INTELLIGENZA ARTIFICIALE IN AMBITO SANITARIO E FARMACEUTICO
Intelligenza artificiale in ambito sanitario intelligence drug: Latest results from PubMed
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Advanced applications in chronic disease monitoring using IoT mobile sensing device data, machine learning algorithms and frame theory: a systematic review
The escalating demand for chronic disease management has presented substantial challenges to traditional methods. However, the emergence of Internet of Things (IoT) and artificial intelligence (AI) technologies offers a potential resolution by facilitating more precise chronic disease management through data-driven strategies. This review concentrates on the utilization of IoT mobile sensing devices in managing major chronic diseases such as cardiovascular diseases, cancer, chronic respiratory... -
Efficacy and safety of adenosine for supraventricular tachycardia: A meta-analysis utilizing BioMedGPT-LM-7B
CONCLUSION: Adenosine/ATP and CCBs have similar efficacy in treating SVT, but adenosine/ATP has a shorter conversion time and no reported cases of hypotension. Clinical studies indicate that adenosine has a higher success rate and faster conversion time in restoring sinus rhythm compared to ATP, with milder side effects. However, further prospective studies are needed to evaluate patient experience and potential adverse events, ensuring a more comprehensive understanding of treatment safety and... -
Advances in analytical approaches for background parenchymal enhancement in predicting breast tumor response to neoadjuvant chemotherapy: A systematic review
CONCLUSION: This review provides a thorough examination of current advancements in analytical approaches for BPE analysis in predicting breast tumor response to NAC. The shift towards longitudinal BPE analysis has highlighted significant gaps, suggesting the need for alternative analytical techniques, particularly in the realm of artificial intelligence (AI). Future longitudinal BPE research work should focus on standardization in longitudinal BPE measurement and analysis, through integration of... -
Assessment of Automation Models in Hospital Pharmacy: Systematic Review of Technologies, Practices, and Clinical Impacts
Medication management in hospitals is a complex process that encompasses every step from prescription to administration, involving multiple healthcare professionals. This process is prone to various errors that can compromise patient safety and generate significant human and financial costs. Automation in hospital pharmacies represents a major advancement, enhancing patient safety, optimizing professional practices, and reducing hospital expenses. This study aims to analyze the different types... -
NETest and Gastro-Entero-Pancreatic Neuroendocrine Tumors: Still Far from Routine Clinical Application? A Systematic Review
Background: Gastro-entero-pancreatic neuroendocrine tumors (GEP-NETs) are the most prevalent subgroup among NETs and include heterogeneous tumors characterized by different clinical behavior and prognosis. The NETest is a tool based on real-time PCR combined with deep learning strategies to specifically identify tumors with a neuroendocrine genotype. Despite the promising results achieved regarding its utility in the field of GEP-NETs, the NETest has not yet entered into routine clinical... -
Machine learning for predicting antimicrobial resistance in critical and high-priority pathogens: A systematic review considering antimicrobial susceptibility tests in real-world healthcare settings
CONCLUSIONS: ML displays potential as a technology for predicting AMR, incorporating antimicrobial susceptibility tests in CHPP in real-world healthcare settings. However, limitations such as retrospective methodology for model development, nonstandard data processing, and lack of validation in randomized controlled trials must be considered before applying these models in clinical practice. -
The research progress on effective connectivity in adolescent depression based on resting-state fMRI
INTRODUCTION: The brain's spontaneous neural activity can be recorded during rest using resting state functional magnetic resonance imaging (rs-fMRI), and intricate brain functional networks and interaction patterns can be discovered through correlation analysis. As a crucial component of rs-fMRI analysis, effective connectivity analysis (EC) may provide a detailed description of the causal relationship and information flow between different brain areas. It has been very helpful in identifying... -
Comparison of artificial intelligence and logistic regression models for mortality prediction in acute respiratory distress syndrome: a systematic review and meta-analysis
CONCLUSION: The AI algorithms showed superior performance in predicting the mortality of ARDS patients and demonstrated strong potential for clinical application. Additionally, we found that for ARDS, a highly heterogeneous condition, the accuracy of the model is influenced by the severity of the disease. -
Artificial intelligence (AI) in pharmacovigilance: A systematic review on predicting adverse drug reactions (ADR) in hospitalized patients
INTRODUCTION: Adverse drug reactions (ADRs) significantly impact healthcare systems, leading to increased hospitalization rates and costs. With the growing adoption of artificial intelligence (AI) in healthcare, machine learning (ML) models offer promising solutions for ADR prediction. However, comprehensive evaluations of these models remain limited. -
Prediction models for treatment response in migraine: a systematic review and meta-analysis
CONCLUSION: This review highlights the potential of statistical and machine learning models in predicting treatment response in migraine patients. However, the high risk of bias and significant heterogeneity emphasize the need for caution in interpretation. Future research should focus on developing models using high-quality, comprehensive, and multicenter datasets, rigorous external validation, and adherence to standardized guidelines like TRIPOD + AI. Incorporating multimodal magnetic... -
Mapping knowledge landscapes and emerging trends in artificial intelligence for antimicrobial resistance: bibliometric and visualization analysis
CONCLUSION: This bibliometric analysis shows the importance of artificial intelligence in enhancing the progress in the discovery of antimicrobial drugs especially toward the fight against AMR. From enhancing the fast, efficient and predictive performance of drug discovery methods, current AI capabilities have revealed observable potential to be proactive in combating the ever-growing challenge of AMR worldwide. This study serves not only an identification of current trends, but also, and... -
Current update on surgical management for spinal tuberculosis: a scientific mapping of worldwide publications
INTRODUCTION: Spinal tuberculosis (TB), or Pott's disease, remains a significant global health issue, particularly in regions with high TB prevalence. Despite antitubercular drug therapy being the primary treatment, surgical intervention is often required in cases of spinal instability or neurological complications. This study aims to conduct a comprehensive bibliometric analysis of worldwide publications related to the surgical management of spinal TB and to compare contributions from... -
AI in the Health Sector: Systematic Review of Key Skills for Future Health Professionals
CONCLUSIONS: Despite the broadening of search criteria to capture the evolving nature of AI in health care, the review underscores a significant gap in focused studies on the required competencies. Moreover, the review highlights the critical role of regulatory bodies such as the US Food and Drug Administration in facilitating the adoption of AI technologies by establishing trust and standardizing algorithms. Key areas were identified for developing competencies among health care professionals... -
Factors affecting posaconazole plasma concentrations: a meta-analysis and systematic review
CONCLUSION: DRT maintain more stable concentrations than POS and are not affected by acid-suppressing drugs. Given the significant fluctuations in posaconazole concentrations, patients experiencing diarrhea require close monitoring. -
Machine learning to predict adverse drug events based on electronic health records: a systematic review and meta-analysis
CONCLUSIONS: Future studies should adhere to more rigorous reporting standards and consider new ML methods to facilitate the application of ML models in clinical practice. -
Harnessing AI for advancing pathogenic microbiology: a bibliometric and topic modeling approach
INTRODUCTION: The integration of artificial intelligence (AI) in pathogenic microbiology has accelerated research and innovation. This study aims to explore the evolution and trends of AI applications in this domain, providing insights into how AI is transforming research and practice in pathogenic microbiology. -
Predicting adverse drug event using machine learning based on electronic health records: a systematic review and meta-analysis
INTRODUCTION: Adverse drug events (ADEs) pose a significant challenge in current clinical practice. Machine learning (ML) has been increasingly used to predict specific ADEs using electronic health record (EHR) data. This systematic review provides a comprehensive overview of the application of ML in predicting specific ADEs based on EHR data. -
Innovative technologies to address neglected tropical diseases in African settings with persistent sociopolitical instability
The health, economic, and social burden of neglected tropical diseases (NTDs) in Africa remains substantial, with elimination efforts hindered by persistent sociopolitical instability, including ongoing conflicts among political and ethnic groups that lead to internal displacement and migration. Here, we explore how innovative technologies can support Africa in addressing NTDs amidst such instability, through analysis of WHO and UNHCR data and a systematic literature review. Countries in Africa... -
Challenges of the Biopharmaceutical Industry in the Application of Prescriptive Maintenance in the Industry 4.0 Context: A Comprehensive Literature Review
The biopharmaceutical industry has specificities related to the optimization of its processes, the effectiveness of the maintenance of the productive park in the face of regulatory requirements. and current concepts of modern industry. Current research on the subject points to investments in the health area using the current tools and concepts of Industry 4.0 (I4.0) with the objective of a more assertive production, reduction of maintenance costs, reduction of operating risks, and minimization... -
Exploring machine learning algorithms in sickle cell disease patient data: A systematic review
This systematic review explores the application of machine learning (ML) algorithms in sickle cell disease (SCD), focusing on diagnosis and several clinical characteristics, such as early detection of organ failure, identification of drug dosage, and classification of pain intensity. A comprehensive analysis of recent studies reveals promising results in using ML techniques for diagnosing and monitoring SCD. The review covers various ML algorithms, including Multilayer Perceptron, Support Vector... -
Effects of Licorice Functional Components Intakes on Blood Pressure: A Systematic Review with Meta-Analysis and NETWORK Toxicology
CONCLUSIONS: There were distinct differences in the effects of licorice functional components on blood pressure. Functional constituents dominated by GA were shown to increase both SBP and DBP, whereas those dominated by LF did not exhibit significant effects on blood pressure. The hypertensive mechanism of GA may involve the modulation of GFI1B, MYLK, and RSU1 to regulate nitrogen metabolic pathways. -
Applications and Concerns of ChatGPT and Other Conversational Large Language Models in Health Care: Systematic Review
CONCLUSIONS: Future studies should focus on improving the reliability of LLM applications in complex health-related tasks, as well as investigating the mechanisms of how LLM applications bring bias and privacy issues. Considering the vast accessibility of LLMs, legal, social, and technical efforts are all needed to address concerns about LLMs to promote, improve, and regularize the application of LLMs in health care. -
Impact of combinatorial immunotherapies in breast cancer: a systematic review and meta-analysis
CONCLUSION: The observed improvements in overall survival and progression-free survival suggest that combination immunotherapies could serve as a better approach to breast cancer management. -
Factors predicting treatment response to biological and targeted synthetic disease-modifying antirheumatic drugs in psoriatic arthritis - a systematic review and meta-analysis
The therapeutic response of patients with psoriatic arthritis (PsA) varies greatly and is often unsatisfactory. Accordingly, it is essential to individualise treatment selection to minimise long-term complications. This study aimed to identify factors that might predict treatment response to biological and targeted synthetic disease-modifying antirheumatic drugs (bDMARDs and tsDMARDs) in patients with PsA and to outline their potential application using artificial intelligence (AI). Five... -
Application of Artificial Intelligence in the diagnosis and treatment of colorectal cancer: a bibliometric analysis, 2004-2023
CONCLUSION: Research on the application of AI in the diagnosis and treatment of CRC has made significant progress and is flourishing across the world. Current research hotspots include AI-assisted early screening and diagnosis, pathology, and staging, and prognosis assessment, and future research is predicted to put weight on multimodal data fusion, personalized treatment, and drug development. -
Advancing personalized oncology: a systematic review on the integration of artificial intelligence in monitoring neoadjuvant treatment for breast cancer patients
CONCLUSION: This paper seeks to unveil crucial insights into the application of AI techniques in personalized oncology, particularly in the monitoring and prediction of responses to NAT for BC patients. Finally, the authors suggest avenues for future research into AI-based monitoring systems. -
Integrating whole genome sequencing and machine learning for predicting antimicrobial resistance in critical pathogens: a systematic review of antimicrobial susceptibility tests
BACKGROUND: Infections caused by antibiotic-resistant bacteria pose a major challenge to modern healthcare. This systematic review evaluates the efficacy of machine learning (ML) approaches in predicting antimicrobial resistance (AMR) in critical pathogens (CP), considering Whole Genome Sequencing (WGS) and antimicrobial susceptibility testing (AST). -
Machine learning-augmented interventions in perioperative care: a systematic review and meta-analysis
CONCLUSIONS: HPI decreased the duration of intraoperative hypotension, and NoL decreased postoperative pain scores, but no significant impact on other clinical outcomes was found. We highlight the need to address both methodological and clinical practice gaps to ensure the successful future implementation of ML-driven interventions. -
Availability of Evidence for Predictive Machine Learning Algorithms in Primary Care: A Systematic Review
CONCLUSIONS AND RELEVANCE: The findings indicate an urgent need to improve the availability of evidence regarding the predictive ML algorithms' quality criteria. Adopting the Dutch AIPA guideline could facilitate transparent and consistent reporting of the quality criteria that could foster trust among end users and facilitating large-scale implementation. -
Corrigendum to "Global epidemiology of severe fever with thrombocytopenia syndrome virus in human and animals: a systematic review and meta-analysis" [The Lancet Regional Health - Western Pacific, Volume: 48 (2024) 101133]
[This corrects the article DOI: 10.1016/j.lanwpc.2024.101133.]. -
Machine learning in the prediction of treatment response in rheumatoid arthritis: A systematic review
CONCLUSIONS: In recent years, ML methods have been increasingly used to predict treatment response in RA. Our critical appraisal revealed unclear and high risk of bias in most of the identified models, suggesting that researchers can do more to address the risk of bias and increase transparency, including the use of calibration measures and reporting methods for handling missing data. -
Budget impact models for lung cancer interventions: A systematic literature review
CONCLUSIONS: The number of published BIMs for lung cancer exceeded expectations. There were modest trends toward publication frequency and model quality over time. Our analysis revealed variability across the models, as well as their adherence to best practices, indicating substantial room for improvement. Although none of the models were individually suitable for the purpose of evaluating an artificial intelligence-based treatment selection tool, some models provided valuable insights. -
A Systematic Review of Artificial Intelligence Used to Predict Loneliness, Social Isolation, and Drug Use During the COVID-19 Pandemic
This systematic literature review evaluates the role of machine learning, artificial intelligence (AI), and social determinants of health (SDOH) in identifying loneliness during the COVID-19 pandemic. By examining various studies and articles through a comprehensive search of databases EBSCOhost, Medline Complete, Academic Search Complete, Directory of Open Access Journals, and Complementary Index, the research team sought to discern consistent themes and patterns. We identified four constructs... -
Global epidemiology of severe fever with thrombocytopenia syndrome virus in human and animals: a systematic review and meta-analysis
BACKGROUND: Since the initial identification of the Severe Fever with Thrombocytopenia Syndrome (SFTS) in ticks in rural areas of China in 2009, the virus has been increasingly isolated from a diverse array of hosts globally, exhibiting a rising trend in incidence. This study aims to conduct a systematic analysis of the temporal and spatial distribution of SFTS cases, alongside an examination of the infection rates across various hosts, with the objective of addressing public concerns regarding... -
The performance of digital technologies for measuring tuberculosis medication adherence: a systematic review
CONCLUSION: The limited number of studies available suggests suboptimal and variable performance of DATs for dose monitoring, with significant evidence gaps, notably in real-world programmatic settings. Future research should aim to improve understanding of the relationships of specific technologies, settings and user engagement with DAT performance and should measure and report performance in a more standardised manner. -
Artificial Intelligence and Machine Learning in Neuroregeneration: A Systematic Review
Artificial intelligence (AI) and machine learning (ML) show promise in various medical domains, including medical imaging, precise diagnoses, and pharmaceutical research. In neuroscience and neurosurgery, AI/ML advancements enhance brain-computer interfaces, neuroprosthetics, and surgical planning. They are poised to revolutionize neuroregeneration by unraveling the nervous system's complexities. However, research on AI/ML in neuroregeneration is fragmented, necessitating a comprehensive review.... -
Revisiting Race and the Benefit of RAS Blockade in Heart Failure: A Meta-Analysis of Randomized Clinical Trials
CONCLUSIONS AND RELEVANCE: The mortality benefit from RAS blockade was similar in Black and non-Black patients. Despite the smaller relative risk reduction in hospitalization for HF with RAS blockade in Black patients, the absolute benefit in Black patients was comparable with non-Black patients because of the greater incidence of this outcome in Black patients. -
Applications of artificial intelligence in anesthesia: A systematic review
This review article examines the utility of artificial intelligence (AI) in anesthesia, with a focus on recent developments and future directions in the field. A total of 19,300 articles were available on the given topic after searching in the above mentioned databases, and after choosing the custom range of years from 2015 to 2023 as an inclusion component, only 12,100 remained. 5,720 articles remained after eliminating non-full text. Eighteen papers were identified to meet the inclusion... -
The effect of bolus advisors on glycaemic parameters in adults with diabetes on intensive insulin therapy: A systematic review with meta-analysis
CONCLUSION: Use of a bolus advisor is associated with slightly better glucose control and treatment satisfaction in people with diabetes on intensive insulin treatment. Future studies should investigate whether personalizing bolus advisors using artificial intelligence technology can enhance these effects. -
Current perspectives and trend of computer-aided drug design: a review and bibliometric analysis
CONCLUSIONS: Influential authors in the field were identified. Current research shows active collaboration between countries, institutions, and companies. CADD technologies such as homology modeling, pharmacophore modeling, quantitative conformational relationships, molecular docking, molecular dynamics simulation, binding free energy prediction, and high-throughput virtual screening can effectively improve the efficiency of new drug discovery. Artificial intelligence-assisted drug design and... -
Artificial intelligence and its clinical application in Anesthesiology: a systematic review
CONCLUSION: AI systems are being integrated into anesthesiology clinical practice, enhancing medical professionals' skills of decision-making, diagnostic accuracy, and therapeutic response. -
Time for Using Machine Learning for Dose Guidance in Titration of People With Type 2 Diabetes? A Systematic Review of Basal Insulin Dose Guidance
CONCLUSIONS: Studies mainly used titration algorithms to titrate basal insulin as telehealth or in paper format, except for studies using mathematical models. A numerically larger proportion of participants seemed to reach target using telehealth solutions compared to paper-based titration algorithms. Exploring capabilities of machine learning may provide insights that could pioneer future research while focusing on holistic development.
