Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine

Research trends of neoadjuvant therapy for breast cancer: A bibliometric analysis

The approach of neoadjuvant therapy for breast cancer, which involves administering systemic treatment prior to primary surgery, has undergone substantial advancements in recent decades. This strategy is intended to reduce tumor size, thereby enabling less invasive surgical procedures and enhancing patient outcomes. This study presents a comprehensive bibliometric analysis of research trends in neoadjuvant therapy for breast cancer from 2009 to 2024. Using data extracted from the Web of Science Core Collection, a total of 3,674 articles were analyzed to map the research landscape in this field. The analysis reveals a steady increase in publication output, peaking in 2022, with the United States and China identified as the leading contributors. Key institutions, such as the University of Texas System and MD Anderson Cancer Center, have been instrumental in advancing the research on neoadjuvant therapy. The study also highlights the contributions of influential authors like Sibylle Loibl and Gunter von Minckwitz, as well as major journals such as the Journal of Clinical Oncology. Emerging research topics, including immunotherapy, liquid biopsy, and artificial intelligence, are gaining prominence and represent potential future directions for clinical applications. This bibliometric analysis provides critical insights into global research trends, key contributors, and future developments in the field of neoadjuvant therapy for breast cancer, offering a foundation for future research and clinical practice advancements.

Assessing serum thrombopoietin for enhanced diagnosis of ITP, AA, and MDS using machine learning: A retrospective cohort study

Differentiating between immune thrombocytopenia (ITP), aplastic anemia (AA), and myelodysplastic syndromes (MDS) is critical due to the distinct treatment approaches required for each condition. This study investigates the role of serum thrombopoietin (TPO) levels as a potential biomarker to aid in the diagnosis of these hematological disorders. This retrospective cohort study analyzed serum TPO levels in patients diagnosed with ITP, AA, and MDS, using clinical records and stored serum samples collected from patients treated between September 2023 and May 2024. Statistical analyses were performed to determine cut-off values for TPO levels that effectively differentiate between these conditions. Additionally, machine learning models were utilized to enhance diagnostic accuracy based on clinical indicators, including TPO levels. Serum TPO levels were markedly elevated in AA (1369.19 ± 751.26 pg/ml) compared to ITP (263.57 ± 355.91 pg/ml), MDS (434.55 ± 551.56 pg/ml), and health control (71.64 ± 30.32 pg/ml) (P < 0.0001). Correlation analysis revealed a significant positive correlation between TPO levels and ITP, AA, and MDS (P < 0.0001), Linear regression analysis indicated that age was a significant predictor of TPO levels (P < 0.0001). The optimal cut-off value for TPO levels distinguishing ITP from AA was 302.43 pg/mL, yielding an AUC of 0.925 (sensitivity with 80.75%, specificity with 94.06%). Machine learning models demonstrated that Logistic Regression, XGBoost, and LightGBM performed best, with the Logistic Regression achieving an accuracy of 86.3% and an AUC of 0.910. Serum TPO levels are a promising non-invasive biomarker for distinguishing between ITP, AA, and MDS. Incorporating TPO measurements into clinical practice may enhance diagnostic accuracy and improve patient management strategies.

The integration of artificial intelligence into clinical medicine: Trends, challenges, and future directions

BACKGROUND AND OBJECTIVES

AI has emerged as a transformative force in clinical medicine, changing the diagnosis, treatment, and management of patients. Tools have been derived for working with ML, DL, and NLP algorithms to analyze large complex medical datasets with unprecedented accuracy and speed, thereby improving diagnostic precision, treatment personalization, and patient care outcomes. For example, CNNs have dramatically improved the accuracy of medical imaging diagnoses, and NLP algorithms have greatly helped extract insights from unstructured data, including EHRs. However, there are still numerous challenges that face AI integration into clinical workflows, including data privacy, algorithmic bias, ethical dilemmas, and problems with the interpretability of "black-box" AI models. These barriers have thus far prevented the widespread application of AI in health care, and its possible trends, obstacles, and future implications are necessary to be systematically explored. The purpose of this paper is, therefore, to assess the current trends in AI applications in clinical medicine, identify those obstacles that are hindering adoption, and identify possible future directions. This research hopes to synthesize evidence from other peer-reviewed articles to provide a more comprehensive understanding of the role that AI plays to advance clinical practices, improve patient outcomes, or enhance decision-making.

METHODS

A systematic review was done according to the PRISMA guidelines to explore the integration of Artificial Intelligence in clinical medicine, including trends, challenges, and future directions. PubMed, Cochrane Library, Web of Science, and Scopus databases were searched for peer-reviewed articles from 2014 to 2024 with keywords such as "Artificial Intelligence in Medicine," "AI in Clinical Practice," "Machine Learning in Healthcare," and "Ethical Implications of AI in Medicine." Studies focusing on AI application in diagnostics, treatment planning, and patient care reporting measurable clinical outcomes were included. Non-clinical AI applications and articles published before 2014 were excluded. Selected studies were screened for relevance, and then their quality was critically appraised to synthesize data reliably and rigorously.

RESULTS

This systematic review includes the findings of 8 studies that pointed out the transformational role of AI in clinical medicine. AI tools, such as CNNs, had diagnostic accuracy more than the traditional methods, particularly in radiology and pathology. Predictive models efficiently supported risk stratification, early disease detection, and personalized medicine. Despite these improvements, significant hurdles, including data privacy, algorithmic bias, and resistance from clinicians regarding the "black-box" nature of AI, had yet to be surmounted. XAI has emerged as an attractive solution that offers the promise to enhance interpretability and trust. As a whole, AI appeared promising in enhancing diagnostics, treatment personalization, and clinical workflows by dealing with systemic inefficiencies.

CONCLUSION

The transformation potential of AI in clinical medicine can transform diagnostics, treatment strategies, and efficiency. Overcoming obstacles such as concerns about data privacy, the danger of algorithmic bias, and difficulties with interpretability may pave the way for broader use and facilitate improvement in patient outcomes while transforming clinical workflows to bring sustainability into healthcare delivery.

Role of machine learning in molecular pathology for breast cancer: A review on gene expression profiling and RNA sequencing application

INTRODUCTION

Breast cancer is the most prevalent cancer among women, with growing incidence and mortality rates. Regardless of remarkable progress in cancer research, breast cancer remains a major concern due to its complex nature. These factors underscore the necessity of innovative research and diagnostic tools. Attention to gene signatures and biotechnology methods have shown significant performance in the diagnosis and management of breast cancer. Currently, artificial intelligence (AI) is known as a revolutionary tool to analyze data, identify biomarkers, and enrich diagnostic and prognostic accuracy. Therefore, the integration of breast cancer datasets with artificial intelligence can play a crucial role in the control of breast cancer. This review explores advanced machine learning techniques to analyze transcriptomic data while focusing on breast cancer subtype classification and its potential impact and limitations.

METHOD

A comprehensive literature search was performed in PubMed, Scopus, WoS, Embase, and IEEE Xplore. Duplicates were removed, two reviewers screened articles, and two additional reviewers resolved conflicts. Data extraction included details on molecular methods, AI techniques, clinical targets, study populations, and data analysis methods which were used to categorize relevant studies into RNA sequencing and gene expression profiling groups.

RESULT

In the initial stage, 7287 articles were identified, and 54 were retained following further screening, 24 in RNA sequencing and 30 in gene expression profiling. A review of these studies showed how artificial intelligence is advancing breast cancer research by using RNA sequencing and gene expression profiling. AI algorithms, including Random Forest, CNNs, SVMs, and LASSO, were the most applied techniques that showed significant potential to identify biomarkers, prognostic survival, and optimize drug responses to manage breast cancer.

CONCLUSION

The methods of artificial intelligence hold very great potential for change in the field of breast cancer. This promising progress can be seen in every aspect including diagnosis, prognosis, and treatment. However, it is important to note that we are still in the early stages of progress, and larger-scale studies and interdisciplinary collaborations in this field are needed.

Artificial intelligence to improve cardiovascular population health

With the advent of artificial intelligence (AI), novel opportunities arise to revolutionize healthcare delivery and improve population health. This review provides a state-of-the-art overview of recent advancements in AI technologies and their applications in enhancing cardiovascular health at the population level. From predictive analytics to personalized interventions, AI-driven approaches are increasingly being utilized to analyse vast amounts of healthcare data, uncover disease patterns, and optimize resource allocation. Furthermore, AI-enabled technologies such as wearable devices and remote monitoring systems facilitate continuous cardiac monitoring, early detection of diseases, and promise more timely interventions. Additionally, AI-powered systems aid healthcare professionals in clinical decision-making processes, thereby improving accuracy and treatment effectiveness. By using AI systems to augment existing data sources, such as registries and biobanks, completely new research questions can be addressed to identify novel mechanisms and pharmaceutical targets. Despite this remarkable potential of AI in enhancing population health, challenges related to legal issues, data privacy, algorithm bias, and ethical considerations must be addressed to ensure equitable access and improved outcomes for all individuals.

The role of nanomedicine and artificial intelligence in cancer health care: individual applications and emerging integrations-a narrative review

Cancer remains one of the deadliest diseases globally, significantly impacting patients' quality of life. Addressing the rising incidence of cancer deaths necessitates innovative approaches such as nanomedicine and artificial intelligence (AI). The convergence of nanomedicine and AI represents a transformative frontier in cancer healthcare, promising unprecedented advancements in diagnosis, treatment, and patient management. This narrative review explores the distinct applications of nanomedicine and AI in oncology, alongside their synergistic potential. Nanomedicine leverages nanoparticles for targeted drug delivery, enhancing therapeutic efficacy while minimizing adverse effects. Concurrently, AI algorithms facilitate early cancer detection, personalized treatment planning, and predictive analytics, thereby optimizing clinical outcomes. Emerging integrations of these technologies could transform cancer care by facilitating precise, personalized, and adaptive treatment strategies. This review synthesizes current research, highlights innovative individual applications, and discusses the emerging integrations of nanomedicine and AI in oncology. The goal is to provide a comprehensive understanding of how these cutting-edge technologies can collaboratively improve cancer diagnosis, treatment, and patient prognosis.

Comparative Evaluation of Large Language and Multimodal Models in Detecting Spinal Stabilization Systems on X-Ray Images

Background/Objectives: Open-source AI models are increasingly applied in medical imaging, yet their effectiveness in detecting and classifying spinal stabilization systems remains underexplored. This study compares ChatGPT-4o (a large language model) and BiomedCLIP (a multimodal model) in their analysis of posturographic X-ray images (AP projection) to assess their accuracy in identifying the presence, type (growing vs. non-growing), and specific system (MCGR vs. PSF). Methods: A dataset of 270 X-ray images (93 without stabilization, 80 with MCGR, and 97 with PSF) was analyzed manually by neurosurgeons and evaluated using a three-stage AI-based questioning approach. Performance was assessed via classification accuracy, Gwet's Agreement Coefficient (AC1) for inter-rater reliability, and a two-tailed z-test for statistical significance (p < 0.05). Results: The results indicate that GPT-4o demonstrates high accuracy in detecting spinal stabilization systems, achieving near-perfect recognition (97-100%) for the presence or absence of stabilization. However, its consistency is reduced when distinguishing complex growing-rod (MCGR) configurations, with agreement scores dropping significantly (AC1 = 0.32-0.50). In contrast, BiomedCLIP displays greater response consistency (AC1 = 1.00) but struggles with detailed classification, particularly in recognizing PSF (11% accuracy) and MCGR (4.16% accuracy). Sensitivity analysis revealed GPT-4o's superior stability in hierarchical classification tasks, while BiomedCLIP excelled in binary detection but showed performance deterioration as the classification complexity increased. Conclusions: These findings highlight GPT-4o's robustness in clinical AI-assisted diagnostics, particularly for detailed differentiation of spinal stabilization systems, whereas BiomedCLIP's precision may require further optimization to enhance its applicability in complex radiographic evaluations.

Role of Artificial Intelligence and Personalized Medicine in Enhancing HIV Management and Treatment Outcomes

The integration of artificial intelligence and personalized medicine is transforming HIV management by enhancing diagnostics, treatment optimization, and disease monitoring. Advances in machine learning, deep neural networks, and multi-omics data analysis enable precise prognostication, tailored antiretroviral therapy, and early detection of drug resistance. AI-driven models analyze vast genomic, proteomic, and clinical datasets to refine treatment strategies, predict disease progression, and pre-empt therapy failures. Additionally, AI-powered diagnostic tools, including deep learning imaging and natural language processing, improve screening accuracy, particularly in resource-limited settings. Despite these innovations, challenges such as data privacy, algorithmic bias, and the need for clinical validation remain. Successful integration of AI into HIV care requires robust regulatory frameworks, interdisciplinary collaboration, and equitable technology access. This review explores both the potential and limitations of AI in HIV management, emphasizing the need for ethical implementation and expanded research to maximize its impact. AI-driven approaches hold great promise for a more personalized, efficient, and effective future in HIV treatment and care.

Integrating Digital Health Innovations to Achieve Universal Health Coverage: Promoting Health Outcomes and Quality Through Global Public Health Equity

Digital health innovations are reshaping global healthcare systems by enhancing access, efficiency, and quality of care. Technologies such as artificial intelligence, telemedicine, mobile health applications, and big data analytics have been widely applied to support disease surveillance, enable remote care, and improve clinical decision making. This review critically identifies persistent implementation challenges that hinder the equitable adoption of digital health solutions, such as the digital divide, limited infrastructure, and weak data governance, particularly in low- and middle-income countries (LMICs). It aims to propose strategic pathways for integrating digital innovations to strengthen universal health coverage (UHC) and bridge health disparities in the region. By analyzing the best global practices and emerging innovations, this study contributes to the ongoing dialogue on leveraging digital health for inclusive, scalable, and sustainable healthcare delivery in underserved regions.

Artificial intelligence in retinal image analysis for hypertensive retinopathy diagnosis: a comprehensive review and perspective

Hypertensive retinopathy (HR) occurs when the choroidal vessels, which form the photosensitive layer at the back of the eye, are injured owing to high blood pressure. Artificial intelligence (AI) in retinal image analysis (RIA) for HR diagnosis involves the use of advanced computational algorithms and machine learning (ML) strategies to recognize and evaluate signs of HR in retinal images automatically. This review aims to advance the field of HR diagnosis by investigating the latest ML and deep learning techniques, and highlighting their efficacy and capability for early diagnosis and intervention. By analyzing recent advancements and emerging trends, this study seeks to inspire further innovation in automated RIA. In this context, AI shows significant potential for enhancing the accuracy, effectiveness, and consistency of HR diagnoses. This will eventually lead to better clinical results by enabling earlier intervention and precise management of the condition. Overall, the integration of AI into RIA represents a considerable step forward in the early identification and treatment of HR, offering substantial benefits to both healthcare providers and patients.

Machine learning in prediction of epidermal growth factor receptor status in non-small cell lung cancer brain metastases: a systematic review and meta-analysis

BACKGROUND

Epidermal growth factor receptor (EGFR) mutations are present in 10-60% of all non-small cell lung cancer (NSCLC) patients and are associated with dismal prognosis. Lung cancer brain metastases (LCBM) are a common complication of lung cancer. Predictions of EGFR can help physicians in decision-making and, through optimizing treatment strategies, can result in more favorable outcomes. This systematic review and meta-analysis evaluated the predictive performance of machine learning (ML)-based models in EGFR status in NSCLC patients with brain metastasis.

METHODS

On December 20, 2024, the four electronic databases, Pubmed, Embase, Scopus, and Web of Science, were systematically searched. Studies that evaluated EGFR status in patients with brain metastasis from NSCLC were included.

RESULTS

Twenty studies with 3517 patients with 6205 NSCLC brain metastatic lesions were included. The majority of the best-performance models were ML-based (70%, 7/10), and deep learning (DL)-based models comprised 30% (6/20) of models. The area under the curve (AUC) and accuracy (ACC) of the best-performance models ranged from 0.765 to 1 and 0.69 to 0.93, respectively. The meta-analysis of the best-performance model revealed a pooled AUC of 0.91 (95%CI: 0.88-0.93) and ACC of 0.82 (95%CI: 0.79-0.86) along with a pooled sensitivity of 0.87 (95%CI: 0.83-0.9), specificity of 0.86 (95%CI: 0.79-0.9), and diagnostic odds ratio (DOR) of 35.2 (95%CI: 21.2-58.4). The subgroup analysis did not show significant differences between ML and DL models.

CONCLUSION

ML-based models demonstrated promising predictive outcomes in predicting EGFR status. Applying ML-based models in daily clinical practice can optimize treatment strategies and enhance clinical and radiological outcomes.

Machine Learning for Predicting Stillbirth: A Systematic Review

Stillbirth is a major global issue, with over 5 million cases each year. The multifactorial nature of stillbirth makes it difficult to predict. Artificial intelligence (AI) and machine learning (ML) have the potential to enhance clinical decision-making and enable precise assessments. This study reviewed the literature on predictive ML models for stillbirth highlighting input characteristics, performance metrics, and validation. The PubMed, Cochrane, and Web of Science databases were searched for studies using AI to develop predictive models for stillbirth. Findings were analyzed qualitatively using narrative synthesis and graphics. Risk of bias and the applicability of the studies were assessed using PROBAST. Model design and performance were discussed. Eight studies involving 14,840,654 women with gestational ages ranging from 20 weeks to full term were included in the qualitative analysis. Most studies used neural networks, random forests, and logistic regression algorithms. The number of predictive features varied from 14 to 53. Only 50% of studies validated the models. Cross-validation was commonly employed, and only 25% of studies performed external validation. All studies reported area under the curve as a performance metric (range 0.54-0.9), and five studies reported sensitivity (range, 60- 90%) and specificity (range, 64 - 93.3%). A stacked ensemble model that analyzed 53 features performed better than other models (AUC = 0.9; sensitivity and specificity > 85%). Available ML models can attain a considerable degree of accuracy for prediction of stillbirth; however, these models require further development before they can be applied in a clinical setting.

Artificial intelligence in stroke rehabilitation: From acute care to long-term recovery

Stroke is a leading cause of disability worldwide, driving the need for advanced rehabilitation strategies. The integration of Artificial Intelligence (AI) into stroke rehabilitation presents significant advancements across the continuum of care, from acute diagnosis to long-term recovery. This review explores AI's role in stroke rehabilitation, highlighting its impact on early diagnosis, motor recovery, and cognitive rehabilitation. AI-driven imaging techniques, such as deep learning applied to CT and MRI scans, improve early diagnosis and identify ischemic penumbra, enabling timely, personalized interventions. AI-assisted decision support systems optimize acute stroke treatment, including thrombolysis and endovascular therapy. In motor rehabilitation, AI-powered robotics and exoskeletons provide precise, adaptive assistance, while AI-augmented Virtual and Augmented Reality environments offer immersive, tailored recovery experiences. Brain-Computer Interfaces utilize AI for neurorehabilitation through neural signal processing, supporting motor recovery. Machine learning models predict functional recovery outcomes and dynamically adjust therapy intensities. Wearable technologies equipped with AI enable continuous monitoring and real-time feedback, facilitating home-based rehabilitation. AI-driven tele-rehabilitation platforms overcome geographic barriers by enabling remote assessment and intervention. The review also addresses the ethical, legal, and regulatory challenges associated with AI implementation, including data privacy and technical integration. Future research directions emphasize the transformative potential of AI in stroke rehabilitation, with case studies and clinical trials illustrating the practical benefits and efficacy of AI technologies in improving patient recovery.

Intelligent Care: A Scientometric Analysis of Artificial Intelligence in Precision Medicine

The integration of advanced computational methods into precision medicine represents a transformative advancement in healthcare, enabling highly personalized treatment strategies based on individual genetic, environmental, and lifestyle factors. These methodologies have significantly enhanced disease diagnostics, genomic analysis, and drug discovery. However, rapid expansion in this field has resulted in fragmented understandings of its evolution and persistent knowledge gaps. This study employs a scientometric approach to systematically map the research landscape, identify key contributors, and highlight emerging trends in precision medicine. Methods: A scientometric analysis was conducted using data retrieved from the Scopus database, covering publications from 2019 to 2024. Tools such as VOSviewer and R-bibliometrix package (version 4.3.0) were used to perform co-authorship analysis, co-citation mapping, and keyword evolution tracking. The study examined annual publication growth, citation impact, research productivity by country and institution, and thematic clustering to identify core research areas. Results: The analysis identified 4574 relevant publications, collectively amassing 70,474 citations. A rapid growth trajectory was observed, with a 34.3% increase in publications in 2024 alone. The United States, China, and Germany emerged as the top contributors, with Harvard Medical School, the Mayo Clinic, and Sichuan University leading in institutional productivity. Co-citation and keyword analysis revealed three primary research themes: diagnostics and medical imaging, genomic and multi-omics data integration, and personalized treatment strategies. Recent trends indicate a shift toward enhanced clinical decision support systems and precision drug discovery. Conclusions: Advanced computational methods are revolutionizing precision medicine, spurring increased global research collaboration and rapidly evolving methodologies. This study provides a comprehensive knowledge framework, highlighting key developments and future directions. The insights derived can inform policy decisions, funding allocations, and interdisciplinary collaborations, driving further advancements in healthcare solutions.

Development of machine learning models to predict the risk of fungal infection following flexible ureteroscopy lithotripsy

BACKGROUND

The flexible ureteroscopy lithotripsy (F-URL) is an important treatment for upper urinary tract stones. However, urolithiasis, surgical procedures, and catheter placement are risk factors for fungal infections. Our study aimed to construct a machine learning algorithm predictive model to predict the risk of fungal infection following F-URL.

METHODS

This study retrospectively collected the clinical data of patients who underwent F-URL at the Second Affiliated Hospital of Zhengzhou University from January 2016 to March 2024. The patients were divided into a non-fungal infection group and a fungal infection group based on whether a fungal infection occurred within three months post-surgery. The patient data from January 2016 to December 2023 were used as training data, and the patient data from January 2024 to March 2024 were used as testing set. The training data was randomly divided into a training set and validation set at a ratio of 90:10. Use LASSO regression to screen clinical features based on the training set. Nine machine learning algorithms, Logistic Regression (LR), k-Nearest Neighbours (KNN), Support Vector Machines (SVM), Random Forest (RF), Categorical Boosting (CatBoost), eXtreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), Gradient Boosting Machines (GBM), and Neural Network (NNet), were used to construct models. The performance of these nine models was evaluated and the best predictive model was selected based on the validation set, and evaluate the best predictive model's generalization ability using the testing set. Visualize the constructed optimal machine learning model using the SHapley additive interpretation (SHAP) value method. SHAP force plots were established to show the application of the prediction model at the individual level.

RESULTS

A total of 13 clinical features were used to construct predictive models: age, diabetes mellitus (DM), history of malignancy, being bedridden, admission white blood cells (WBC), preoperative ureteral stenting, operation time, postoperative fever, postoperative Neu, carbapenem antibiotics use, duration of antibiotic therapy, length of hospital stay (LOS), and postoperative stent duration. Comparing the performance of 9 prediction models, we found that the model constructed using XGBoost algorithm had the best performance. The model constructed using XGBoost algorithm shows good discrimination, generalization and clinical applicability in the testing set.

CONCLUSIONS

The XGBoost model developed in this study has good predictive ability and clinical applicability for evaluating the risk of fungal infection following F-URL.

Prediction of outpatient visits for allergic rhinitis using an artificial intelligence LSTM model - a study in Eastern China

BACKGROUND

Allergic rhinitis is a common disease that can affect the health of patients and bring huge social and economic burdens. In this study, we developed a model to predict the incidence rate of allergic rhinitis so as to provide accurate information for the treatment, prevention, and control of allergic rhinitis.

METHODS

We developed a Long Short-Term Memory model for effectively predicting the daily outpatient visits of allergic rhinitis patients based on air pollution and meteorological data. We collected the outpatient data from the departments of otolaryngology, emergency medicine, pediatrics, and respiratory medicine at the Affiliated Hospital of Hangzhou Normal University, from January 2022 to August 2024. The data were stratified by gender and age and were separately input into the model for evaluation. A total of 25,425 outpatient data samples were assessed in this study.

RESULTS

Based on the data obtained from males (n = 13,943), females (n = 11,482), adults (n = 17,473), and minors (n = 7,952), the normalized mean squared errors of the Long Short-Term Memory model were 0.4674976, 0.3812502, 0.418301, and 0.4322124, respectively. By comparing the NMSE prediction results of ARIMA and LSTM models on this dataset, the LSTM model was found to outperform the ARIMA model in terms of stability and accuracy.

CONCLUSIONS

The model presented here could effectively predict the daily outpatient visits for allergic rhinitis patients based on air pollution and meteorological data, thereby offering valuable data-driven support for hospital management and for potentially improving societal management and prevention of allergic rhinitis.

Artificial Intelligence in Dentistry: A Narrative Review of Diagnostic and Therapeutic Applications

Advancements in digital and precision medicine have fostered the rapid development of artificial intelligence (AI) applications, including machine learning, artificial neural networks (ANN), and deep learning, within the field of dentistry, particularly in imaging diagnosis and treatment. This review examines the progress of AI across various domains of dentistry, focusing on its role in enhancing diagnostics and optimizing treatment for oral diseases such as endodontic disease, periodontal disease, oral implantology, orthodontics, prosthodontic treatment, and oral and maxillofacial surgery. Additionally, it discusses the emerging opportunities and challenges associated with these technologies. The findings indicate that AI can be effectively utilized in numerous aspects of oral healthcare, including prevention, early screening, accurate diagnosis, treatment plan design assistance, treatment execution, follow-up monitoring, and prognosis assessment. However, notable challenges persist, including issues related to inaccurate data annotation, limited capability for fine-grained feature expression, a lack of universally applicable models, potential biases in learning algorithms, and legal risks pertaining to medical malpractice and data privacy breaches. Looking forward, future research is expected to concentrate on overcoming these challenges to enhance the accuracy and applicability of AI in diagnosing and treating oral diseases. This review aims to provide a comprehensive overview of the current state of AI in dentistry and to identify pathways for its effective integration into clinical practice.

A Machine Learning Model to Predict De Novo Hepatocellular Carcinoma Beyond Year 5 of Antiviral Therapy in Patients With Chronic Hepatitis B

BACKGROUND AND AIMS

This study aims to develop and validate a machine learning (ML) model predicting hepatocellular carcinoma (HCC) in chronic hepatitis B (CHB) patients after the first 5 years of entecavir (ETV) or tenofovir (TFV) therapy.

METHODS

CHB patients treated with ETV/TFV for > 5 years and not diagnosed with HCC during the first 5 years of therapy were selected from two hospitals. We used 36 variables, including baseline characteristics (age, sex, cirrhosis, and type of antiviral agent) and laboratory values (at baseline, at 5 years, and changes between 5 years) for model development. Five machine learning algorithms were applied to the training dataset and internally validated using a test dataset. External validation was performed.

RESULTS

In years 5-15, a total of 279/5908 (4.7%) and 25/562 (4.5%) patients developed HCC in the derivation and external validation cohorts, respectively. In the training dataset (n = 4726), logistic regression showed the highest area under the receiver operating curve (AUC) of 0.803 and a balanced accuracy of 0.735, outperforming other ML algorithms. An ensemble model combining logistic regression and random forest performed best (AUC, 0.811 and balanced accuracy, 0.754). The results from the test dataset (n = 1182) verified the good performance of the ensemble model (AUC, 0.784 and balanced accuracy, 0.712). External validation confirmed the predictive accuracy of our ensemble model (AUC, 0.862 and balanced accuracy, 0.771). A web-based calculator was developed (http://ai-wm.khu.ac.kr/HCC/).

CONCLUSIONS

The proposed ML model excellently predicted HCC risk beyond year 5 of ETV/TFV therapy and, therefore, could facilitate individualised HCC surveillance based on risk stratification.

Optimising Nurse-Patient Assignments: The Impact of Machine Learning Model on Care Dynamics-Discursive Paper

BACKGROUND

Machine learning (ML) models can enhance patient-nurse assignments in healthcare organisations by learning from real data and identifying key capabilities. Nurses must develop innovative ideas for adapting to the dynamic environment, managing staffing and establishing flexible workforce solutions.

AIM

This discursive paper discusses the application of ML in optimising patient-nurse assignments within healthcare settings, considering various factors such as staff skill mix, patient acuity, cultural competencies and language considerations.

METHODS

A discursive approach was used to optimise nurse-patient assignments and the impact of ML models. Through a review of traditional and emerging perspectives, factors such as staff skill mix, patient acuity, cultural competencies and language-related challenges were emphasised.

RESULTS

Machine learning models can potentially enhance healthcare patient-nurse assignments by considering skill integration, acuity level assessment and cultural and language barrier awareness. Thus, models have the potential to optimise patient care through dynamic adjustments.

CONCLUSION

The application of ML models in optimising patient-nurse assignments presents significant opportunities for improving healthcare delivery. Future research should focus on refining algorithms, ensuring real-time adaptability, addressing ethical considerations, evaluating long-term patient outcomes, fostering cooperative systems, and integrating relevant data and policies within the healthcare framework. No patient or public contribution.

Incretin-based therapies: advancements, challenges, and future directions in type 2 diabetes management

Incretin-based medicines have considerably impacted the treatment of type 2 diabetes mellitus (T2DM), providing considerable advantages in glycemic regulation, weight control, and cardiovascular results. This narrative review examines progress in incretin medicines, encompassing glucagon-like peptide-1 (GLP-1) receptor agonists, dual-receptor, and triple-receptor agonists, while emphasizing their therapeutic advantages, obstacles, and prospective developments. The examined articles were sourced from databases including PubMed and Google Scholar, concentrating on publications predominantly from 2010 to 2024. Selective foundational papers released before this timeline were incorporated to furnish critical historical context about incretin processes and their discovery. Incretin-based medicines, despite their therapeutic efficacy, encounter hurdles including elevated treatment costs, patient compliance difficulties, and variability in response attributable to genetic and physiological variables. Moreover, there are still deficiencies in comprehending the long-term cardiovascular safety and cancer risks linked to these medicines. Emerging dual- and triple-receptor agonists demonstrate potential in overcoming the shortcomings of conventional GLP-1 receptor agonists, providing enhanced metabolic results and broader uses in intricate disease profiles. Future research must concentrate on economic obstacles, streamlined regimens, customized medicine, the integration of artificial intelligence, patient stratification, as well as the safety and efficacy of incretin-based medicines for holistic management of T2DM.

Leveraging large language models to mimic domain expert labeling in unstructured text-based electronic healthcare records in non-english languages

BACKGROUND

The integration of big data and artificial intelligence (AI) in healthcare, particularly through the analysis of electronic health records (EHR), presents significant opportunities for improving diagnostic accuracy and patient outcomes. However, the challenge of processing and accurately labeling vast amounts of unstructured data remains a critical bottleneck, necessitating efficient and reliable solutions. This study investigates the ability of domain specific, fine-tuned large language models (LLMs) to classify unstructured EHR texts with typographical errors through named entity recognition tasks, aiming to improve the efficiency and reliability of supervised learning AI models in healthcare.

METHODS

Turkish clinical notes from pediatric emergency room admissions at Hacettepe University İhsan Doğramacı Children's Hospital from 2018 to 2023 were analyzed. The data were preprocessed with open source Python libraries and categorized using a pretrained GPT-3 model, "text-davinci-003," before and after fine-tuning with domain-specific data on respiratory tract infections (RTI). The model's predictions were compared against ground truth labels established by pediatric specialists.

RESULTS

Out of 24,229 patient records classified as poorly labeled, 18,879 were identified without typographical errors and confirmed for RTI through filtering methods. The fine-tuned model achieved a 99.88% accuracy, significantly outperforming the pretrained model's 78.54% accuracy in identifying RTI cases among the remaining records. The fine-tuned model demonstrated superior performance metrics across all evaluated aspects compared to the pretrained model.

CONCLUSIONS

Fine-tuned LLMs can categorize unstructured EHR data with high accuracy, closely approximating the performance of domain experts. This approach significantly reduces the time and costs associated with manual data labeling, demonstrating the potential to streamline the processing of large-scale healthcare data for AI applications.

Machine learning radiomics for H3K27M mutation prediction in gliomas: A systematic review and meta-analysis

PURPOSE

Noninvasive prediction and identification of the H3K27M mutation play an important role in optimizing therapeutic strategies and improving outcomes in gliomas. In this systematic review and meta-analysis, we aimed to evaluate the performance of machine learning (ML)-based models in predicting H3K27M mutation in gliomas.

METHODS

Literature records were retrieved on September 16th, 2024, in PubMed, Embase, Scopus, and Web of Science. Records were screened according to the eligibility criteria, and the data from the included studies were extracted. The meta-analysis, sensitivity analysis, and meta-regression were conducted using R software.

RESULTS

A total of 15 studies were included in our study. Our meta-analysis demonstrated a pooled AUC, sensitivity, and specificity of 0.87 (95% CI: 0.77-0.97), 92% (95% CI: 83%-96%), and 89% (95% CI: 86%-91%)), respectively. The subgroup meta-analysis revealed that despite the higher sensitivity of the deep learning (DL) models, the sensitivity is not superior to ML (P = 0.6). In contrast, the ML-based pooled specificity was significantly higher (P < 0.01). The meta-analysis revealed a 78.1 (95% CI: 33.3 - 183.5). The SROC curve indicated an AUC of 0.921, and the estimated sensitivity is 0.898 concurrent with the false positive rate of 0.126, which indicates high sensitivity with a low false positive rate.

CONCLUSION

Our systematic review and meta-analysis demonstrated that ML-based magnetic resonance imaging (MRI) radiomics models are associated with promising diagnostic performance in predicting H3K27M mutation in gliomas.

Analysis of Personalized Cardiovascular Drug Therapy: From Monitoring Technologies to Data Integration and Future Perspectives

Cardiovascular diseases have long been a major challenge to human health, and the treatment differences caused by individual variability remain unresolved. In recent years, personalized cardiovascular drug therapy has attracted widespread attention. This paper reviews the strategies for achieving personalized cardiovascular drug therapy through traditional dynamic monitoring and multidimensional data integration and analysis. It focuses on key technologies for dynamic monitoring, dynamic monitoring based on individual differences, and multidimensional data integration and analysis. By systematically reviewing the relevant literature, the main challenges in current research and the proposed potential directions for future studies were summarized.

Performance of DeepSeek, Qwen 2.5 MAX, and ChatGPT Assisting in Diagnosis of Corneal Eye Diseases, Glaucoma, and Neuro-Ophthalmology Diseases Based on Clinical Case Reports

Background

This study evaluates the diagnostic performance of several AI models, including Deepseek, in diagnosing corneal diseases, glaucoma, and neuro□ophthalmologic disorders.

Methods

We retrospectively selected 53 case reports from the Department of Ophthalmology and Visual Sciences at the University of Iowa, comprising 20 corneal disease cases, 11 glaucoma cases, and 22 neuro□ophthalmology cases. The case descriptions were input into DeepSeek, ChatGPT□4.0, ChatGPT□01, and Qwens 2.5 Max. These responses were compared with diagnoses rendered by human experts (corneal specialists, glaucoma attendings, and neuro□ophthalmologists). Diagnostic accuracy and interobserver agreement, defined as the percentage difference between each AI model's performance and the average human expert performance, were determined.

Results

DeepSeek achieved an overall diagnostic accuracy of 79.2%, with specialty-specific accuracies of 90.0% in corneal diseases, 54.5% in glaucoma, and 81.8% in neuro□ophthalmology. ChatGPT□01 outperformed the other models with an overall accuracy of 84.9% (85.0% in corneal diseases, 63.6% in glaucoma, and 95.5% in neuro□ophthalmology), while Qwens exhibited a lower overall accuracy of 64.2% (55.0% in corneal diseases, 54.5% in glaucoma, and 77.3% in neuro□ophthalmology). Interobserver agreement analysis revealed that in corneal diseases, DeepSeek differed by -3.3% (90.0% vs 93.3%), ChatGPT□01 by -8.3%, and Qwens by -38.3%. In glaucoma, DeepSeek outperformed the human expert average by +3.0% (54.5% vs 51.5%), while ChatGPT□4.0 and ChatGPT□01 exceeded it by +12.1%, and Qwens was +3.0% above the human average. In neuro□ophthalmology, DeepSeek and ChatGPT□4.0 were 9.1% lower than the human average, ChatGPT□01 exceeded it by +4.6%, and Qwens was 13.6% lower.

Conclusions

ChatGPT□01 demonstrated the highest overall diagnostic accuracy, especially in neuro□ophthalmology, while DeepSeek and ChatGPT□4.0 showed comparable performance. Qwens underperformed relative to the other models, especially in corneal diseases. Although these AI models exhibit promising diagnostic capabilities, they currently lag behind human experts in certain areas, underscoring the need for a collaborative integration of clinical judgment.

Plain Language Summary

This study evaluated how well several artificial intelligence (AI) models diagnose eye diseases compared to human experts. We tested four AI systems across three types of eye conditions: diseases of the cornea, glaucoma, and neuro-ophthalmologic disorders. Overall, one AI model, ChatGPT-01, performed the best, correctly diagnosing about 85% of cases, and it excelled in neuro-ophthalmology by correctly diagnosing 95.5% of cases. Two other models, DeepSeek and ChatGPT-4.0, each achieved an overall accuracy of around 79%, while the Qwens model performed lower, with an overall accuracy of about 64%. When compared with human experts, who achieved very high accuracy in corneal diseases (93.3%) and neuro-ophthalmology (90.9%) but lower in glaucoma (51.5%), the AI models showed mixed results. In glaucoma, for instance, some AI models even outperformed human experts slightly, while in corneal diseases, all AI models were less accurate than the experts. These findings indicate that while AI shows promise as a supportive tool in diagnosing eye conditions, it still needs further improvement. Combining AI with human clinical judgment appears to be the best approach for accurate eye disease diagnosis.

Key summary points

Why carry out this study? With the rising burden of eye diseases and the inherent diagnostic challenges for complex conditions like glaucoma and neuro-ophthalmologic disorders, there is an unmet need for innovative diagnostic tools to support clinical decision-making. What did the study ask? This study evaluated the diagnostic performance of four AI models across three ophthalmologic subspecialties, testing the hypothesis that advanced language models can achieve accuracy levels comparable to human experts. What was learned from the study? Our results showed that ChatGPT-01 achieved the highest overall accuracy (84.9%), excelling in neuro-ophthalmology with a 95.5% accuracy, while DeepSeek and ChatGPT-4.0 each achieved 79.2%, and Qwens reached 64.2%. What specific outcomes were observed? In glaucoma, AI model accuracies ranged from 54.5% to 63.6%, with some models slightly surpassing the human expert average of 51.5%, underscoring the diagnostic difficulty of this condition. What has been learned and future implications? These findings highlight the potential of AI as a valuable adjunct to clinical judgment in ophthalmology, although further research and the integration of multimodal data are essential to optimize these tools for routine clinical practice.

Big data analytics and machine learning in hematology: Transformative insights, applications and challenges

The integration of big data analytics and machine learning (ML) into hematology has ushered in a new era of precision medicine, offering transformative insights into disease management. By leveraging vast and diverse datasets, including genomic profiles, clinical laboratory results, and imaging data, these technologies enhance diagnostic accuracy, enable robust prognostic modeling, and support personalized therapeutic interventions. Advanced ML algorithms, such as neural networks and ensemble learning, facilitate the discovery of novel biomarkers and refine risk stratification for hematological disorders, including leukemias, lymphomas, and coagulopathies. Despite these advancements, significant challenges persist, particularly in the realms of data integration, algorithm validation, and ethical concerns. The heterogeneity of hematological datasets and the lack of standardized frameworks complicate their application, while the "black-box" nature of ML models raises issues of reliability and clinical trust. Moreover, safeguarding patient privacy in an era of data-driven medicine remains paramount, necessitating the development of secure and ethical analytical practices. Addressing these challenges is critical to ensuring equitable and effective implementation of these technologies. Collaborative efforts between hematologists, data scientists, and bioinformaticians are pivotal in translating these innovations into real-world clinical practice. Emphasis on developing explainable artificial intelligence models, integrating real-time analytics, and adopting federated learning approaches will further enhance the utility and adoption of these technologies. As big data analytics and ML continue to evolve, their potential to revolutionize hematology and improve patient outcomes remains immense.

A Critical Review of the Prospect of Integrating Artificial Intelligence in Infectious Disease Diagnosis and Prognosis

This paper explores the transformative potential of integrating artificial intelligence (AI) in the diagnosis and prognosis of infectious diseases. By analyzing diverse datasets, including clinical symptoms, laboratory results, and imaging data, AI algorithms can significantly enhance early detection and personalized treatment strategies. This paper reviews how AI-driven models improve diagnostic accuracy, predict patient outcomes, and contribute to effective disease management. It also addresses the challenges and ethical considerations associated with AI, including data privacy, algorithmic bias, and equitable access to healthcare. Highlighting case studies and recent advancements, the paper underscores AI's role in revolutionizing infectious disease management and its implications for future healthcare delivery.

A novel hybrid ViT-LSTM model with explainable AI for brain stroke detection and classification in CT images: A case study of Rajshahi region

Computed tomography (CT) scans play a key role in the diagnosis of stroke, a leading cause of morbidity and mortality worldwide. However, interpreting these scans is often challenging, necessitating automated solutions for timely and accurate diagnosis. This research proposed a novel hybrid model that integrates a Vision Transformer (ViT) and a Long Short Term Memory (LSTM) to accurately detect and classify stroke characteristics using CT images. The ViT identifies essential features from CT images, while LSTM processes sequential information generated by the ViT, adept at capturing crucial temporal dependencies for understanding patterns and context in sequential data. Moreover, our approach addresses class imbalance issues in stroke datasets by utilizing advanced strategies to improve model robustness. To ensure clinical relevance, Explainable Artificial Intelligence (XAI) methods, including attention maps, SHAP, and LIME, were incorporated to provide reliable and interpretable predictions. The proposed model was evaluated using the primary BrSCTHD-2023 dataset, collected from Rajshahi Medical College Hospital, achieving top accuracies of 73.80%, 91.61%, 93.50%, and 94.55% with the SGD, RMSProp, Adam, and AdamW optimizers, respectively. To further validate and generalize the model, it was also tested on the Kaggle brain stroke dataset, where it achieved an impressive accuracy of 96.61%. The proposed ViT-LSTM model significantly outperformed traditional CNNs and ViT models, demonstrating superior diagnostic performance and generalizability. This study advances automated stroke diagnosis by combining deep learning innovations, domain expertise, and enhanced interpretability to support clinical decision-making, providing reliable diagnostic solutions.

Integrating machine learning and multitargeted drug design to combat antimicrobial resistance: a systematic review

Antimicrobial resistance (AMR) is a critical global health challenge, undermining the efficacy of antimicrobial drugs against microorganisms like bacteria, fungi and viruses. Multidrug resistance (MDR) arises when microorganisms become resistant to multiple antimicrobial agents. The World Health Organisation classifies AMR bacteria into priority list - I (critical), II (high) and III (medium), prompting action from nearly 170 countries. Six priority bacterial strains account for over 70% of AMR-related fatalities, contributing to more than 1.3 million direct deaths annually and linked to over 5 million deaths globally. Enterobacteriaceae, including Escherichia coli, Salmonella enterica and Klebsiella pneumoniae, significantly contribute to AMR fatalities. This systematic literature review explores how machine learning (ML) and multitargeted drug design (MTDD) can combat AMR in Enterobacteriaceae. We followed PRISMA guidelines and comprehensively analysed current prospects and limitations by mining PubMed and Scopus literature databases. Innovative strategies integrating AI algorithms with advanced computational techniques allow for the analysis of vast datasets, identification of novel drug targets, prediction of resistance mechanisms, and optimisation of drug molecules to overcome resistance. Leveraging ML and MTDD is crucial for both advancing our fight against AMR in Enterobacteriaceae, and developing combination therapies that target multiple bacterial survival pathways, reducing the risk of resistance development.

Current perspectives in tackling glaucoma blindness

As a major reason for irreversible vision loss, glaucoma is a significant public health concern. Its multifactorial nature demands a nuanced understanding of its pathophysiology, risk factors, and management. An understanding, and continuous refinement, of diagnostic and therapeutic modalities, including pharmacological interventions, novel methods of drug delivery, and surgical techniques (including minimally invasive glaucoma surgeries) are critical. The advent of personalized medicine, genetic profiling, and innovative biomarkers for identifying susceptible individuals and tailoring treatment strategies may help prevent blindness and improve patient outcomes. Evaluation of the impact of lifestyle modifications and holistic approaches and integration of telemedicine and artificial intelligence in glaucoma management may revolutionize current glaucoma practice. In addressing the global challenge of glaucoma blindness, this narrative review highlights ongoing initiatives, public health policies, and community-based interventions. This includes raising awareness, enhancing early detection programs, and access to care, particularly in underserved populations.

Physical activity and neuroplasticity in neurodegenerative disorders: a comprehensive review of exercise interventions, cognitive training, and AI applications

This review aimed to elucidate the mechanisms through which (i) physical activity (PA) enhances neuroplasticity and cognitive function in neurodegenerative disorders, and (ii) identify specific PA interventions for improving cognitive rehabilitation programs. We conducted a literature search in PubMed, Medline, Scopus, Web of Science, and PsycINFO, covering publications from January 1990 to August 2024. The search strategy employed key terms related to neuroplasticity, physical exercise, cognitive function, neurodegenerative disorders, and personalized physical activity. Inclusion criteria included original research on the relationship between PA and neuroplasticity in neurodegenerative disorders, while exclusion criteria eliminated studies focusing solely on pharmacological interventions. The review identified multiple pathways through which PA may enhance neuroplasticity, including releasing neurotrophic factors, modulation of neuroinflammation, reduction of oxidative stress, and enhancement of synaptic connectivity and neurogenesis. Aerobic exercise was found to increase hippocampal volume by 1-2% and improve executive function scores by 5-10% in older adults. Resistance training enhanced cognitive control and memory performance by 12-18% in elderly individuals. Mind-body exercises, such as yoga and tai-chi, improved gray matter density in memory-related brain regions by 3-5% and enhanced emotional regulation scores by 15-20%. Dual-task training improved attention and processing speed by 8-14% in individuals with neurodegenerative disorders. We also discuss the potential role of AI-based exercise and AI cognitive training in preventing and rehabilitating neurodegenerative illnesses, highlighting innovative approaches to personalized interventions and improved patient outcomes. PA significantly enhances neuroplasticity and cognitive function in neurodegenerative disorders through various mechanisms. Aerobic exercise, resistance training, mind-body practices, and dual-task exercises each offer unique cognitive benefits. Implementing these activities in clinical settings can improve patient outcomes. Future research should focus on creating personalized interventions tailored to specific conditions, incorporating personalized physical exercise programs to optimize cognitive rehabilitation.

Advances in Wearable Biosensors for Wound Healing and Infection Monitoring

Wound healing is a complicated biological process that is important for restoring tissue integrity and function after injury. Infection, usually due to bacterial colonization, significantly complicates this process by hindering the course of healing and enhancing the chances of systemic complications. Recent advances in wearable biosensors have transformed wound care by making real-time monitoring of biomarkers such as pH, temperature, moisture, and infection-related metabolites like trimethylamine and uric acid. This review focuses on recent advances in biosensor technologies designed for wound management. Novel sensor architectures, such as flexible and stretchable electronics, colorimetric patches, and electrochemical platforms, enable the non-invasive detection of changes associated with wounds with high specificity and sensitivity. These are increasingly combined with AI and analytics based on smartphones that can enable timely and personalized interventions. Examples are the PETAL patch sensor that applies multiple sensing mechanisms for wide-ranging views on wound status and closed-loop systems that connect biosensors to therapeutic devices to automate infection control. Additionally, self-powered biosensors that tap into body heat or energy from the biofluids themselves avoid any external batteries and are thus more effective in field use or with limited resources. Internet of Things connectivity allows further support for remote sharing and monitoring of data, thus supporting telemedicine applications. Although wearable biosensors have developed relatively rapidly and their prospects continue to expand, regular clinical application is stalled by significant challenges such as regulatory, cost, patient compliance, and technical problems related to sensor accuracy, biofouling, and power, among others, that need to be addressed by innovative solutions. The goal of this review is to synthesize current trends, challenges, and future directions in wound healing and infection monitoring, with emphasis on the potential for wearable biosensors to improve patient outcomes and reduce healthcare burdens. These innovations are leading the way toward next-generation wound care by bridging advanced materials science, biotechnology, and digital health.

Building an intelligent diabetes Q&A system with knowledge graphs and large language models

Introduction

This paper introduces an intelligent question-answering system designed to deliver personalized medical information to diabetic patients. By integrating large language models with knowledge graphs, the system aims to provide more accurate and contextually relevant medical guidance, addressing the limitations of traditional healthcare systems in handling complex medical queries.

Methods

The system combines a Neo4j-based knowledge graph with the Baichuan2-13B and Qwen2.5-7B models. To enhance performance, Low-Rank Adaptation (LoRA) and prompt-based learning techniques are applied. These methods improve the system's semantic understanding and ability to generate high-quality responses. The system's performance is evaluated using entity recognition and intent classification tasks.

Results

The system achieves 85.91% precision in entity recognition and 88.55% precision in intent classification. The integration of a structured knowledge graph significantly improves the system's accuracy and clinical relevance, enhancing its ability to provide personalized medical responses for diabetes management.

Discussion

This study demonstrates the effectiveness of integrating large language models with structured knowledge graphs to improve medical question-answering systems. The proposed approach offers a promising framework for advancing diabetes management and other healthcare applications, providing a solid foundation for future personalized healthcare interventions.

Transformative Approaches in Breast Cancer Detection: Integrating Transformers into Computer-Aided Diagnosis for Histopathological Classification

Breast cancer (BC) remains a leading cause of cancer-related mortality among women worldwide, necessitating advancements in diagnostic methodologies to improve early detection and treatment outcomes. This study proposes a novel twin-stream approach for histopathological image classification, utilizing both histopathologically inherited and vision-based features to enhance diagnostic precision. The first stream utilizes Virchow2, a deep learning model designed to extract high-level histopathological features, while the second stream employs Nomic, a vision-based transformer model, to capture spatial and contextual information. The fusion of these streams ensures a comprehensive feature representation, enabling the model to achieve state-of-the-art performance on the BACH dataset. Experimental results demonstrate the superiority of the twin-stream approach, with a mean accuracy of 98.60% and specificity of 99.07%, significantly outperforming single-stream methods and related studies. Statistical analyses, including paired t-tests, ANOVA, and correlation studies, confirm the robustness and reliability of the model. The proposed approach not only improves diagnostic accuracy but also offers a scalable and efficient solution for clinical applications, addressing the challenges of resource constraints and increasing diagnostic demands.

BioMapAI: Artificial Intelligence Multi-Omics Modeling of Myalgic Encephalomyelitis / Chronic Fatigue Syndrome

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a chronic illness with a multifactorial etiology and heterogeneous symptomatology, posing major challenges for diagnosis and treatment. Here, we present BioMapAI, a supervised deep neural network trained on a four-year, longitudinal, multi-omics dataset from 249 participants, which integrates gut metagenomics, plasma metabolomics, immune cell profiling, blood laboratory data, and detailed clinical symptoms. By simultaneously modeling these diverse data types to predict clinical severity, BioMapAI identifies disease- and symptom-specific biomarkers and robustly classifies ME/CFS in both held-out and independent external cohorts. Using an explainable AI approach, we construct the first connectivity map spanning the microbiome, immune system, and plasma metabolome in health and ME/CFS, adjusted for age, gender, and additional clinical factors. This map uncovers disrupted associations between microbial metabolism (e.g., short-chain fatty acids, branched-chain amino acids, tryptophan, benzoate), plasma lipids and bile acids, and heightened inflammatory responses in mucosal and inflammatory T cell subsets (MAIT, γδT) secreting IFNγ and GzA. Overall, BioMapAI provides unprecedented systems-level insights into ME/CFS, refining existing hypotheses and hypothesizing new pathways associated to the disease's heterogeneous symptoms.

Integrating radiological and clinical data for clinically significant prostate cancer detection with machine learning techniques

In prostate cancer (PCa), risk calculators have been proposed, relying on clinical parameters and magnetic resonance imaging (MRI) enable early prediction of clinically significant cancer (CsPCa). The prostate imaging-reporting and data system (PI-RADS) is combined with clinical variables predominantly based on logistic regression models. This study explores modeling using regularization techniques such as ridge regression, LASSO, elastic net, classification tree, tree ensemble models like random forest or XGBoost, and neural networks to predict CsPCa in a dataset of 4799 patients in Catalonia (Spain). An 80-20% split was employed for training and validation. We used predictor variables such as age, prostate-specific antigen (PSA), prostate volume, PSA density (PSAD), digital rectal exam (DRE) findings, family history of PCa, a previous negative biopsy, and PI-RADS categories. When considering a sensitivity of 0.9, in the validation set, the XGBoost model outperforms others with a specificity of 0.640, followed closely by random forest (0.638), neural network (0.634), and logistic regression (0.620). In terms of clinical utility, for a 10% missclassification of CsPCa, XGBoost can avoid 41.77% of unnecessary biopsies, followed closely by random forest (41.67%) and neural networks (41.46%), while logistic regression has a lower rate of 40.62%. Using SHAP values for model explainability, PI-RADS emerges as the most influential risk factor, particularly for individuals with PI-RADS 4 and 5. Additionally, a positive digital rectal examination (DRE) or family history of prostate cancer proves highly influential for certain individuals, while a previous negative biopsy serves as a protective factor for others.

Computer vision algorithms in healthcare: Recent advancements and future challenges

Computer vision has emerged as a promising technology with numerous applications in healthcare. This systematic review provides an overview of advancements and challenges associated with computer vision in healthcare. The review highlights the application areas where computer vision has made significant strides, including medical imaging, surgical assistance, remote patient monitoring, and telehealth. Additionally, it addresses the challenges related to data quality, privacy, model interpretability, and integration with existing healthcare systems. Ethical and legal considerations, such as patient consent and algorithmic bias, are also discussed. The review concludes by identifying future directions and opportunities for research, emphasizing the potential impact of computer vision on healthcare delivery and outcomes. Overall, this systematic review underscores the importance of understanding both the advancements and challenges in computer vision to facilitate its responsible implementation in healthcare.

Advancing Medical Research Through Artificial Intelligence: Progressive and Transformative Strategies: A Literature Review

Background and Aims

Artificial intelligence (AI) has become integral to medical research, impacting various aspects such as data analysis, writing assistance, and publishing. This paper explores the multifaceted influence of AI on the process of writing medical research papers, encompassing data analysis, ethical considerations, writing assistance, and publishing efficiency.

Methods

The review was conducted following the PRISMA guidelines; a comprehensive search was performed in Scopus, PubMed, EMBASE, and MEDLINE databases for research publications on artificial intelligence in medical research published up to October 2023.

Results

AI facilitates the writing process by generating drafts, offering grammar and style suggestions, and enhancing manuscript quality through advanced models like ChatGPT. Ethical concerns regarding content ownership and potential biases in AI-generated content underscore the need for collaborative efforts among researchers, publishers, and AI creators to establish ethical standards. Moreover, AI significantly influences data analysis in healthcare, optimizing outcomes and patient care, particularly in fields such as obstetrics and gynecology and pharmaceutical research. The application of AI in publishing, ranging from peer review to manuscript quality control and journal matching, underscores its potential to streamline and enhance the entire research and publication process. Overall, while AI presents substantial benefits, ongoing research, and ethical guidelines are essential for its responsible integration into the evolving landscape of medical research and publishing.

Conclusion

The integration of AI in medical research has revolutionized efficiency and innovation, impacting data analysis, writing assistance, publishing, and others. While AI tools offer significant benefits, ethical considerations such as biases and content ownership must be addressed. Ongoing research and collaborative efforts are crucial to ensure responsible and transparent AI implementation in the dynamic landscape of medical research and publishing.

The role of artificial intelligence in pandemic responses: from epidemiological modeling to vaccine development

Integrating Artificial Intelligence (AI) across numerous disciplines has transformed the worldwide landscape of pandemic response. This review investigates the multidimensional role of AI in the pandemic, which arises as a global health crisis, and its role in preparedness and responses, ranging from enhanced epidemiological modelling to the acceleration of vaccine development. The confluence of AI technologies has guided us in a new era of data-driven decision-making, revolutionizing our ability to anticipate, mitigate, and treat infectious illnesses. The review begins by discussing the impact of a pandemic on emerging countries worldwide, elaborating on the critical significance of AI in epidemiological modelling, bringing data-driven decision-making, and enabling forecasting, mitigation and response to the pandemic. In epidemiology, AI-driven epidemiological models like SIR (Susceptible-Infectious-Recovered) and SIS (Susceptible-Infectious-Susceptible) are applied to predict the spread of disease, preventing outbreaks and optimising vaccine distribution. The review also demonstrates how Machine Learning (ML) algorithms and predictive analytics improve our knowledge of disease propagation patterns. The collaborative aspect of AI in vaccine discovery and clinical trials of various vaccines is emphasised, focusing on constructing AI-powered surveillance networks. Conclusively, the review presents a comprehensive assessment of how AI impacts epidemiological modelling, builds AI-enabled dynamic models by collaborating ML and Deep Learning (DL) techniques, and develops and implements vaccines and clinical trials. The review also focuses on screening, forecasting, contact tracing and monitoring the virus-causing pandemic. It advocates for sustained research, real-world implications, ethical application and strategic integration of AI technologies to strengthen our collective ability to face and alleviate the effects of global health issues.

Applying AI/ML for Analyzing Gene Expression Patterns

Artificial intelligence (AI) and machine learning (ML) have advanced in several areas and fields of life; however, its progress in the field of genomics is not matching the levels others have achieved. Challenges include but are not limited to the handling and analysis of high volumes of complex genomic data, and the expertise needed to implement and execute AI/ML approaches. In this chapter, we highlight the importance of transcriptomics, and RNA-seq driven gene expression data exploration to discover novel biomarkers and predict rare, common, and complex diseases. We discuss relevant high volume sequence data generated in the recent past and its availability through various channels, development of orthodox bioinformatics tools and technologies to investigate significantly expressed and abundantly enriched genes, and the implementation of cutting-edge AI/ML approaches to observe disease specific patterns. Current challenges include but are not limited to the acceptance of AI/ML in the scientific research and clinical environments, especially in providing personalized diagnoses and treatments. Reasons include unavailability of user-friendly AI/ML applications and reproducible results. Addressing these issues, we discuss our recently developed Findable, Accessible, Intelligent, and Reproducible (FAIR) solutions, designed for the users with and without computational background to discover biomarkers and predict diseases with high accuracy. We strongly believe that the rightful application of AI/ML techniques has the potential to open avenues for broader research, ultimately leading to personalized interventions and novel treatment targets. Its widespread application will contribute to the public health at large in the United States and around the globe.

Application of Artificial Intelligence in Prediction of Ki-67 Index in Meningiomas: A Systematic Review and Meta-Analysis

BACKGROUND

The Ki-67 index is a histopathological marker that has been reported to be a crucial factor in the biological behavior and prognosis of meningiomas. Several studies have developed artificial intelligence (AI) models to predict the Ki-67 based on radiomics. In this study, we aimed to perform a systematic review and meta-analysis of AI models that predicted the Ki-67 index in meningioma.

METHODS

Literature records were retrieved on April 27, 2024, using the relevant key terms without filters in PubMed, Embase, Scopus, and Web of Science. Records were screened according to the eligibility criteria, and the data from included studies were extracted. The quality assessment was performed using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. The meta-analysis, sensitivity analysis, and meta-regression were conducted using R software.

RESULTS

Our study included 6 studies. The mean Ki-67 ranged from 2.7 ± 2.97 to 4.8 ± 40.3. Of 6 studies, 5 utilized a machine learning method. The most used AI method was the least absolute shrinkage and selection operator. The area under the curve and accuracy ranged from 0.83 to 0.99 and 0.81 to 0.95, respectively. AI models demonstrated a pooled sensitivity of 87.5% (95% confidence interval [CI]: 75.2%, 94.2%), a specificity of 86.9% (95% CI: 75.8%, 93.4%), and a diagnostic odds ratio of 40.02 (95% CI: 13.5, 156.4). The summary receiver operating characteristic curve indicated an area under the curve of 0.931 for the prediction of Ki-67 index status in intracranial meningiomas.

CONCLUSIONS

AI models have demonstrated promising performance for predicting the Ki-67 index in meningiomas and can optimize the treatment strategy.

The Role of Machine Learning Approaches in Pediatric Oncology: A Systematic Review

To enhance patient outcomes in pediatric cancer, a better understanding of the medical and biological risk variables is required. With the growing amount of data accessible to research in pediatric cancer, machine learning (ML) is a form of algorithmic inference from sophisticated statistical techniques. In addition to highlighting developments and prospects in the field, the objective of this systematic study was to methodically describe the state of ML in pediatric oncology. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to search for relevant studies on four distinct databases (Scopus, Web of Science, PubMed, and Cochrane Library). A total of 1536 relevant studies were retrieved to the EndNote library (Clarivate, Philadelphia, USA) where duplicates were removed and the rest of the studies were assessed for eligibility based on titles, abstracts, and the availability of full-text articles. After assessing the studies for eligibility, we found 42 studies eligible for inclusion in this systematic review. We found nine studies on liquid tumors, 13 on solid tumors, and 20 on central nervous system (CNS) tumors. ML goals included classification, treatment response prediction, and dose optimization. Neural networks, k-nearest neighbors, random forests, support vector machines, and naive Bayes were among the techniques employed. The identified studies' strengths included treatment response prediction and automated analysis that matched or outperformed physician comparators. Significant variation in clinical applicability, criteria for reporting, limited sample numbers, and the absence of external validation cohorts were among the common issues. We found places where ML can improve clinical care in manners that would not be possible otherwise. Even though ML has great promise for enhancing pediatric cancer diagnosis, decision-making, and monitoring, the discipline is still in its infancy, and standards and recommendations will support future research to guarantee robust methodologic design and maximize therapeutic applicability.

Advancements in Machine Learning and Artificial Intelligence in the Radiological Detection of Pulmonary Embolism

Pulmonary embolism (PE) is a clinically challenging diagnosis that varies from silent to life-threatening symptoms. Timely diagnosis of the condition is subject to clinical assessment, D-dimer testing and radiological imaging. Computed tomography pulmonary angiogram (CTPA) is considered the gold standard imaging modality, although some cases can be missed due to reader dependency, resulting in adverse patient outcomes. Hence, it is crucial to implement faster and precise diagnostic strategies to help clinicians diagnose and treat PE patients promptly and mitigate morbidity and mortality. Machine learning (ML) and artificial intelligence (AI) are the newly emerging tools in the medical field, including in radiological imaging, potentially improving diagnostic efficacy. Our review of the studies showed that computer-aided design (CAD) and AI tools displayed similar to superior sensitivity and specificity in identifying PE on CTPA as compared to radiologists. Several tools demonstrated the potential in identifying minor PE on radiological scans showing promising ability to aid clinicians in reducing missed cases substantially. However, it is imperative to design sophisticated tools and conduct large clinical trials to integrate AI use in everyday clinical setting and establish guidelines for its ethical applicability. ML and AI can also potentially help physicians in formulating individualized management strategies to enhance patient outcomes.

Artificial intelligence for personalized nanomedicine; from material selection to patient outcomes

INTRODUCTION

Artificial intelligence (AI) is changing the field of nanomedicine by exploring novel nanomaterials for developing therapies of high efficacy. AI works on larger datasets, finding sought-after nano-properties for different therapeutic aims and eventually enhancing nanomaterials' safety and effectiveness. AI leverages patient clinical and genetic data to predict outcomes, guide treatments, and optimize drug dosages and forms, enhancing benefits while minimizing side effects. AI-supported nanomedicine faces challenges like data fusion, ethics, and regulation, requiring better tools and interdisciplinary collaboration. This review highlights the importance of AI regarding patient care and urges scientists, medical professionals, and regulators to adopt AI for better outcomes.

AREAS COVERED

Personalized Nanomedicine, Material Discovery, AI-Driven Therapeutics, Data Integration, Drug Delivery, Patient Centric Care.

EXPERT OPINION

Today, AI can improve personalized health wellness through the discovery of new types of drug nanocarriers, nanomedicine of specific properties to tackle targeted medical needs, and an increment in efficacy along with safety. Nevertheless, problems such as ethical issues, data security, or unbalanced data sets need to be addressed. Potential future developments involve using AI and quantum computing together and exploring telemedicine i.e. the Internet-of-Medical-Things (IoMT) approach can enhance the quality of patient care in a personalized manner by timely decision-making.

VAREANT: a bioinformatics application for gene variant reduction and annotation

Motivation

The analysis of high-quality genomic variant data may offer a more complete understanding of the human genome, enabling researchers to identify novel biomarkers, stratify patients based on disease risk factors, and decipher underlying biological pathways. Although the availability of genomic data has sharply increased in recent years, the accessibility of bioinformatic tools to aid in its preparation is still lacking. Limitations with processing genomic data primarily include its large volume, associated computational and storage costs, and difficulty in identifying targeted and relevant information.

Results

We present VAREANT, an accessible and configurable bioinformatic application to support the preparation of variant data into a usable analysis-ready format. VAREANT is comprised of three standalone modules: (i) Pre-processing, (ii) Variant Annotation, (iii) AI/ML Data Preparation. Pre-processing supports the fine-grained filtering of complex variant datasets to eliminate extraneous data. Variant Annotation allows for the addition of variant metadata from the latest public annotation databases for subsequent analysis and interpretation. AI/ML Data Preparation supports the user in creating AI/ML-ready datasets suitable for immediate analysis with minimal pre-processing required. We have successfully tested and validated our tool on numerous variable-sized datasets and implemented VAREANT in two case studies involving patients with cardiovascular diseases.

Availability and implementation

The open-source code of VAREANT is available at GitHub: https://github.com/drzeeshanahmed/Gene_VAREANT.

Circadian rhythms and cancer: implications for timing in therapy

Circadian rhythms, intrinsic cycles spanning approximately 24 h, regulate numerous physiological processes, including sleep-wake cycles, hormone release, and metabolism. These rhythms are orchestrated by the circadian clock, primarily located in the suprachiasmatic nucleus (SCN) of the hypothalamus. Disruptions in circadian rhythms, whether due to genetic mutations, environmental factors, or lifestyle choices, can significantly impact health, contributing to disorders such as sleep disturbances, metabolic syndrome, and cardiovascular diseases. Additionally, there is a profound link between the disruption of circadian rhythms and development of various cancer, the influence on disease incidence and progression. This incurred regulation by circadian clock on pathways has its implication in tumorigenesis, such as cell cycle control, DNA damage response, apoptosis, and metabolism. Furthermore, the circadian timing system modulates the efficacy and toxicity of cancer treatments. In cancer treatment, the use of chronotherapy to optimize the timing of medical treatments, involves administering chemotherapy, radiation, or other therapeutic interventions at specific intervals to enhance efficacy and minimize side effects. This approach capitalizes on the circadian variations in cellular processes, including DNA repair, cell cycle progression, and drug metabolism. Preclinical and clinical studies have demonstrated that chronotherapy can significantly improve the therapeutic index of chemotherapeutic agents like cisplatin and 5-fluorouracil by enhancing anticancer activity and reducing toxicity. Further research is needed to elucidate the mechanisms underlying circadian regulation of cancer and to develop robust chronotherapeutic protocols tailored to individual patients' circadian profiles, potentially transforming cancer care into more effective and personalized treatment strategies.

Integration of RNA Editing with Multiomics Data Improves Machine Learning Models for Predicting Drug Responses in Breast Cancer Patients

Background

The integration of conventional omics data such as genomics and transcriptomics data into artificial intelligence models has advanced significantly in recent years; however, their low applicability in clinical contexts, due to the high complexity of models, has been limited in their direct use inpatients. We integrated classic omics, including DNA mutation and RNA gene expression, added a novel focus on promising omics methods based on A>I(G) RNA editing, and developed a drug response prediction model.

Methods

We analyzed 104 patients from the Breast Cancer Genome-Guided Therapy Study (NCT02022202). This study was used to train (70%) with 10-fold cross-validation and test (30%) the drug response classification models. We assess the performance of the random forest (RF), generalized linear model (GLM), and support vector machine (SVM) with the Caret package in classifying therapy response via various combinations of clinical data, tumoral and germline mutation data, gene expression data, and RNA editing data via the LASSO and PCA strategies.

Results

First, we characterized the cohort on the basis of clinical data, mutation landscapes, differential gene expression, and RNAediting sites in 69 nonresponders and 35 responders to therapy. Second, regarding the prediction models, we demonstrated that RNA editing data improved or maintained the performance of the RF model for predicting drug response across all combinations. To select the final model, we compared the F1 score between models with different data combinations, highlighting an F1 score of 0.96 (95% CI: 0.957--0.961) and an AUC of 0.922, using LASSO for feature selection. Finally, we developed a nonresponse risk score on the basis of features that contributed to the selected model, focusing on three RNA-edited sites in the genes KDM4B, miRNA200/TTLL10-AS1, and BEST1. The score was created to facilitate the clinical translation of our findings, presenting a probability of therapy response according to RNA editing site patterns.

Conclusion

Our study highlights the potential of RNA editing as a valuable addition to predictive modeling for drug response in patients with breast cancer. The nonresponse risk score could represent a tool for clinical translation, offering a probability-based assessment of therapy response. These findings suggest that incorporating RNA editing into predictive models could enhance personalized treatment strategies and improve decision-making in oncology.

Assuring assistance to healthcare and medicine: Internet of Things, Artificial Intelligence, and Artificial Intelligence of Things

Introduction

The convergence of healthcare with the Internet of Things (IoT) and Artificial Intelligence (AI) is reshaping medical practice with promising enhanced data-driven insights, automated decision-making, and remote patient monitoring. It has the transformative potential of these technologies to revolutionize diagnosis, treatment, and patient care.

Purpose

This study aims to explore the integration of IoT and AI in healthcare, outlining their applications, benefits, challenges, and potential risks. By synthesizing existing literature, this study aims to provide insights into the current landscape of AI, IoT, and AIoT in healthcare, identify areas for future research and development, and establish a framework for the effective use of AI in health.

Method

A comprehensive literature review included indexed databases such as PubMed/Medline, Scopus, and Google Scholar. Key search terms related to IoT, AI, healthcare, and medicine were employed to identify relevant studies. Papers were screened based on their relevance to the specified themes, and eventually, a selected number of papers were methodically chosen for this review.

Results

The integration of IoT and AI in healthcare offers significant advancements, including remote patient monitoring, personalized medicine, and operational efficiency. Wearable sensors, cloud-based data storage, and AI-driven algorithms enable real-time data collection, disease diagnosis, and treatment planning. However, challenges such as data privacy, algorithmic bias, and regulatory compliance must be addressed to ensure responsible deployment of these technologies.

Conclusion

Integrating IoT and AI in healthcare holds immense promise for improving patient outcomes and optimizing healthcare delivery. Despite challenges such as data privacy concerns and algorithmic biases, the transformative potential of these technologies cannot be overstated. Clear governance frameworks, transparent AI decision-making processes, and ethical considerations are essential to mitigate risks and harness the full benefits of IoT and AI in healthcare.

Multisource Data Framework for Prehospital Emergency Triage in Real-Time IoMT-Based Telemedicine Systems

BACKGROUND AND OBJECTIVE

The Internet of Medical Things (IoMT) has revolutionized telemedicine by enabling the remote monitoring and management of patient care. Nevertheless, the process of regeneration presents the difficulty of effectively prioritizing the information of emergency patients in light of the extensive amount of data generated by several integrated health care devices. The main goal of this study is to be improving the procedure of prioritizing emergency patients by implementing the Real-time Triage Optimization Framework (RTOF), an innovative method that utilizes diverse data from the Internet of Medical Things (IoMT).

METHODS

The study's methodology utilized a variety of Internet of Medical Things (IoMT) data, such as sensor data and texts derived from electronic medical records. Tier 1 supplies sensor and textual data, and Tier 3 imports textual data from electronic medical records. We employed our methodologies to handle and examine data from a sample of 100,000 patients afflicted with hypertension and heart disease, employing artificial intelligence algorithms. We utilized five machine-learning algorithms to enhance the accuracy of triage.

RESULTS

The RTOF approach has remarkable efficacy in a simulated telemedicine environment, with a triage accuracy rate of 98%. The Random Forest algorithm exhibited superior performance compared to the other approaches under scrutiny. The performance characteristics attained were an accuracy rate of 98%, a precision rate of 99%, a sensitivity rate of 98%, and a specificity rate of 100%. The findings show a significant improvement compared to the present triage methods.

CONCLUSIONS

The efficiency of RTOF surpasses that of existing triage frameworks, showcasing its significant ability to enhance the quality and efficacy of telemedicine solutions. This work showcases substantial enhancements compared to existing triage approaches, while also providing a scalable approach to tackle hospital congestion and optimize resource allocation in real-time. The results of our study emphasize the capacity of RTOF to mitigate hospital overcrowding, expedite medical intervention, and enable the creation of adaptable telemedicine networks. This study highlights potential avenues for further investigation into the integration of the Internet of Medical Things (IoMT) with machine learning to develop cutting-edge medical technologies.

Artificial Intelligence for Prediction of Shunt Response in Idiopathic Normal Pressure Hydrocephalus: A Systematic Review

BACKGROUND

Idiopathic normal pressure hydrocephalus (iNPH) is a reversible cause of dementia, typically treated with shunt surgery, although outcomes vary. Artificial intelligence (AI) advancements could improve predictions of shunt response (SR) by analyzing extensive datasets.

METHODS

We conducted a systematic review to assess AI's effectiveness in predicting SR in iNPH. Studies using AI or machine learning algorithms for SR prediction were identified through searches in MEDLINE, Embase, and Web of Science up to September 2023, adhering to Synthesis Without Meta-Analysis reporting guidelines.

RESULTS

Of 3541 studies identified, 33 were assessed for eligibility, and 8 involving 479 patients were included. Study sample sizes varied from 28 to 132 patients. Common data inputs included imaging/radiomics (62.5%) and demographics (37.5%), with Support Vector Machine being the most frequently used machine learning algorithm (87.5%). Two studies compared multiple algorithms. Only 4 studies reported the Area Under the Curve values, which ranged between 0.80 and 0.94. The results highlighted inconsistency in outcome measures, data heterogeneity, and potential biases in the models used.

CONCLUSIONS

While AI shows promise for improving iNPH management, there is a need for standardized data and extensive validation of AI models to enhance their clinical utility. Future research should aim to develop robust and generalizable AI models for more effective diagnosis and management of iNPH.