SEARCH · PubMed Health

Results for “generative artificial intelligence”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 145 records · Page 8Linked to original sources

PubMed2026

Metal-Organic Framework-Based and Metal-Organic Framework-Derived Nanomaterials for Cancer Theranostics and Antibacterial Applications: Advances, Challenges, and Perspectives.

Metal-organic frameworks (MOFs), constructed through coordination-driven self-assembly of metal ions/clusters and organic linkers, have emerged as a uniquely versatile class of porous nanomaterials with broad biomedical potential. Despite substantial clinical progress, both oncological treatment and antimicrobial intervention remain constrained by inadequate tumor-targeting selectivity, multidrug resistance, immunosuppressive tumor microenvironments, and the global proliferation of antibiotic-resistant pathogens, limitations that conventional nanocarrier platforms have addressed only in part. MOF-based and MOF-derived nanomaterials, distinguished by tunable pore architecture, structurally and compositionally adaptable metal nodes, high surface areas, and stimulus-responsive degradability, offer a rational framework for overcoming these barriers. This review systematically examines the synthetic strategies underlying MOF-based and MOF-derived nanomaterials, including pyrolysis, chemical etching, composite modification, and functional group introduction, and their structural determinants of performance. In cancer theranostics, we critically evaluate their roles as multimodal imaging contrast agents, stimulus-responsive drug delivery carriers, and platforms for combination therapies encompassing photodynamic, photothermal, chemodynamic, and immunomodulatory modalities. In antibacterial applications, we analyze the mechanistic basis of MOF-based and MOF-derived activity, including physical membrane disruption, reactive oxygen species-mediated oxidative stress, and sustained metal ion release, alongside strategies targeting biofilm formation and antibiotic resistance. Multifunctional platforms that concurrently integrate cancer theranostic and antibacterial capabilities are further discussed. This review also addresses the principal barriers to clinical translation, encompassing large-scale manufacturing, long-term biosafety, and regulatory approval, and proposes future directions incorporating artificial intelligence-assisted design and materials genomics, underscoring the transformative potential of MOF-based and MOF-derived nanomaterials as next-generation precision nanomedicines. This review establishes a unified mechanistic framework grounded in the intrinsic physicochemical properties of MOF-derived nanomaterials, systematically integrating their applications in cancer theranostics and antibacterial therapy. Critically, it bridges fundamental advances with translational reality by incorporating a rigorous assessment of regulatory pathways, scalable manufacturing constraints, and clinical implementation barriers, and offers a comprehensive, practice-oriented reference for the rational design and responsible translation of MOF-based and MOF-derived nanomaterials.

Theranostic Nanomedicine↗

PubMed1991

[Medical diagnosis].

The diagnostic process holds a firm position in medical practice, but is often claimed to be part of the "art of medicine", partially beyond reach of rational and logical analysis. Research in clinical cognition, decision analysis and artificial intelligence have, however, elucidated essential parts of medical diagnosis. A characteristic feature of diagnosis is the manner in which uncertainties are handled. Early generation of hypotheses about the nature of the condition present seems to be useful method. Similarly, probabilistic, causal and deterministic reasoning can be illustrated by diagnostic models which have found favor during recent years. A certain type of cognitive process (heuristic) is employed when assessing information of probabilistic nature. The diagnostic models are partial and concern the parts of the process, which may be represented verbally and consciously. This raises the question of how the clinician actually draws upon experience (background knowledge), which preconditions shape the generation of applicable diagnostic hypotheses and how the diagnostic capability of the individual physician can be facilitated.

Clinical Competence↗

PubMed1989

Three-dimensional radiation treatment planning.

A major aim of radiation therapy is to deliver sufficient dose to the tumour volume to kill the cancer cells while sparing the nearby healthy organs to prevent complications. With the introduction of devices such as CT and MR scanners, radiation therapy treatment planners have access to full three-dimensional anatomical information to define, simulate, and evaluate treatments. There are a limited number of prototype software systems that allow 3D treatment planning currently in use. In addition, there are more advanced tools under development or still in the planning stages. They require sophisticated graphics and computation equipment, complex physical and mathematical algorithms, and new radiation treatment machines that deliver dose very precisely under computer control. Components of these systems include programs for the identification and delineation of the anatomy and tumour, the definition of radiation beams, the calculation of dose distribution patterns, the display of dose on 2D images and as three dimensional surfaces, and the generation of computer images to verify proper patient positioning in treatment. Some of these functions can be performed more quickly and accurately if artificial intelligence or expert systems techniques are employed.

Radiotherapy Dosage↗

PubMed2002

Disruptive visions.

Numerous advanced technologies, both medical and nonmedical, are emerging faster than their social, behavioral, political, moral, and ethical implications can be understood. Some of these technologies will fundamentally challenge the practice of surgery: human cloning, genetic engineering, tissue engineering, intelligent robotics, nanotechnology, suspended animation, regeneration, and species prolongation. Because of the rapidity of change, the current status of these emerging technologies with their specific moral and ethical issues must be addressed at this time by the new generation of surgeons, or we must all face the consequences of an uncontrolled and unprepared future.

Animals↗

PubMed2005

Synthetic ground truth for validation of brain tumor MRI segmentation.

Validation and method of comparison for segmentation of magnetic resonance images (MRI) presenting pathology is a challenging task due to the lack of reliable ground truth. We propose a new method for generating synthetic multi-modal 3D brain MRI with tumor and edema, along with the ground truth. Tumor mass effect is modeled using a biomechanical model, while tumor and edema infiltration is modeled as a reaction-diffusion process that is guided by a modified diffusion tensor MRI. We propose the use of warping and geodesic interpolation on the diffusion tensors to simulate the displacement and the destruction of the white matter fibers. We also model the process where the contrast agent tends to accumulate in cortical csf regions and active tumor regions to obtain contrast enhanced T1w MR image that appear realistic. The result is simulated multi-modal MRI with ground truth available as sets of probability maps. The system will be able to generate large sets of simulation images with tumors of varying size, shape and location, and will additionally generate infiltrated and deformed healthy tissue probabilities.

Algorithms↗

PubMed2000

A comparison between two neural network rule extraction techniques for the diagnosis of hepatobiliary disorders.

Neural networks have been widely used as tools for prediction in medicine. We expect to see even more applications of neural networks for medical diagnosis as recently developed neural network rule extraction algorithms make it possible for the decision process of a trained network to be expressed as classification rules. These rules are more comprehensible to a human user than the classification process of the networks which involves complex nonlinear mapping of the input data. This paper reports the results from two neural network rule extraction techniques, NeuroLinear and NeuroRule applied to the diagnosis of hepatobiliary disorders. The dataset consists of nine measurements collected from patients in a Japanese hospital and these measurements have continuous values. NeuroLinear generates piece-wise linear discriminant functions for this dataset. The continuous measurements have previously been discretized by domain experts. NeuroRule is applied to the discretized dataset to generate symbolic classification rules. We compare the rules generated by the two techniques and find that the rules generated by NeuroLinear from the original continuously valued dataset to be slightly more accurate and more concise than the rules generated by NeuroRule from the discretized dataset.

Algorithms↗

PubMed2002

Evolving neural networks through augmenting topologies.

An important question in neuroevolution is how to gain an advantage from evolving neural network topologies along with weights. We present a method, NeuroEvolution of Augmenting Topologies (NEAT), which outperforms the best fixed-topology method on a challenging benchmark reinforcement learning task. We claim that the increased efficiency is due to (1) employing a principled method of crossover of different topologies, (2) protecting structural innovation using speciation, and (3) incrementally growing from minimal structure. We test this claim through a series of ablation studies that demonstrate that each component is necessary to the system as a whole and to each other. What results is significantly faster learning. NEAT is also an important contribution to GAs because it shows how it is possible for evolution to both optimize and complexify solutions simultaneously, offering the possibility of evolving increasingly complex solutions over generations, and strengthening the analogy with biological evolution.

Algorithms↗

PubMed2006

Instance-based concept learning from multiclass DNA microarray data.

BACKGROUND: Various statistical and machine learning methods have been successfully applied to the classification of DNA microarray data. Simple instance-based classifiers such as nearest neighbor (NN) approaches perform remarkably well in comparison to more complex models, and are currently experiencing a renaissance in the analysis of data sets from biology and biotechnology. While binary classification of microarray data has been extensively investigated, studies involving multiclass data are rare. The question remains open whether there exists a significant difference in performance between NN approaches and more complex multiclass methods. Comparative studies in this field commonly assess different models based on their classification accuracy only; however, this approach lacks the rigor needed to draw reliable conclusions and is inadequate for testing the null hypothesis of equal performance. Comparing novel classification models to existing approaches requires focusing on the significance of differences in performance. RESULTS: We investigated the performance of instance-based classifiers, including a NN classifier able to assign a degree of class membership to each sample. This model alleviates a major problem of conventional instance-based learners, namely the lack of confidence values for predictions. The model translates the distances to the nearest neighbors into 'confidence scores'; the higher the confidence score, the closer is the considered instance to a pre-defined class. We applied the models to three real gene expression data sets and compared them with state-of-the-art methods for classifying microarray data of multiple classes, assessing performance using a statistical significance test that took into account the data resampling strategy. Simple NN classifiers performed as well as, or significantly better than, their more intricate competitors. CONCLUSION: Given its highly intuitive underlying principles--simplicity, ease-of-use, and robustness--the k-NN classifier complemented by a suitable distance-weighting regime constitutes an excellent alternative to more complex models for multiclass microarray data sets. Instance-based classifiers using weighted distances are not limited to microarray data sets, but are likely to perform competitively in classifications of high-dimensional biological data sets such as those generated by high-throughput mass spectrometry.

Algorithms↗

PubMed1992

Attempts to use computers as diagnostic aids in medical decision making: a thirty-year experience.

For more than 30 years our group of physicians, statisticians, and computer scientists has worked toward developing a computer program with the capability of a trained physician to make diagnostic decisions in the relatively broad medical subspecialty of hematology. We devised and tested many programs, none of which have been sufficiently useful to warrant carrying beyond the pilot-study stage. We analyzed the reasons for this failure. Our experience confirms the great difficulty, and even the impossibility, of incorporating the complexity of human thought into a system that can be handled by a computer. We concluded that we should stop trying to make a computer act like a diagnostician and concentrate instead on ways of making computer-generated relevant information available to physicians as they make decisions.

Algorithms↗

PubMed2026

Artificial Intelligence for Natural Products Discovery and Development.

Natural products (NPs) remain a cornerstone of modern drug discovery, offering stereochemical complexity and diverse bioactivities that precisely modulate therapeutic targets, refined through billions of years of evolution. However, their research has long been hindered by inefficient, empirical workflows, high resource consumption, structural complexity, and the "multicomponent, multi-target" nature of their mechanisms. The exponential growth of genomic, metabolomic, and spectral data has overwhelmed conventional analytical methods, exposing critical bottlenecks in handling high-dimensional, heterogeneous datasets that exceed human interpretive capacity. Artificial intelligence (AI) is emerging as a transformative paradigm to address these challenges, integrating multi-omics and chemical data to shift NP research from fragmented empiricism toward mechanism-driven, precision-oriented development. By leveraging deep learning architectures- including graph neural networks, Transformers, and diffusion-based generative models-AI enables systematic decoding of NP biosynthesis, automated structure elucidation, rational target identification, knowledge extraction from vast unstructured scientific literature, and de novo molecular design. This review comprehensively surveys recent advances in AI applications across the full NP discovery and development pipeline, encompassing genome mining, structure-based and ligand-based virtual screening, multimodal structural characterization, lead optimization, and biosynthetic pathway engineering. We further examine the emerging roles of protein-centric, molecule- centric, and multimodal foundation models, as well as large language models, in bridging genotype-to-chemotype gaps and unlocking unstructured scientific knowledge. Finally, we discuss critical challenges including data scarcity, representational limitations for complex stereochemistry, physical plausibility in generative models, and the urgent need for experimental validation, while outlining future directions toward autonomous experimentation, closed-loop optimization, and human-AI collaborative discovery.

Artificial intelligence↗

PubMed1999

Intelligent control of a planning system for astronaut training.

This work intends to design, analyze and solve, from the systems control perspective, a complex, dynamic, and multiconstrained planning system for generating training plans for crew members of the NASA-led International Space Station. Various intelligent planning systems have been developed within the framework of artificial intelligence. These planning systems generally lack a rigorous mathematical formalism to allow a reliable and flexible methodology for their design, modeling, and performance analysis in a dynamical, time-critical, and multiconstrained environment. Formulating the planning problem in the domain of discrete-event systems under a unified framework such that it can be modeled, designed, and analyzed as a control system will provide a self-contained theory for such planning systems. This will also provide a means to certify various planning systems for operations in the dynamical and complex environments in space. The work presented here completes the design, development, and analysis of an intricate, large-scale, and representative mathematical formulation for intelligent control of a real planning system for Space Station crew training. This planning system has been tested and used at NASA-Johnson Space Center.

Algorithms↗

PubMed2025

DNA methylation and machine learning: challenges and perspective toward enhanced clinical diagnostics.

DNA methylation is an epigenetic modification that regulates gene expression by adding methyl groups to DNA, affecting cellular function and disease development. Machine learning, a subset of artificial intelligence, analyzes large datasets to identify patterns and make predictions. Over the past two decades, advances in bioinformatics technologies for arrays and sequencing have generated vast amounts of data, leading to the widespread adoption of machine learning methods for analyzing complex biological information for medical problems. This review explores recent advancements in DNA methylation studies that leverage emerging machine learning techniques for more precise, comprehensive, and rapid patient diagnostics based on DNA methylation markers. We present a general workflow for researchers, from clinical research questions to result interpretation and monitoring. Additionally, we showcase successful examples in diagnosing cancer, neurodevelopmental disorders, and multifactorial diseases. Some of these studies have led to the development of diagnostic platforms that have entered the global healthcare market, highlighting the promising future of this field.

Humans↗

PubMed2001

An intelligent tele-healthcare environment offering person-centric and wellness-maintenance services.

Worldwide healthcare delivery trends are undergoing a subtle paradigm shift--patient centered services as opposed to provider centered services and wellness maintenance as opposed to illness management. In this paper we present a Tele-Healthcare project TIDE--Tele-Healthcare Information and Diagnostic Environment. TIDE manifests an 'intelligent' healthcare environment that aims to ensure lifelong coverage of person-specific health maintenance decision-support services--i.e., both wellness maintenance and illness management services--ubiquitously available via the Internet/WWW. Taking on an all-encompassing health maintenance role--spanning from wellness to illness issues--the functionality of TIDE involves the generation and delivery of (a) Personalized, Pro-active, Persistent, Perpetual, and Present wellness maintenance services, and (b) remote diagnostic services for managing noncritical illnesses. Technically, TIDE is an amalgamation of diverse computer technologies--Artificial Intelligence, Internet, Multimedia, Databases, and Medical Informatics--to implement a sophisticated healthcare delivery infostructure.

Artificial Intelligence↗

PubMed2026

AI-integrated digital breeding for crop improvement.

Crop breeding increasingly depends on the effective integration and interpretation of large, heterogeneous datasets spanning genomic, phenotypic, multi-omics, and environmental layers. Conventional breeding approaches are often insufficient to capture the complex relationships among these data or to support timely selection decisions. Digital breeding can help address this limitation by complementing field experimentation, mixed models, and genomic prediction with the integration of biological data and computational prediction throughout the breeding process. In particular, the rapid advancement of artificial intelligence (AI) has improved the analysis of high-dimensional datasets and broadened its application to trait prediction, selection, and breeding design. Here, we review recent developments in AI-enabled digital breeding, encompassing genomic, phenomic, and multi-omics data generation and analysis, predictive modeling, explainable and generative AI, and data-driven breeding decision support. We further discuss emerging AI applications, their current contributions to crop research and breeding, and the major considerations affecting their reliable and practical implementation. Collectively, this review provides a structured understanding of the roles of AI across the digital breeding process and offers guidance for future methodological development and practical application in crop improvement.

artificial intelligence↗

PubMed2000

Rule extraction by successive regularization.

Knowledge acquisition is, needless to say, important, because it is a key to the solution to one of the bottlenecks in artificial intelligence. Recently, knowledge acquisition using neural networks, called rule extraction, is attracting wide attention because of its computational simplicity and ability to generalize. Proposed in this paper is a novel approach to rule extraction named successive regularization. It generates a small number of dominant rules at an earlier stage and less dominant rules or exceptions at later stages. It has various advantages such as robustness of computation, better understanding, and similarity to child development. It is applied to the classification of mushrooms, the recognition of promoters in DNA sequences and the classification of irises. Empirical results indicate superior performance of rule extraction in terms of the number and the size of rules for explaining data.

Agaricales↗

PubMed1996

[Permanent cardiac pacing. Present possibilities and perspectives].

Cardiac pacing is reckoned to be one of the most successful branches of medicine in the second half of the 20th century. Indications for single chamber ventricular pacing are getting gradually restricted. This early pacing mode, which was used in 1958 for the first time, has been recently replaced from modern modes: for atrial, dual chamber and rate adaptive ones. Not only these most recent methods prevent from bradycardia but they also meet demands of modern cardiac pacing, i.e. to restore the synchronization of the atriums and the ventricles and to restore the frequency response. So-called physiological pacing improves both the well-being and the survival of patients and it has also extended pacing indications to the non-arrhythmic ones: first and foremost to the cardiomyopathies. It is dual chamber pacing, the artificial biosensors, various kinds of counters and monitoring functions that enhance the energy consumption. Both low-threshold pacing leads and an automatic adjustment of the pulse energy are helpful in extending the longevity of tiny lithium-powered pacemakers of these days. Current dual chamber rate responsive devices represent an important first step towards a fully automatic smart pacemaker of the future. Such an intelligent pulse generator with several sensors will be operating in the DDDR mode checking automatically the pacing threshold and switching its pacing mode according to pathophysiological requirements.

Cardiac Pacing, Artificial↗

PubMedDate not supplied

A computational model for cell differentiation.

In this paper, we present a word set generating mechanism, called cell-differentiation system, inspired by the tissue process formation in multicellular organisms, which might model some properties of evolving communities of living cells at the syntactical level. The tools utilized to model these biological phenomena belong to the formal language theory. In this context chromosomal mutations are defined as operations on strings and the differentiation according to the control of gene expression is represented by some random-context conditions in formal languages. In the presented formal framework we prove that in a simplified form of this formalism, with only one cell-type which is regular, one single cell and no mitosis involved, the problem of establishing whether or not the set of vectors of integers indicating the number of cells in each population, is finite, linear or semilinear, is recursively undecidable. However, one can algorithmically decide whether or not a cell-differentiation system of finite cell-type can produce a specific generation of cells.

Algorithms↗

PubMed2005

A novel ensemble machine learning for robust microarray data classification.

Microarray data analysis and classification has demonstrated convincingly that it provides an effective methodology for the effective diagnosis of diseases and cancers. Although much research has been performed on applying machine learning techniques for microarray data classification during the past years, it has been shown that conventional machine learning techniques have intrinsic drawbacks in achieving accurate and robust classifications. This paper presents a novel ensemble machine learning approach for the development of robust microarray data classification. Different from the conventional ensemble learning techniques, the approach presented begins with generating a pool of candidate base classifiers based on the gene sub-sampling and then the selection of a sub-set of appropriate base classifiers to construct the classification committee based on classifier clustering. Experimental results have demonstrated that the classifiers constructed by the proposed method outperforms not only the classifiers generated by the conventional machine learning but also the classifiers generated by two widely used conventional ensemble learning methods (bagging and boosting).

Algorithms↗