across 17 studies, AI models demonstrated moderate to good discrimination for predicting NAC response in gastric cancer, with pooled AUCs of 0.844 (95% CI 0.812 to 0.877) for internal validation and 0.812 (95% CI 0.775 to 0.848) for external validation. Model-methodology subgroup analyses did not demonstrate robust differences between deep learning and machine-learning approaches, and AI models significantly outperformed traditional clinical assessment. However, GRADE-rated...

7. Artificial Intelligence and Machine Learning-based prediction of tuberculosis treatment failure: A systematic review and meta-analysis

   CONCLUSIONS: AI/ML models for predicting TB treatment failure show promising discrimination but are not yet ready for routine clinical implementation. Performance varies substantially across populations and settings, and methodological limitations, including inadequate validation, poor calibration assessment, and high risk of bias, limit confidence in current estimates. Future research should prioritize rigorous external validation, calibration assessment, and development in underrepresented...

8. Large Language Models in Adverse Drug Reaction Detection and Pharmacovigilance: A Systematic Review of Current Applications, Challenges, and Future Directions

   Background/Objectives: Pharmacovigilance workflows rely heavily on unstructured text across diverse sources. Here, we systematically reviewed how large language models (LLMs) are being explored as support tools for adverse drug reaction (ADR) detection, extraction, triage, and documentation, highlighting their potential for precision medicine and big data-enabled safety monitoring. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines, we...

9. Current status and research hotspots of pediatric nephrotic syndrome: a bibliometric analysis (2011-2025)

   CONCLUSION: This study conducted a systematic bibliometric evaluation of pediatric NS, and clarified its current research status and identified future research hotspots and development trends. The exploration of novel immunosuppressants and the elucidation of complex pathogenic mechanisms remain enduring hotspots in the evolving landscape of pediatric NS.

10. In Silico Modeling of Nanoparticle Transport across the Blood-Brain Barrier: A Systematic Review

    Reliable prediction of nanoparticle (NP) transport across the blood-brain barrier (BBB) is essential for designing effective central nervous system-targeted drug delivery systems. The BBB protects the brain but severely restricts the entry of therapeutic compounds, and fewer than 5% of candidate drugs reach the brain in pharmacologically meaningful amounts. NP-based delivery systems have emerged as a promising approach to overcome this limitation by enhancing drug stability, circulation, and BBB...

11. Optimizing Treatment Strategies in the Bipolar Disorder Spectrum With Classical AI Approaches: Systematic Review of Performance, Bias, and Clinical Applicability

    CONCLUSIONS: The adoption of classical AI tools in BD serves as a driver for therapeutic optimization, although current AI tools in BD should still be considered exploratory rather than ready for clinical use. Effective implementation in real-world clinical scenarios requires more robust, transparent, and externally validated models to ensure reliability and generalizability.

12. Precipitants and clinical features of serotonin syndrome: a systematic review with patient-level analysis of published case reports and series

    CONCLUSIONS: Non-suicidal cases of SS during regular prescription were less serious than SS cases associated with intentional overdose. The emerging role of non-antidepressant agents (e.g., several opioids and antiparkinsonian drugs) as potential precipitants support tailored interprofessional medication review in poly-medicated subjects.

13. Beyond the algorithm: health technology assessment frameworks for AI in cardiology under the European Union Health Technology Assessment Regulation: a systematic review

    CONCLUSIONS: While AI tools in cardiology show increasing promise, current HTA practices do not yet fully align with the regulatory and methodological expectations of the EU HTAR. Adapted evaluation models are needed to support the effective, evidence-based adoption of AI technologies in cardiovascular care.

14. The role of AI-assisted drug repurposing in neurological disorders: a systematic review of validation strategies, challenges and opportunities

    Neurological disorders refer to a diverse group of conditions that affect the brain, peripheral nerves, and spinal cord and impair socioemotional, cognitive, motor, and sensory functions. Alzheimer's disease (AD), Multiple Sclerosis (MS), Parkinson's disease (PD), Huntington's disease (HD), and Amyotrophic Lateral Sclerosis (ALS) are some of the well-known neurodegenerative diseases that affect millions of people worldwide. Despite the advanced technologies and nano-drug delivery systems, the...

15. Artificial Intelligence for Evidence Synthesis of Emerging Biologics to Improve Skeletal Health in Osteogenesis Imperfecta: Systematic Review and Meta-Analysis

    CONCLUSIONS: This review is the first to synthesize and quantitatively compare skeletal outcomes across multiple biologics in OI with an AI-assisted review workflow. Denosumab and setrusumab demonstrate promising efficacy in improving lumbar spine aBMD across ages, although current evidence does not support superior fracture reduction over bisphosphonates. GPT-4o can substantially accelerate evidence synthesis but should be deployed with explicit human oversight in tasks requiring contextual...

16. Prediction of PD-L1 expression in nonsmall cell lung cancer using artificial intelligence models based on radiomics: a systematic review and meta-analysis

    CONCLUSIONS: Imaging modality and algorithm model are key factors influencing AI prediction models for PD-L1 expression in NSCLC patients.

17. The impact of hyperglycaemia and/or type 2 diabetes on women with breast cancer undergoing or post-cytotoxic chemotherapy: a systematic literature review

    CONCLUSION: Proactive identification and rigorous management of hyperglycaemia and/or T2D are essential to reducing complications and improving outcomes in women with BC receiving chemotherapy. Evidence demonstrates that poor glycaemic control clearly impairs treatment response. The current research gap and fragmented care pathways demand strengthened multidisciplinary collaboration and the delivery of personalised care. These measures are necessary to significantly improve the quality of living...

18. Research trends and hotspots in sclerotherapy for vascular malformations: bibliometric and visual analyses

    INTRODUCTION: Vascular malformations are a group of congenital vascular developmental anomalies. According to the International Society for the Study of Vascular Anomalies classification, they are mainly divided into Slow-Flow and Fast-Flow lesions. Slow-Flow lesions are represented by venous malformations, lymphatic malformations, and capillary malformations; Fast-Flow lesions primarily include arteriovenous malformations and arteriovenous fistulae. As a minimally invasive interventional...

19. Unveiling the efficacy predictors and potential mechanisms of Semen Cuscutae against osteoporosis via machine learning and meta-analysis: a preclinical study

    CONCLUSION: Semen Cuscutae exerts robust osteoprotective effects via coupled anti-inflammatory and osteogenic mechanisms. The exploratory ML-derived ranking suggests that species-specific dose scaling and treatment duration may be important considerations for future preclinical and translational studies of Semen Cuscutae.

20. Leprosy masquerading as systemic lupus erythematosus: a case report and systematic review of the literature

    CONCLUSION: Leprosy should be considered in the differential diagnosis of SLE, particularly in patients presenting with cutaneous and articular manifestations accompanied by peripheral neuropathy and poor response to immunosuppressive therapy. By delineating recurring clinical patterns and diagnostic pitfalls, our findings provide practical clues for earlier recognition, helping to prevent diagnostic delay, inappropriate immunosuppression, and adverse outcomes.

21. Cardioprotective role of antihypertensive treatment in chemotherapy-induced cardiotoxicity: umbrella review of meta-analyses of randomised controlled trials

    CONCLUSIONS: ACEIs and beta blockers appear to confer modest cardioprotective effects during chemotherapy, particularly regarding systolic function and heart failure incidence, although with predominantly low-certainty evidence. Larger, robust RCTs with standardised endpoints are required before routine prophylactic use is recommended.

22. Methodological challenges in machine learning and deep learning applied to food analysis: A critical review

    Artificial Intelligence (AI) is increasingly applied for food quality control, authenticity assessment, and chemical profiling. However, the reliability and industrial applicability of Machine Learning (ML) and Deep Learning (DL) models critically depend on how the datasets are constructed, validated, and interpreted. Among the different analytical techniques used in food analysis, chromatographic fingerprints and chromatographic hyphenated techniques typically generate high-dimensional datasets...

23. Higher disease reactivation risk in women after fingolimod withdrawal

    BACKGROUND: Disease reactivation following cessation of sphingosine 1-phosphate receptor modulators (S1PRM) occurs in ~ 10% of multiple sclerosis (MS) patients. The biological factors underlying this phenomenon remain incompletely understood, including the potential contribution of sex-specific differences.

24. Global distribution, clinical characteristics, and outcomes of human intestinal capillariasis, 2000-2025: a systematic review

    CONCLUSIONS: Intestinal capillariasis is an underrecognized foodborne infection with a consistent clinical and laboratory profile across study designs. Early diagnosis through repeated stool examination and timely treatment with benzimidazoles is critical to improving outcomes. Strengthening food safety practices and enhancing surveillance are essential to reduce disease burden.

25. Hypertension and myocardial fibrosis: A systematic review and meta-analysis

    CONCLUSIONS: The extent of MF is closely associated with various factors, underscoring the importance of its identification in hypertensive patients. Histology and T1 mapping parameters are effective for quantifying MF. Due to the variable effects of antihypertensive medications on MF, the use of specific agents to mitigate MF in hypertensive patients is recommended.

26. Application of Digital Twin Technology to Enhance Chronic Diseases Management: A Systematic Review

    CONCLUSIONS: Our findings demonstrate that DT technology has evolved from theoretical models to integrated clinical applications, with the potential to revolutionize healthcare through personalized medicine, continuous monitoring, and AI-driven decision support.

27. PARP1 as a novel therapeutic and diagnostic tool in autoimmune rheumatic diseases: a systematic literature review

    ADP-ribosylation is a reversible post-translational modification regulated by poly(ADP-ribose) polymerases (PARPs), a family of enzymes involved in DNA repair, transcriptional regulation, and immune responses. Among the 17 known PARP family members, PARP1 is the most extensively studied in autoimmune rheumatic diseases (ARDs). Although increasing evidence implicates PARP1 in ARD pathogenesis, its potential diagnostic and therapeutic relevance has not been systematically synthesised. This...

28. Artificial Intelligence for Antimicrobial Resistance Detection and Prediction in Klebsiella pneumoniae: A Systematic Review of Clinical Microbiology Applications

    CONCLUSION: AI-based AMR prediction and detection in K. pneumoniae is advancing rapidly, with MALDI-TOF-enabled approaches appearing most readily translatable to clinical microbiology workflows. However, the field remains dominated by retrospective, internally validated studies, often using imperfect automated susceptibility systems as reference standards. Progress now depends on rigorous external and prospective multicentre validation using geographically diverse datasets.

29. Artificial intelligence in anaesthesiology: why don't we have it in our hands after a decade of innovation? A systematic review and perspective

    CONCLUSIONS: Machine learning represents a highly active and debated domain in anaesthesia, characterised by a substantial volume of published research. However, exceptionally few algorithmic models have successfully translated into practice-changing clinical tools. This persistent gap indicates that technical innovation alone is insufficient, as translation is severely hindered by inherent model limitations, software interoperability constraints, and socio-technical challenges within the...

30. Strategies for mitigating artificial intelligence bias in healthcare: a systematic review

    CONCLUSION: There is a significant opportunity for model developers and end users to identify and reduce bias, particularly during model design. When evaluating strategy effectiveness, efforts should be measured using evidenced-based fairness metrics-such as group-based metrics-to ensure effectiveness and interpretability.

31. Systematic review of artificial intelligence use in behavioral analysis of invertebrate and larval model organisms: methods, applications and future recommendations

    Invertebrate and larval model organisms such as Drosophila melanogaster, Caenorhabditis elegans, Danio rerio larvae, and Galleria mellonella are increasingly employed in biomedical, toxicological, and ecological research. Their behavioral responses serve as sensitive indicators of functional changes, yet traditional methods of observation remain low-throughput, subjective, and poorly scalable. Artificial intelligence (AI), including machine learning (ML) and deep learning (DL), has emerged as a...

32. Global Trends in Integrating Machine Learning (ML) with Model-Informed Drug Development (MIDD): A Bibliometric and Systematic Review (2015-2025)

    Background/Objectives: The integration of machine learning (ML) within model-informed drug development (MIDD) represents a rapidly evolving paradigm in pharmacometrics, enabling improved prediction, optimization, and regulatory decision-making across drug development pipelines. However, the extent to which ML methods are explicitly integrated into regulatory decision-making remains limited and unevenly characterized. This study aims to systematically map the ML-MIDD scholarly landscape, identify...

33. Vitamin D Supplementation in Children with Asthma: An Umbrella Review

    CONCLUSIONS: This umbrella review found no convincing evidence that vitamin D supplementation improves asthma control, reduces exacerbations, or enhances lung function in children with asthma, despite its effect on increasing serum 25-hydroxyvitamin D levels and a possible benefit for asthma recurrence. However, these findings should be interpreted with caution, considering that the available evidence was limited by generally low methodological quality, substantial overlap among meta-analyses,...

34. The Organoid Decade: Leveraging 3D Patient-Derived Organoids to Bridge the Translational Gap in Triple-Negative Breast Cancer: A Systematic Review

    Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer with limited effective therapies. Two-dimensional (2D) in vitro models poorly recapitulate tumor microenvironment (TME) interactions, impeding the translational relevance of TNBC immunotherapy research. Three-dimensional patient-derived tumor organoids (3D PDTOs) have emerged as advanced preclinical models that better mimic tumor-immune interactions. The objective of this systematic review was to assess the landscape...

35. Receipt of Medicines Information From the Internet and Other Information Sources Among Adult Medicine Users in Developed Economies, 2010-2025: Systematic Review

    CONCLUSIONS: Traditional MI sources remain central for adult medicine users despite the growing role of electronic platforms. While the receipt of MI from electronic sources appears more common among internet-experienced users, no significant temporal trends in using these sources were identified. Further research is needed to better distinguish between different digital MI sources, including artificial intelligence-based MI sources, and to explore their evolving roles, implications for...

36. Computational approaches for drug-drug interaction prediction: a systematic review of data sources, modeling strategies, and evaluation frameworks

    INTRODUCTION: Drug-drug interactions (DDIs) are a major cause of preventable harm in polypharmacy and remain difficult to anticipate as formularies, indication profiles, and interaction labels evolve. Over the last few years, the DDI modeling landscape has shifted rapidly toward graph-native, multimodal, and contrastive or self-supervised learning, alongside renewed interest in extraction, decision support, and pharmacovigilance pipelines.

37. The Role of Artificial Intelligence in Medication Management for Older Adults: A Systematic Review

    Older adults face increased risks of medication non-adherence, adverse drug events, and polypharmacy due to chronic health conditions and complex drug regimens. Traditional medication management approaches often fall short in addressing these challenges. Artificial intelligence (AI) has emerged as a promising tool for enhancing medication safety and personalization in geriatric care. This systematic review aimed to explore the role of AI in medication management for older adults, highlighting...

38. Machine Learning for Comparative Antidepressant Selection in Major Depressive Disorder: Systematic Review

    CONCLUSIONS: ML for comparative antidepressant selection remains in an early stage of development. Only 1 study implemented a unified framework directly supporting patient-level treatment ranking. Key barriers to clinical translation include insufficient distinction between prognostic and predictive markers, limited cross-trial validation, near-absent calibration reporting, and absent explainability. Future research should prioritize unified comparative frameworks with calibrated predictions,...

39. Ultrasonographic Assessment of Upper Airway Structures in Adult Obstructive Sleep Apnea: A Systematic Review

    Background: Ultrasonography (US) has emerged as a non-invasive method for anatomical and functional evaluation of upper airway structures in adult obstructive sleep apnea (OSA). However, its role in severity stratification, dynamic assessment, elastographic characterization, and therapeutic monitoring remain to be investigated. Background/Objectives: The goal herein is thus to systematically review and synthesize available evidence on US assessment in adults with OSA, including structural...

40. Valve involvement in infective endocarditis among intravenous drug users: a systematic review and meta-analysis

    CONCLUSION: This SRMA confirms the predominance of right-sided IE, particularly tricuspid valve involvement, in IVDUs, with a significantly higher risk compared to left-sided IE. The findings underscore the need for targeted screening, early intervention, and IVDU-specific management strategies in IE care. Future research should focus on regional variations, microbiological patterns, and long-term outcomes in this high-risk population.

41. Factors Contributing to Variability in Longitudinal Pain Scores in Osteoarthritis Randomised Clinical Trials: A Systematic Review and Meta-Analysis

    CONCLUSIONS: This review highlights significant variability in WOMAC pain reporting in osteoarthritis trials, affecting statistical power and trial design. Key factors influencing variability include trial design, administration route, and participant characteristics. Integrating these variability estimates into sample-size calculations can enhance the efficiency of future pain trials.

42. Computational paradigms for antimicrobial resistance prediction: integrating multi-omics, structural modeling, and foundation artificial intelligence systems

    Antimicrobial resistance (AMR) poses an escalating threat to global health, as multidrug-resistant pathogens undermine therapeutic efficacy and surveillance systems. Although whole-genome sequencing and phenotypic drug susceptibility testing have strengthened resistome profiling, translating multi-omics data into reliable, clinically deployable intelligence remains computationally fragmented. Following PRISMA 2020 guidelines, we systematically reviewed 156 records published between 2016 and...

43. Global mapping of bedaquiline-resistant Mycobacterium tuberculosis: a systematic review

    CONCLUSION: Given the increasing BDQ resistance and regional variability, it is essential to develop early detection systems, genomic surveillance, robust drug policy enforcement, and rapid diagnostics to maintain treatment effectiveness and curb the spread of resistance. Future research should focus on elucidating resistance mechanisms and developing novel therapeutic strategies.

44. AI/ML-based prediction of TB treatment failure: A systematic review and meta-analysis

    CONCLUSIONS: Machine learning models for predicting TB treatment failure show promising discrimination but are not yet ready for routine clinical implementation. Performance varies substantially across populations and settings, and methodological limitations, including inadequate validation, poor calibration assessment, and high risk of bias, limit confidence in current estimates. Future research should prioritize rigorous external validation, calibration assessment, and development in...

45. Association of oral contraceptives with depression symptoms, diagnosis, and treatment in healthy women: A meta-analysis

    CONCLUSION: Evidence from this meta-analysis suggests oral contraceptive use is associated with increased risks of depression diagnoses, antidepressant initiation, and higher depressive symptom scores. The findings, which reflect the association between hormonal oral contraceptives and depression in women without pre-existing psychological or gynaecological conditions, suggest that adverse effects on mood should be closely monitored by contraception prescribers.

46. Hypoglycaemia Risk Prediction Models for Type 2 Diabetes: A Systematic Review and Meta-Analysis

    CONCLUSIONS: Current hypoglycaemia prediction models for T2DM show substantial methodological limitations and high bias risk. While machine learning models have advanced rapidly in recent years, their methodology remains opaque and validation is limited. Future research should focus on optimising existing models, enhancing methodological rigour and conducting external validation.

47. Machine Learning in HIV Care and Antiretroviral Therapy: Systematic Review

    CONCLUSIONS: Depending on the field of application, some ML methods are more suitable and adapt better to certain HIV concerns. However, some areas, such as treatment recommendations, treatment adherence, and treatment optimization, still lack AI algorithms and need further exploration, such as therapeutical optimization. The development of new clinical decision-support systems for people living with HIV is the new challenge for the years ahead, and AI represents one of the most promising tools...

48. First-Line Integration of Local Ablative Therapy With EGFR Tyrosine Kinase Inhibitors in Advanced EGFR+ NSCLC: A Systematic Review and Meta-Analysis

    CONCLUSIONS: Across a heterogeneous evidence base, integrating LAT into first-line EGFR TKI therapy is associated with improved progression-free survival and overall survival with acceptable toxicity. These findings support further prospective investigation to better define patient selection, optimal timing, and integration with contemporary systemic combination strategies.

49. Application of Reinforcement Learning Techniques in De Novo Drug Design: A Systematic Literature Review

    CONCLUSION: Reinforcement learning provides a robust framework for automated drug design, enabling intelligent exploration of chemical space and the generation of novel, bioactive compounds. However, further improvements in multi-objective optimization, computational efficiency, and model transparency are essential for broader clinical applicability. Future research should focus on hybrid RL architectures and explainable AI techniques to bridge computational and experimental drug discovery.

50. Use of AI to Predict and Support Medication Adherence in Patients With Breast Cancer: Systematic Review

    CONCLUSIONS: To our knowledge, this is the first systematic review of AI applications specifically targeting medication adherence in BC. It focuses on both predictive and interventional studies, mapping current AI applications within this specific clinical context. The findings highlight gaps in the implementation phase and emphasize the need for future research integrating a coordinated, multidisciplinary approach involving researchers, AI specialists, policymakers, and health care teams.

51. The Indoor Microbiome: Sampling, Analysis and Emerging Trends

    Indoor spaces contain diverse microbial communities that shape human health. These microorganisms are particularly relevant to respiratory diseases, including asthma and allergies. Despite growing recognition of the importance of indoor microbial exposures, research in this field is slowed by differences in methods. These inconsistencies make it difficult to compare results and draw conclusions. This systematic review analyses 106 studies published between 2000 and 2025 that investigated indoor...

52. Mapping the knowledge domain: a bibliometric analysis of global research on traditional Chinese medicine for non-alcoholic fatty liver disease (2000-2024)

    CONCLUSION: This bibliometric analysis thoroughly outlines the current status and developmental tendencies of TCM research in NAFLD for the first time, offering significant references for future investigations in this domain.

53. Systematic review of AI-based models in pharmacoepidemiology for adverse drug event prediction and detection

    INTRODUCTION: Artificial intelligence (AI) has increasingly been applied in pharmacoepidemiology, yet the methodological landscape of adverse drug event (ADE) prediction remains heterogeneous and insufficiently mapped.

54. Efficacy and Safety of Automated Insulin Delivery in People With Type 2 Diabetes: A Systematic Review and Meta-Analysis

    CONCLUSIONS: In individuals with type 2 diabetes, AID is associated with short-term improvements in glycemic control, although the certainty of evidence is low to moderate.

55. Radiomics and artificial intelligence-based prediction of tumor response in digestive system neoplasm: a systematic review and meta-analysis

    CONCLUSION: Validation through prospective multicenter studies and reporting that has been standardized is the key to clinical reliability enhancement and backed-up precision oncology implementation.

56. Exosomes in diabetic kidney disease: pathogenesis, biomarker discovery, and emerging therapeutics-a comprehensive systematic review

    Diabetic kidney disease (DKD), characterized by progressive renal dysfunction, is a prevalent microvascular complication of diabetes mellitus and a leading cause of end-stage renal disease worldwide. Despite advances in glycemic and blood pressure control, the incidence and prevalence of DKD continue to escalate, posing a growing public health challenge. Extracellular vesicles, particularly exosomes, are nanometer-sized vesicles secreted by diverse cells and have emerged as key regulators of...

57. Gender and other intersecting factors in antimicrobial resistance for infectious diseases of poverty: a systematic evidence gap analysis in low- and lower-middle-income countries

    BACKGROUND: Gender influences health outcomes by affecting exposure to risk factors, healthcare access, and health-seeking behaviours. Yet, many studies fail to consider how these gendered experiences interact with other social factors, such as age, socioeconomic status, and ethnicity. Our study systematically mapped existing research to identify gaps in understanding how these factors affect service delivery outcomes related to antimicrobial resistance (AMR) for infectious diseases of poverty....

58. Localizing the epileptogenic zone using deep learning and neuroimaging: A systematic review

    CONCLUSION: This review highlights methodological limitations hindering the clinical translation of current DL approaches for EZ localization and provides a comprehensive set of recommendations to address them. Future work should prioritize developing standardized, clinically informative evaluation frameworks and explore research avenues aligned with modern DL practices, spanning from uncertainty quantification to large-scale vision foundation models and synthetic data generation.

59. Closed-Loop Automated Insulin Delivery in Patients With Type 2 Diabetes: A Meta-Analysis of Randomised Controlled Trials

    CONCLUSIONS: Closed-loop AID significantly improves glycemic control in T2DM without increasing serious adverse events.

60. Modulation of Oncogenic NOTCH Signaling in Highly Aggressive Malignancies by Targeting the γ-Secretase Complex: A Systematic Review

    Background. NOTCH receptors play a pivotal role in carcinogenesis. Upon ligand binding, a cascade of proteolytic cleavages mediated by ADAM proteases and the γ-secretase complex activates the receptor, ultimately releasing the NOTCH intracellular domain (NICD). NICD translocates to the nucleus, where it regulates gene expression. This review mainly aims to evaluate γ-secretase inhibitors (GSIs) as anticancer agents in preclinical and clinical settings, with a focus on their ability to block...

61. Digital Twins in Neuro-Oncology: A Systematic Review of Current Implementations, Technical Strategies, and Clinical Applications

    Purpose To perform a systematic review evaluating current digital twin (DT) implementations, highlighting clinical relevance and technical strategies, and identifying opportunities to advance personalized, predictive care in neuro-oncology. Materials and Methods PubMed, Scopus, and Web of Science databases were systematically screened for English-language original research articles published from inception through June 2025 focused on DT development, validation, or patient-specific computational...

62. Efficacy and Safety of Amino Acid-Enriched Hyaluronic Acid in Facial Rejuvenation: A Systematic Review and Meta-Analysis

    CONCLUSION: Amino acid-enriched hyaluronic acid improves wrinkle severity, dermal thickness, and cell viability, enhancing overall skin aesthetics. Larger prospective studies are needed to confirm these findings.

63. Renal Protection at a Metabolic Cost: A Systematic Review and Meta-Analysis of Perioperative Use of Sodium-Glucose Cotransporter 2 Inhibitors

    CONCLUSION: The use of perioperative SGLT2 inhibitors poses a clinical paradox between significant renoprotection and survival advantages and a latent risk of ketoacidosis concealed by considerable heterogeneity. While metabolic monitoring is essential, current surgeries requiring more prolonged withholding may need to weigh metabolic risk against the drug's significant benefit in reducing acute kidney injury and mortality.

64. The role of CD36 in immune function: bridging innate and adaptive responses

    CD36 is a multifunctional glycoprotein essential in fatty acid metabolism, angiogenesis, and atherogenesis, playing a critical role in immunological processes. This comprehensive review synthesizes current research to elucidate CD36's integral functions within the immune system, including its involvement in phagocytosis, inflammation, and the crucial interplay between innate and adaptive immune responses. We highlight novel insights into CD36 as a therapeutic target, presenting recent advances...

65. The hepatitis B care cascade among key populations towards global elimination: a systematic review and meta-analysis

    BACKGROUND: Key populations bear a disproportionate burden of hepatitis B virus (HBV). We synthesized evidence on the HBV care cascade among key populations to inform strategies toward WHO's 2030 elimination targets.

66. Pulmonary thromboembolism in Glanzmann Thrombasthenia: a case report and systematic literature review

    Glanzmann Thrombasthenia (GT) is a congenital platelet disorder characterized by a life-long bleeding tendency, historically considered protective against thrombosis. This report describes a rare case of pulmonary embolism (PE) in a patient with GT, challenging this assumption and highlighting a critical management paradox. A 55-year-old woman with GT underwent elective cervical discectomy. Her perioperative hemostatic regimen included a single prophylactic dose of recombinant Factor VIIa (90...

67. Budget Impact of Faricimab in Neovascular Age-Related Macular Degeneration in the Netherlands: A Systematic Review and Meta-Analysis of Injection Count

    CONCLUSIONS: Switching patients to faricimab reduced the injection frequency by two to three injections in the first year. Although evidence certainty was limited by statistical heterogeneity, the reduction was consistent across studies. Although replacing first-line bevacizumab increases costs, substantial savings are achievable in later lines. Strategic positioning of faricimab in the second-line yields significantly higher savings compared to third-line use, and could significantly lower the...

68. Economic evaluation of AI-assisted technologies in healthcare: A systematic review

    Artificial intelligence (AI) technologies are increasingly integrated into healthcare, yet their economic value remains uncertain. Traditional economic evaluation methods may not adequately capture the unique features of AI, including dynamic model evolution, scalability, and broader societal impacts. This systematic review synthesized existing evidence on the cost-effectiveness of AI-based healthcare interventions and assessed the methodological rigor of published studies. A comprehensive...

69. A systematic review of pharmacological effects on human aversive memory

    A large body of work has investigated the effect of various pharmacological compounds on aversive memory formation, retrieval, and modification in humans. A broad overview across signalling pathways and memory models is currently lacking. Here, we systematically review publications that tested the impact of acute pharmacological interventions on aversive memory in healthy humans, following PRISMA-2020. We identified 215 candidate compounds from 17 systems and searched PubMed, Web of Science and...

70. A systematic review of molecular representation learning foundation models

    Molecular representation learning (MRL) is afoundation in leveraging computational methods for drug discovery, enabling the transformation of molecular structure and properties into numerical vectors. These vectors serve as input for machine learning models and facilitate the prediction and analysis of molecular attributes, functions, and reactions. The advent of foundation models has introduced both new opportunities and challenges to MRL. These models have improved generalizability and...

71. Comparative Efficacy and Safety of Pharmacological Interventions for IgA Nephropathy: A Systematic Review and Meta-Analysis

    Background and Objectives: IgA nephropathy represents the most prevalent form of primary glomerulonephritis around the world, with significant heterogeneity in management strategies and outcomes. We conducted a systematic review and meta-analysis to evaluate the efficacy and safety of pharmacological interventions for IgA nephropathy. Materials and Methods: We searched multiple databases through June 2025, identifying randomized controlled trials and observational studies evaluating...

72. Tumor-derived extracellular vesicles and genitourinary cancers: from biological mechanisms to clinical applications

    CONCLUSION: TDEVs represent a paradigm shift in precision oncology for genitourinary malignancies. With advancing technologies in isolation methods, multi-omics integration, and artificial intelligence applications, TDEVs are poised to become indispensable tools for early tumor detection, real-time monitoring, and personalized therapeutic strategies, heralding a new era in uro-oncological practice.

73. A Systematic Literature Review of Precision Anesthesia Through Machine Learning: Automated Drug Titration and Real-Time Physiologic Optimization

    The integration of machine learning (ML) and artificial intelligence (AI) technologies into anesthesia practice represents a paradigm shift toward precision medicine by enabling automated, data-driven decision-making during surgery. This systematic review aimed to evaluate current applications of ML for automated drug titration and real-time physiologic optimization in anesthesia. A comprehensive literature search, adhering to PRISMA (Preferred Reporting Items for Systematic Reviews and...

74. Artificial Intelligence Methods for the Differential Diagnosis of Irritable Bowel Syndrome and Inflammatory Bowel Disease: A Systematic Review

    CONCLUSIONS: Although AI/ML methods show significant potential for distinguishing IBS from IBD, existing studies present limitations, including small sample sizes, data heterogeneity, and generalizability challenges. The development of standardized protocols and extensive multicenter studies is recommended to clinically validate these models, facilitating their integration into current medical practice.

75. Evidence-Based Recommendations for Geriatric Trauma Care: Systematic Review and AI-Assisted Consolidation of Clinical Practice Guidelines Between 2016 and 2021

    CONCLUSIONS: Our systematic review provides a comprehensive summary of evidence-based CPG recommendations for geriatric trauma care, offering clinicians a solid foundation for managing this vulnerable patient population.

76. Global trends in carbapenem-resistant gram-negative bacteria research (2020-2025): a bibliometric analysis and systematic review

    CONCLUSION: CRGNB research is increasingly directed toward elucidating resistance mechanisms, improving diagnostic tools, and exploring non-antibiotic therapeutic options. Strengthening international collaboration and fostering multidisciplinary approaches are imperative to advance high-quality research and address this growing threat.

77. Botulinum Toxin Combined with Robot-Assisted Therapy for Post-Stroke Spasticity: A Systematic Review

    (1) Background: Post-stroke spasticity limits motor recovery and independence. Combining botulinum toxin type-A (BoNT-A) injection with intensive, task-specific robot-assisted therapy (RAT) might enhance neuroplasticity and functional gains, but its additive effect and optimal timing are uncertain. (2) Methods: We systematically searched major medical databases and trial registries up to April 2025 for randomized controlled trials in adults with post-stroke spasticity comparing botulinum toxin...

78. Rethinking Treatment-Resistant Depression: A Systematic Review of Novel Therapeutic Strategies and Precision Medicine Approaches

    CONCLUSION: A multidisciplinary and precision-based approach is essential for optimizing TRD management. Future research should focus on biomarker-driven treatment selection, artificial intelligence-assisted decision making, and large-scale trials to refine personalized therapeutic strategies.

79. Treatment outcomes in Mycobacterium abscessus pulmonary disease: A systematic review

    CONCLUSION: These findings underscore the suboptimal outcomes associated with current treatment strategies for MABC pulmonary disease. There is an urgent need for large-scale, multicentre prospective studies utilising standardised treatment outcome definitions and unified therapeutic regimens to improve patient care and clinical outcomes.

80. Integrative machine learning approaches with genomic data for predicting antitubercular drug resistance: A systematic review and meta-analysis

    CONCLUSIONS: ML models trained on genomic data demonstrate high diagnostic accuracy and robust discriminative ability for predicting first-line drug resistance-particularly for RIF and INH-although sensitivity remains variable across drugs and model types. Standardized external validation and calibration are needed before broad clinical deployment.

81. Computational drug design in the artificial intelligence era: A systematic review of molecular representations, generative architectures, and performance assessment

    Generative drug design has emerged as a transformative approach in pharmaceutical research, leveraging deep learning models to create novel molecules with targeted properties. This systematic review analyzes the current landscape of computational approaches across 3 critical dimensions: molecular representation strategies (1-dimensional, 2-dimensional, and 3-dimensional), generative architectural frameworks (including variational autoencoders, generative adversarial networks, reinforcement...

82. Synergistic innovations of nanomedicine in lymphoma treatment: a systematic review

    Lymphoma therapy faces persistent challenges, including tumor heterogeneity, drug resistance, and immunosuppressive microenvironments, particularly in relapsed or refractory cases. Current treatments, such as chemotherapy, targeted therapy, and cell-based therapies, are limited by suboptimal targeting, systemic toxicity, and manufacturing complexities, highlighting the urgent need for innovative solutions. Nanomedicine has emerged as a transformative approach, integrating material design with...

83. Pathogenic mechanisms and resistance profiles of microbial pulmonary infections in lung cancer: a systematic review

    BACKGROUND: Pulmonary infections caused by microorganisms in lung cancer patients contribute to disease progression and treatment challenges. This systematic review aims to explore the clinical and pathophysiological characteristics of microbial pulmonary infections in lung cancer. METHODS: A systematic literature search was conducted across Embase, PubMed/MEDLINE, Scopus, and Web of Science, covering studies published between January 1, 2015 and February 1, 2025, without restrictions on...

84. Machine learning methods for predicting adverse drug events: A systematic review

    Predicting adverse drug events (ADEs) in outpatient settings is crucial for improving medication safety, identifying high-risk patients and reducing health-care costs. While traditional methods struggle with the complexity of health-care data, machine learning (ML) models offer improved prediction capabilities; however, their effectiveness in ADE prediction remains unclear. This systematic review evaluated ML algorithms used for this purpose, analysing studies that focussed on outpatient care or...

85. Artificial intelligence nanoparticle-based drug delivery systems and targeted delivery of therapeutics for the treatment of cardiovascular diseases: a systematic review

    This article reviews the progress of nanoparticles as drug carriers in the treatment of cardiovascular diseases, and how nanoparticles can deliver anti-inflammatory, anti-proliferative, and anticoagulant drugs directly to the surgical site in surgical procedures. For this, 45 articles published between 2005 and 2024 with keywords including "Artificial intelligence Nanoparticle", "Emergency Medicine Unit", and "Therapeutic for the treatment of cardiovascular diseases" in Scopus, Elsevier, Web of...

86. Bridging the digital divide in the pharmaceutical industry: A future research agenda

    Digital transformation is reshaping the pharmaceutical industry, but adoption remains fragmented due to regulatory constraints, organizational inertia, and unequal digital capabilities. This study investigates how the digital divide influences the implementation of new technologies across the pharmaceutical value chain. We combined a systematic literature review of 70 peer-reviewed studies with topic modeling (Latent Dirichlet Allocation) to provide an integrated overview of challenges,...

87. Predicting response to neuromodulation therapies in drug-resistant epilepsy using machine learning models: a meta-analysis and systematic review

    CONCLUSIONS: Our study suggests that multimodal ML approaches show promising performance in predicting response to neuromodulation strategies in patients with drug-resistant epilepsy. However, the limited number of studies, the scarcity of external validation and small cohorts highlight the need for larger, high-quality prospective investigations to confirm these findings and improve the generalizability of ML-based prediction models.

88. AAV Gene Therapy Drug Development and Translation of Engineered Ocular and Neurotropic Capsids: A Systematic Review Using Natural Language Processing

    Natural AAV serotypes often lack specificity and efficiency, leading to off-target effects and a low therapeutic index. To overcome these limitations of naturally occurring serotypes, there has been a keen interest in the field to engineer novel capsids to enhance tissue and cell-specific targeting, resulting in a high number of published literature reports over the past few years. To ensure a systematic review and illustrate advances in engineered capsids that enhance specificity and...

89. Are Image-Based Deep Learning Algorithms of Kidney Volume in Polycystic Kidney Disease Ready for Clinical Deployment? A Systematic Review and Meta-Analysis

    Objectives: In patients with autosomal dominant polycystic kidney disease (ADPKD), total kidney volume (TKV) is the gold standard biomarker for assessing the risk of progression and the need for drug therapy. However, it is a time-consuming process. In this systematic review and meta-analysis, we evaluate the current state of deep learning (DL) algorithms for automatic kidney volume segmentation. Methods: All original research, including the search terms ADPKD, diagnostic imaging, DL, and TKV,...

90. In Silico Clinical Trials in Drug Development: A Systematic Review

    In the context of clinical research, computational models have received increasing attention over the past decades. In this systematic review, we aimed to provide an overview of the role of so-called in silico clinical trials (ISCTs) in medical applications. Exemplary for the broad field of clinical medicine, we focused on in silico (IS) methods applied in drug development, sometimes also referred to as model informed drug development (MIDD). We searched PubMed and ClinicalTrials.gov for...

91. Multimodal machine learning for surgical decision support in epilepsy: Current evidence and translational gaps

    OBJECTIVE: This systematic review synthesizes evidence on multimodal machine learning (ML) decision support systems for epilepsy surgery focusing on postsurgical outcome prediction, with emphasis on methodological quality and implications for clinical practice.

92. Detection and Management of Geographic Atrophy Secondary to Age-Related Macular Degeneration Using Noninvasive Retinal Images and Artificial Intelligence: Systematic Review

    CONCLUSIONS: AI, particularly DL-based algorithms, holds considerable promise for the detection and management of GA secondary to dry AMD with performance comparable to ophthalmologists. This review innovatively consolidates evidence across GA management-from initial detection to progression prediction-using diverse noninvasive imaging. It has strong potential to augment clinical decision-making. However, to realize this potential in real-world settings, future research is needed to robustly...

93. Cannabis and nicotine/tobacco co-use and its association with cognitive and neural outcomes: A systematic review

    CONCLUSIONS: Although findings were heterogenous, converging evidence suggests that cannabis and NTP use may offset each other in cognitive and brain functional outcomes, mitigating impairments linked to single-substance use. These results offer novel insight into the cognitive and neurobiological factors that may underlie co-use and may help inform treatment. Rather than extrapolating cannabis and NTP co-use treatment from single-substance treatment approaches, interventions targeting the...

94. Recent Advances in Interventions Targeting Remyelination and a Systematic Review of Remyelinating Effects of Approved Disease-Modifying Treatments for Multiple Sclerosis

    CONCLUSIONS: Future proof-of-concept clinical trials investigating remyelinating agents in MS should consider combining outcome measures into composite endpoints. Furthermore, research efforts should be dedicated to novel biomarkers to assess repair mechanisms in MS.

95. Diagnostic performance of neuroimaging modalities for epileptogenic focus localization: A systematic review

    OBJECTIVE: Accurate localization of epileptogenic foci remains of significant importance for surgical planning in drug-resistant epilepsy. Multiple neuroimaging modalities are available; however, their comparative diagnostic performance lacks comparative detailed synthesis. This systematic review aimed to evaluate and compare the diagnostic accuracy of structural MRI, PET imaging, SPECT/SISCOM, and combined multimodal strategies for epileptogenic focus localization.

96. FDA Approval of Artificial Intelligence and Machine Learning Devices in Radiology: A Systematic Review

    No abstract

97. Artificial intelligence in clinical pharmacy-A systematic review of current scenario and future perspectives

    CONCLUSION: Artificial intelligence-based algorithms have been identified as applicable tools for the early detection of adverse drug events and prescription errors, the prediction of individual drug response, and the design of patient-specific treatment plans. Prior to broad clinical implementation, future multicenter, prospective studies employing standardized clinical endpoints, external validation, and cost-effectiveness analyses are required.

98. Accuracy of Machine Learning in Identifying Drug Resistance in Tuberculosis: A Systematic Review and Meta-Analysis

    CONCLUSION: ML models, particularly DL, demonstrate high diagnostic efficacy for DR-TB, though performance declines in external data sets. Predictive models show moderate accuracy but remain useful for early risk stratification. Large multi-center validations are needed to ensure robustness and clinical applicability.

99. MRI-based radiomics models for early predicting pathological response to neoadjuvant chemotherapy in triple-negative breast cancer: A systematic review and meta-analysis

    CONCLUSION: MRI-based radiomics exhibits strong and consistent predictive performance for pCR in TNBC patients undergoing NAC, supporting its potential as a non-invasive tool for early treatment response assessment. Further standardization and prospective validation are needed for clinical implementation.

100. Bridging gaps throughout a patient's journey with melanoma: a systematic review

     CONCLUSIONS: Melanoma patients experience significant gaps throughout their healthcare journey. Identifying areas of improvement in current practices is the first step toward developing targeted solutions that improve the patient experience and quality of life.