Search Results (61)

Precision neurosurgery is rapidly evolving as a medical specialty by merging genomic medicine, multi-omics technologies, and artificial intelligence (AI) technology, while at the same time, society is shifting away from the traditional, anatomic model of care to consider a more precise, molecular model of care. The general purpose of this review is to contemporaneously reflect on how these advances will impact neurosurgical care by providing us with more precise diagnostic and treatment pathways. We hope to provide a relevant review of the recent advances in genomics and multi-omics in the context of clinical practice and highlight their transformational opportunities in the existing models of care, where improved molecular insights can support improvements in clinical care. More specifically, we will highlight how genomic profiling, CRISPR-Cas9, and multi-omics platforms (genomics, transcriptomics, proteomics, and metabolomics) are increasing our understanding of central nervous system (CNS) disorders. Achievements obtained with transformational technologies such as single-cell RNA sequencing and intraoperative mass spectrometry are exemplary of the molecular diagnostic possibilities in real-time molecular diagnostics to enable a more directed approach in surgical options. We will also explore how identifying specific biomarkers (e.g., IDH mutations and MGMT promoter methylation) became a tipping point in the care of glioblastoma and allowed for the establishment of a new taxonomy of tumors that became applicable for surgeons, where a change in practice enjoined a different surgical resection approach and subsequently stratified the adjuvant therapies undertaken after surgery. Furthermore, we reflect on how the novel genomic characterization of mutations like DEPDC5 and SCN1A transformed the pre-surgery selection of surgical candidates for refractory epilepsy when conventional imaging did not define an epileptogenic zone, thus reducing resective surgery occurring in clinical practice. While we are atop the crest of an exciting wave of advances, we recognize that we also must be diligent about the challenges we must navigate to implement genomic medicine in neurosurgery—including ethical and technical challenges that could arise when genomic mutation-based therapies require the concurrent application of multi-omics data collection to be realized in practice for the benefit of patients, as well as the constraints from the blood–brain barrier. The primary challenges also relate to the possible gene privacy implications around genomic medicine and equitable access to technology-based alternative practice disrupting interventions. We hope the contribution from this review will not just be situational consolidation and integration of knowledge but also a stimulus for new lines of research and clinical practice. We also hope to stimulate mindful discussions about future possibilities for conscientious and sustainable progress in our evolution toward a genomic model of precision neurosurgery. In the spirit of providing a critical perspective, we hope that we are also adding to the larger opportunity to embed molecular precision into neuroscience care, striving to promote better practice and better outcomes for patients in a global sense. Full article

The integration of artificial intelligence (AI) into next-generation sequencing (NGS) has revolutionized genomics, offering unprecedented advancements in data analysis, accuracy, and scalability. This review explores the synergistic relationship between AI and NGS, highlighting its transformative impact across genomic research and clinical applications. AI-driven tools, including machine learning and deep learning, enhance every aspect of NGS workflows—from experimental design and wet-lab automation to bioinformatics analysis of the generated raw data. Key applications of AI integration in NGS include variant calling, epigenomic profiling, transcriptomics, and single-cell sequencing, where AI models such as CNNs, RNNs, and hybrid architectures outperform traditional methods. In cancer research, AI enables precise tumor subtyping, biomarker discovery, and personalized therapy prediction, while in drug discovery, it accelerates target identification and repurposing. Despite these advancements, challenges persist, including data heterogeneity, model interpretability, and ethical concerns. This review also discusses the emerging role of AI in third-generation sequencing (TGS), addressing long-read-specific challenges, like fast and accurate basecalling, as well as epigenetic modification detection. Future directions should focus on implementing federated learning to address data privacy, advancing interpretable AI to improve clinical trust and developing unified frameworks for seamless integration of multi-modal omics data. By fostering interdisciplinary collaboration, AI promises to unlock new frontiers in precision medicine, making genomic insights more actionable and scalable. Full article

The advancement of computational genomics has significantly enhanced the use of data-driven solutions in disease prediction and precision medicine. Yet, challenges such as data scarcity, privacy constraints, and biases persist. Synthetic data generation offers a promising solution to these issues. However, existing approaches based on generative artificial intelligence often fail to incorporate biological knowledge, limiting the realism and utility of generated samples. In this work, we present BioGAN, a novel generative framework that, for the first time, incorporates graph neural networks into a generative adversarial network architecture for transcriptomic data generation. By leveraging gene regulatory and co-expression networks, our model preserves biological properties in the generated transcriptomic profiles. We validate its effectiveness on E. coli and human gene expression datasets through extensive experiments using unsupervised and supervised evaluation metrics. The results demonstrate that incorporating a priori biological knowledge is an effective strategy for enhancing both the quality and utility of synthetic transcriptomic data. On human data, BioGAN achieves a 4.3% improvement in precision and an up to 2.6% higher correlation with real profiles compared to state-of-the-art models. In downstream disease and tissue classification tasks, our synthetic data improves prediction performance by an average of 5.7%. Results on E. coli further confirm BioGAN’s robustness, showing consistently strong recall and predictive utility. Full article

Gene expression regulation underpins cellular function and disease progression, yet its complexity and the limitations of conventional detection methods hinder clinical translation. In this review, we define “predict” as the AI-driven inference of gene expression levels and regulatory mechanisms from non-invasive multimodal data (e.g., histopathology images, genomic sequences, and electronic health records) instead of direct molecular assays. We systematically examine and analyze the current approaches for predicting gene expression and diagnosing diseases, highlighting their respective advantages and limitations. Machine learning algorithms and deep learning models excel in extracting meaningful features from diverse biomedical modalities, enabling tools like PathChat and Prov-GigaPath to improve cancer subtyping, therapy response prediction, and biomarker discovery. Despite significant progress, persistent challenges—such as data heterogeneity, noise, and ethical issues including privacy and algorithmic bias—still limit broad clinical adoption. Emerging solutions like cross-modal pretraining frameworks, federated learning, and fairness-aware model design aim to overcome these barriers. Case studies in precision oncology illustrate AI’s ability to decode tumor ecosystems and predict treatment outcomes. By harmonizing multimodal data and advancing ethical AI practices, this field holds immense potential to propel personalized medicine forward, although further innovation is needed to address the issues of scalability, interpretability, and equitable deployment. Full article

Harnessing state-of-the-art technologies to improve disease resistance is a critical objective in modern plant breeding. Artificial intelligence (AI), particularly deep learning and big model (large language model and large multi-modal model), has emerged as a transformative tool to enhance disease detection and omics prediction in plant science. This paper provides a comprehensive review of AI-driven advancements in plant disease detection, highlighting convolutional neural networks and their linked methods and technologies through bibliometric analysis from recent research. We further discuss the groundbreaking potential of large language models and multi-modal models in interpreting complex disease patterns via heterogeneous data. Additionally, we summarize how AI accelerates genomic and phenomic selection by enabling high-throughput analysis of resistance-associated traits, and explore AI’s role in harmonizing multi-omics data to predict plant disease-resistant phenotypes. Finally, we propose some challenges and future directions in terms of data, model, and privacy facets. We also provide our perspectives on integrating federated learning with a large language model for plant disease detection and resistance prediction. This review provides a comprehensive guide for integrating AI into plant breeding programs, facilitating the translation of computational advances into disease-resistant crop breeding. Full article

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. Full article

Background: Advancements in pharmacogenomics, artificial intelligence (AI), and CRISPR gene-editing technology are revolutionizing precision medicine by enabling highly individualized therapeutic strategies. Artificial intelligence-driven computational techniques improve biomarker discovery and drug optimization while pharmacogenomics helps to identify genetic polymorphisms affecting medicine metabolism, efficacy, and toxicity. Genetically editing based on CRISPR presents a precise method for changing gene expression and repairing damaging mutations. This review explores the convergence of these three fields to enhance improved precision medicine. Method: A methodical study of the current literature was performed on the effects of pharmacogenomics on drug response variability, artificial intelligence, and CRISPR in predictive modeling and gene-editing applications. Results: Driven by artificial intelligence, pharmacogenomics allows clinicians to classify patients and select the appropriate medications depending on their DNA profiles. This reduces the side effect risk and increases the therapeutic efficacy. Precision genetic modifications made feasible by CRISPR technology improve therapy outcomes in oncology, metabolic illnesses, neurological diseases, and other fields. The integration of artificial intelligence streamlines genome-editing applications, lowers off-target effects, and increases CRISPR specificity. Notwithstanding these advances, issues including computational biases, moral dilemmas, and legal constraints still arise. Conclusions: The synergy of artificial intelligence, pharmacogenomics, and CRISPR alters precision medicine by letting customized therapeutic interventions. Clinically translating, however, hinges on resolving data privacy concerns, assuring equitable access, and strengthening legal systems. Future research should focus on refining CRISPR gene-editing technologies, enhancing AI-driven pharmacogenomics, and developing moral guidelines for applying these tools in individualized medicine going forward. Full article

Hereditary tumor predisposition syndromes (HTPSs) significantly increase the risk of developing various cancers, often at earlier ages than seen in the general population. The development and application of next-generation sequencing (NGS) has revolutionized the identification of individuals with HTPS, facilitating early diagnosis, personalized risk assessment, and tailored preventive strategies. However, the widespread implementation of genetic testing for HTPS presents complex ethical and psychosocial issues. This paper examines key ethical considerations surrounding genetic testing for HTPS, including the following: the distinct nature of genetic information and its implications for families; the challenges of informed consent amidst evolving genetic knowledge and direct-to-consumer testing; the complexities of predictive and presymptomatic testing, particularly in minors; and the implications of incidental findings. It further explores the critical issue of genetic discrimination, particularly concerning insurance, employment, and social stigmatization. This paper highlights the importance of balancing individual rights, such as autonomy and privacy, with familial responsibilities and the potential benefits of early detection and intervention. It also underscores the need for robust legal frameworks, comprehensive genetic counseling, and ongoing public education to address the ethical and psychosocial challenges associated with genetic testing for HTPS, with the ultimate goal of maximizing the benefits of genomic medicine while minimizing potential harms. Full article

The completion of the Human Genome Project in 2003 has led to significant advances in patient care in medicine, particularly in diagnosing and managing genetic diseases and cancer. In the realm of genetic diseases, approximately 15% of critically ill infants born in the U.S.A. are diagnosed with genetic disorders, which comprise a significant cause of mortality in neonatal and pediatric intensive care units. The introduction of rapid whole-genome sequencing (rWGS) as a first-tier test in critically ill children with suspected, undiagnosed genetic diseases is a breakthrough in the diagnosis and subsequent clinical management of such infants and older children in intensive care units. Rapid genome sequencing is currently being used clinically in the USA, the UK, the Netherlands, Sweden, and Australia, among other countries. This review is intended for students and clinical practitioners, including non-experts in genetics, for whom it provides a historical background and a chronological review of the relevant published literature for the progression of pediatric diagnostic genomic sequencing leading to the development of pediatric rWGS in critically ill infants and older children with suspected but undiagnosed genetic diseases. Factors that will help to develop rWGS as a clinical test in critically ill infants and the limitations are briefly discussed, including an evaluation of the clinical utility and accessibility of genetic testing, education for parents and providers, cost-effectiveness, ethical challenges, consent issues, secondary findings, data privacy concerns, false-positive and false-negative results, challenges in variant interpretation, costs and reimbursement, the limited availability of genetic counselors, and the development of evidence-based guidelines, which would all need to be addressed to facilitate the implementation of pediatric genomic sequencing in an effective widespread manner in the era of precision medicine. Full article

Artificial Intelligence (AI) is transforming pancreaticobiliary endoscopy by enhancing diagnostic accuracy, procedural efficiency, and clinical outcomes. This narrative review explores AI’s applications in endoscopic ultrasound (EUS) and endoscopic retrograde cholangiopancreatography (ERCP), emphasizing its potential to address diagnostic and therapeutic challenges in pancreaticobiliary diseases. In EUS, AI improves pancreatic mass differentiation, malignancy prediction, and landmark recognition, demonstrating high diagnostic accuracy and outperforming traditional guidelines. In ERCP, AI facilitates precise biliary stricture identification, optimizes procedural techniques, and supports decision-making through real-time data integration, improving ampulla recognition and predicting cannulation difficulty. Additionally, predictive analytics help mitigate complications like post-ERCP pancreatitis. The future of AI in pancreaticobiliary endoscopy lies in multimodal data fusion, integrating imaging, genomic, and molecular data to enable personalized medicine. However, challenges such as data quality, external validation, clinician training, and ethical concerns—like data privacy and algorithmic bias—must be addressed to ensure safe implementation. By overcoming these challenges, AI has the potential to redefine pancreaticobiliary healthcare, improving diagnostic accuracy, therapeutic outcomes, and personalized care. Full article

Background/Objectives: Artificial intelligence (AI) is transforming healthcare, enabling advances in diagnostics, treatment optimization, and patient care. Yet, its integration raises ethical, regulatory, and societal challenges. Key concerns include data privacy risks, algorithmic bias, and regulatory gaps that struggle to keep pace with AI advancements. This study aims to synthesize a multidisciplinary framework for trustworthy AI in healthcare, focusing on transparency, accountability, fairness, sustainability, and global collaboration. It moves beyond high-level ethical discussions to provide actionable strategies for implementing trustworthy AI in clinical contexts. Methods: A structured literature review was conducted using PubMed, Scopus, and Web of Science. Studies were selected based on relevance to AI ethics, governance, and policy in healthcare, prioritizing peer-reviewed articles, policy analyses, case studies, and ethical guidelines from authoritative sources published within the last decade. The conceptual approach integrates perspectives from clinicians, ethicists, policymakers, and technologists, offering a holistic “ecosystem” view of AI. No clinical trials or patient-level interventions were conducted. Results: The analysis identifies key gaps in current AI governance and introduces the Regulatory Genome—an adaptive AI oversight framework aligned with global policy trends and Sustainable Development Goals. It introduces quantifiable trustworthiness metrics, a comparative analysis of AI categories for clinical applications, and bias mitigation strategies. Additionally, it presents interdisciplinary policy recommendations for aligning AI deployment with ethical, regulatory, and environmental sustainability goals. This study emphasizes measurable standards, multi-stakeholder engagement strategies, and global partnerships to ensure that future AI innovations meet ethical and practical healthcare needs. Conclusions: Trustworthy AI in healthcare requires more than technical advancements—it demands robust ethical safeguards, proactive regulation, and continuous collaboration. By adopting the recommended roadmap, stakeholders can foster responsible innovation, improve patient outcomes, and maintain public trust in AI-driven healthcare. Full article

Genomic and epigenomic instability are defining features of cancer, driving tumor progression, heterogeneity, and therapeutic resistance. Central to this process are epigenetic echoes, persistent and dynamic modifications in DNA methylation, histone modifications, non-coding RNA regulation, and chromatin remodeling that mirror underlying genomic chaos and actively influence cancer cell behavior. This review delves into the complex relationship between genomic instability and these epigenetic echoes, illustrating how they collectively shape the cancer genome, affect DNA repair mechanisms, and contribute to tumor evolution. However, the dynamic, context-dependent nature of epigenetic changes presents scientific and ethical challenges, particularly concerning privacy and clinical applicability. Focusing on lung cancer, we examine how specific epigenetic patterns function as biomarkers for distinguishing cancer subtypes and monitoring disease progression and relapse. Full article

Hematopoietic stem cell transplantation (HSCT) is a life-saving therapy for hematologic malignancies, such as leukemia and lymphoma and other severe conditions but is associated with significant risks, including graft versus host disease (GVHD), relapse, and treatment-related mortality. The increasing complexity of clinical, genomic, and biomarker data has spurred interest in machine learning (ML), which has emerged as a transformative tool to enhance decision-making and optimize outcomes in HSCT. This review examines the applications of ML in HSCT, focusing on donor selection, conditioning regimen, and prediction of post-transplant outcomes. Machine learning approaches, including decision trees, random forests, and neural networks, have demonstrated potential in improving donor compatibility algorithms, mortality and relapse prediction, and GVHD risk stratification. Integrating “omics” data with ML models has enabled the identification of novel biomarkers and the development of highly accurate predictive tools, supporting personalized treatment strategies. Despite promising advancements, challenges persist, including data standardization, algorithm interpretability, and ethical considerations regarding patient privacy. While ML holds promise for revolutionizing HSCT management, addressing these barriers through multicenter collaborations and regulatory frameworks remains essential for broader clinical adoption. In addition, the potential of ML can cope with some challenges such as data harmonization, patients’ data protection, and availability of adequate infrastructure. Future research should prioritize larger datasets, multimodal data integration, and robust validation methods to fully realize ML’s transformative potential in HSCT. Full article

Background: Cancer remains a leading cause of morbidity and mortality worldwide. Traditional treatments like chemotherapy and radiation often result in significant side effects and varied patient outcomes. Immunotherapy has emerged as a promising alternative, harnessing the immune system to target cancer cells. However, the complexity of immune responses and tumor heterogeneity challenges its effectiveness. Objective: This mini-narrative review explores the role of artificial intelligence [AI] in enhancing the efficacy of cancer immunotherapy, predicting patient responses, and discovering novel therapeutic targets. Methods: A comprehensive review of the literature was conducted, focusing on studies published between 2010 and 2024 that examined the application of AI in cancer immunotherapy. Databases such as PubMed, Google Scholar, and Web of Science were utilized, and articles were selected based on relevance to the topic. Results: AI has significantly contributed to identifying biomarkers that predict immunotherapy efficacy by analyzing genomic, transcriptomic, and proteomic data. It also optimizes combination therapies by predicting the most effective treatment protocols. AI-driven predictive models help assess patient response to immunotherapy, guiding clinical decision-making and minimizing side effects. Additionally, AI facilitates the discovery of novel therapeutic targets, such as neoantigens, enabling the development of personalized immunotherapies. Conclusions: AI holds immense potential in transforming cancer immunotherapy. However, challenges related to data privacy, algorithm transparency, and clinical integration must be addressed. Overcoming these hurdles will likely make AI a central component of future cancer immunotherapy, offering more personalized and effective treatments. Full article

With the development of genome sequencing technologies, the amount of data produced has greatly increased in the last two decades. The abundance of digital sequence information (DSI) has provided research opportunities, improved our understanding of the genome, and led to the discovery of new solutions in industry and medicine. It has also posed certain challenges, i.e., how to store and handle such amounts of data. This, coupled with the need for convenience, international cooperation, and the possibility of independent validation, has led to the establishment of numerous databases. Spearheaded with the idea that data obtained with public funds should be available to the public, open access has become the predominant mode of accession. However, the increasing popularity of commercial genetic tests brings back the topic of data misuse, and patient’s privacy. At the previous United Nations Biodiversity Conference (COP15, 2022), an issue of the least-developed countries exploiting their natural resources while providing DSI and the most-developed countries benefitting from this was raised. It has been proposed that financial renumeration for the data could help protect biodiversity. With the goal of introducing the topic to those interested in utilizing biological databases, in this publication, we present the history behind the biological databases, their necessity in today’s scientific world, and the issues that concern them and their content, while providing scientific and policy context in relation to United Nations Biodiversity Conference (COP16, 21.10—1.11.24). Full article