Exploring the current and prospective role of artificial intelligence in disease diagnosis

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.

Fetal and obstetrics manifestations of mitochondrial diseases

During embryonic and neonatal development, mitochondria have essential effects on metabolic and energetic regulation, shaping cell fate decisions and leading to significant short- and long-term effects on embryonic and offspring health. Therefore, perturbation on mitochondrial function can have a pathological effect on pregnancy. Several shreds of evidence collected in preclinical models revealed that severe mitochondrial dysfunction is incompatible with life or leads to critical developmental defects, highlighting the importance of correct mitochondrial function during embryo-fetal development. The mechanism impairing the correct development is unknown and may include a dysfunctional metabolic switch in differentiating cells due to decreased ATP production or altered apoptotic signalling. Given the central role of mitochondria in embryonic and fetal development, the mitochondrial dysfunction typical of Mitochondrial Diseases (MDs) should, in principle, be detectable during pregnancy. However, little is known about the clinical manifestations of MDs in embryonic and fetal development. In this manuscript, we review preclinical and clinical evidence suggesting that MDs may affect fetal development and highlight the fetal and maternal outcomes that may provide a wake-up call for targeted genetic diagnosis.

Predicting Post-surgery Discharge Time in Pediatric Patients Using Machine Learning

Background

Prolonged hospital stays after pediatric surgeries, such as tonsillectomy and adenoidectomy, pose significant concerns regarding cost and patient care. Dissecting the determinants of extended hospitalization is crucial for optimizing postoperative care and resource allocation.

Objective

This study aims to utilize machine learning (ML) techniques to predict post-surgery discharge times in pediatric patients and identify key variables influencing hospital stays.

Methods

The study analyzed data from 423 children who underwent tonsillectomy and/or adenoidectomy at the IRCCS Istituto Giannina Gaslini, Genoa, Italy. Variables included demographic factors, anesthesia-related details, and postoperative events. Preprocessing involved handling missing values, detecting outliers, and converting categorical variables to numerical classes. Univariate statistical analyses identified features correlated with discharge time. Four ML algorithms-Random Forest (RF), Logistic Regression, RUSBoost, and AdaBoost-were trained and evaluated using stratified 10-fold cross-validation.

Results

Significant predictors of delayed discharge included postoperative nausea and vomiting (PONV), continuous infusion of dexmedetomidine, fentanyl use, pain during discharge, and extubation time. The best-performing model, AdaBoost, demonstrated high accuracy and reliable prediction capabilities, with strong performance metrics across all evaluation criteria.

Conclusion

ML models can effectively predict discharge times and highlight critical factors impacting prolonged hospitalization. These insights can enhance postoperative care strategies and resource management in pediatric surgical settings. Future research should explore integrating these predictive models into clinical practice for real-time decision support.

A Comprehensive Review of Recent Advances in Minimally Invasive Glaucoma Surgery: Current Trends and Future Directions

Glaucoma, a leading cause of blindness globally, necessitates effective management strategies to prevent irreversible vision loss. Traditional glaucoma surgeries, while effective, are associated with significant risks and complications. Minimally invasive glaucoma surgery (MIGS) has emerged as a transformative approach, offering safer and less invasive alternatives. This review provides a comprehensive overview of recent advancements in MIGS, highlighting current trends, technological innovations, and future directions. MIGS procedures, characterized by smaller incisions and quicker recovery times, have expanded the therapeutic landscape, enabling earlier intervention and improved patient outcomes. The review evaluates various MIGS techniques, their efficacy, safety profiles, and clinical outcomes, drawing insights from comparative studies and meta-analyses. Technological innovations, including enhanced device designs and integration with digital health technologies, have further bolstered the field. Despite challenges in patient selection and long-term outcomes, the future of MIGS is promising, with ongoing research and development poised to enhance its impact. By synthesizing the latest research, this review aims to inform clinicians, researchers, and policymakers, ultimately contributing to improved management of glaucoma and patient care.

Exploring the Impact of Artificial Intelligence on Global Health and Enhancing Healthcare in Developing Nations

BACKGROUND

Artificial intelligence (AI), which combines computer science with extensive datasets, seeks to mimic human-like intelligence. Subsets of AI are being applied in almost all fields of medicine and surgery.

AIM

This review focuses on the applications of AI in healthcare settings in developing countries, designed to underscore its significance by comprehensively outlining the advancements made thus far, the shortcomings encountered in AI applications, the present status of AI integration, persistent challenges, and innovative strategies to surmount them.

METHODOLOGY

Articles from PubMed, Google Scholar, and Cochrane were searched from 2000 to 2023 with keywords including AI and healthcare, focusing on multiple medical specialties.

RESULTS

The increasing role of AI in diagnosis, prognosis prediction, and patient management, as well as hospital management and community healthcare, has made the overall healthcare system more efficient, especially in the high patient load setups and resource-limited areas of developing countries where patient care is often compromised. However, challenges, including low adoption rates and the absence of standardized guidelines, high installation and maintenance costs of equipment, poor transportation and connectivvity issues hinder AI's full use in healthcare.

CONCLUSION

Despite these challenges, AI holds a promising future in healthcare. Adequate knowledge and expertise of healthcare professionals for the use of AI technology in healthcare is imperative in developing nations.