PubMed Health⌕ Search

SEARCH · PubMed Health

Results for “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 613 records · Page 34Linked to original sources
PubMed2026

Recent advances for the pharmaceutical production of highly attenuated poxviruses as viral vector platforms.

INTRODUCTION: Highly attenuated poxviruses serve as potent viral vectors, oncolytic agents, and therapeutic vaccines. They can accommodate and stably maintain a large genomic payload of foreign inserts. Their limited replication in human cells provides an excellent safety profile, but it concomitantly necessitates higher doses of infectious particles for full therapeutic efficacy. AREAS COVERED: We review recent advances in bioprocesses for the pharmaceutical production of poxvirus-based vectors, focusing mainly on the vaccinia virus and the Orf virus. These include upstream processing using highly permissive cell substrates, optimized feeding strategies, and a virus phenotype that facilitates downstream processing. The study explores ongoing challenges and identifies strategies to adapt the downstream process to intensified upstream processes in order to achieve an economic end-to-end production. EXPERT OPINION: For notably increased virus yields of up to 2 log after amplification, we propose to replace classic adsorption chromatography by a collective and continuous purification platform for separating the virus from process-related impurities. Filtration operations facilitate process scalability while reducing volumes, which is beneficial for a flow-through polishing to meet pharmaceutical quality attributes. Combined with artificial intelligence modeling, these advancements alleviate financial pressures on healthcare systems and accelerate the production of novel vaccine candidates for clinical use.

Humans↗
PubMed1997

Quality monitoring, standardized documentation and management with a computerized system in oncology.

Within the last years the prerequisite was prepared to develop a computerized tumor--patient documentation system including quality monitoring and oncological therapy recommendations for every day use. In medicine today, there is an increasing need for quality oriented low cost and transparent management--what is especially true in the field of oncology. The German Federal Authority of Health demands the documentation of all tumor disorders for the establishment of an cancer registry. For these reasons our study group established the program "OncoDoc" in cooperation with the laboratory for Artificial Intelligence of the University Bremen.

Artificial Intelligence↗
PubMed1995

An intelligent interactive system for delivering individualized information to patients.

This paper is a report on the first phase of a long-term, interdisciplinary project whose goal is to increase the overall effectiveness of physicians' time, and thus the quality of health care, by improving the information exchange between physicians and patients in clinical settings. We are focusing on patients with long-term and chronic conditions, initially on migraine patients, who require periodic interaction with their physicians for effective management of their condition. We are using medical informatics to focus on the information needs of patients, as well as of physicians, and to address problems of information exchange. This requires understanding patients' concerns to design an appropriate system, and using state-of-the-art artificial intelligence techniques to build an interactive explanation system. In contrast to many other knowledge-based systems, our system's design is based on empirical data on actual information needs. We used ethnographic techniques to observe explanations actually given in clinic settings, and to conduct interviews with migraine sufferers and physicians. Our system has an extensive knowledge base that contains both general medical terminology and specific knowledge about migraine, such as common trigger factors and symptoms of migraine, the common therapies, and the most common effects and side effects of those therapies. The system consists of two main components: (a) an interactive history-taking module that collects information from patients prior to each visit, builds a patient model, and summarizes the patients' status for their physicians; and (b) an intelligent explanation module that produces an interactive information sheet containing explanations in everyday language that are tailored to individual patients, and responds intelligently to follow-up questions about topics covered in the information sheet.

Anthropology, Cultural↗
PubMed1991

Methods for knowledge extraction from a clinical database on liver diseases.

We performed exploratory data analysis (EDA) to examine the hidden structure in liver disease data. The purpose was to demonstrate the potential of statistical techniques for extracting knowledge from an active HIS (hospital information system) database with decision support. The goal is to give strong support to the creation of new rules or "tuning" of old rules in the knowledge base. This would facilitate utilization of large patient databases, now commonly available, to help build/update decision support systems for improved patient care. Several statistical techniques were investigated. Stepwise discriminant analysis was found to be a good method in discriminating among different disease classes. Results showed that classification strength of a few (3) variables was similar to all the available (19) variables. Other important issues in the work are treatment of missing values as well as atypical values in medical databases. In estimating missing values we utilized both statistical methods and artificial intelligence approaches. Both these approaches were promising in the estimation of missing values. The study showed that several statistical approaches are possible for knowledge extraction from clinical data collected retrospectively.

Artificial Intelligence↗
PubMed2006

Breast tumor malignancy modelling using evolutionary neural logic networks.

The present work proposes a computer assisted methodology for the effective modelling of the diagnostic decision for breast tumor malignancy. The suggested approach is based on innovative hybrid computational intelligence algorithms properly applied in related cytological data contained in past medical records. The experimental data used in this study were gathered in the early 1990s in the University of Wisconsin, based in post diagnostic cytological observations performed by expert medical staff. Data were properly encoded in a computer database and accordingly, various alternative modelling techniques were applied on them, in an attempt to form diagnostic models. Previous methods included standard optimisation techniques, as well as artificial intelligence approaches, in a way that a variety of related publications exists in modern literature on the subject. In this report, a hybrid computational intelligence approach is suggested, which effectively combines modern mathematical logic principles, neural computation and genetic programming in an effective manner. The approach proves promising either in terms of diagnostic accuracy and generalization capabilities, or in terms of comprehensibility and practical importance for the related medical staff.

Artificial Intelligence↗
PubMed1987

[Principles of computer-assisted classification in psychiatry].

The use of computer based diagnostic decision and artificial intelligence are discussed in relation to the problems of validity and reliability of psychiatric diagnoses. The authors suggest a definition of a computer aided classificator as an existent decision system for an automatically analysis of findings.

Artificial Intelligence↗
PubMedDate not supplied

Prognostic factors: guidelines for investigation design and state of the art analytical methods.

The proliferation of putative prognostic factors, derived prognostic indices and computerised prediction of outcome in surgical oncology has led to some confusion over the exact methods available for deriving clinically significant prognostic factors. The realisation that the interaction between factors is often complex and non-linear has led to the development of new statistical techniques. The aim of this article is to review the currently available methods of analysis. A review of the relevant literature available from statistical, medical and computer science sources was performed. Information has been conveyed at a level aimed at producing a practical understanding of the techniques involved rather than their underlying mathematical basis. There are now clear guidelines for the investigation of putative prognostic factors (Table 1). The established role of linear statistical models and prognostic indices remains vitally important for the majority of diseases with many derived prognostic indices having been validated in a prospective fashion. However, in order to improve the delineation of prognostic factors other more complex methods of analysis are now being utilised. Furthermore, the recognition of complex dynamic non-linearity within biological systems has led to the increasing use of non-linear statistical techniques and artificial intelligence. As such it is incumbent upon the modern clinician to be able to understand the basic assumptions required for multivariate analysis and also to realise when alternative statistical techniques should be employed.

Artificial Intelligence↗
PubMed2000

Subjective symptoms acquisition system in a health promotion system for the elderly. Committee of System Development, Council of Japan AMHTS Institutions.

A previous report was concerned with the evaluation of quality of life using a Health Promotion System for the Elderly. In the present report, we describe one part of that system: a subjective symptoms acquisition and reporting system. The main purpose of this system is to permit any physician or nurse to uniformly employ questionnaires to acquire accurate subjective symptoms. This system is applied in three steps. First, the subjective answers to 21 questions displayed on a personal computer are obtained. These answers correspond to the basic subjective symptoms. Second, if a basic subjective symptom is "positive", more detailed questions are automatically generated. Finally, clear sentences regarding subjective symptoms are generated and output as a "finding report". This information is helpful to physicians and nurses in their health-counseling work. An artificial intelligence (AI) program based on "XpertRule" produces detailed questions which are generated by an interactive questionnaire using branching logical rules.

Aged↗
PubMedDate not supplied

Computer-aided diagnosis.

The evolution of logic thought in relation to the predominant recent advances in technology is analyzed. In fact, the computer was the prime mover of this major change. At present the use of sophisticated software has allowed to attain increasingly accurate levels of simulation of human reasoning. The computer has determined profound transformations in many sectors of work, study and research. Among these, it is extremely relevant how the computer has completely transformed medicine and the medical role, especially in the field of radiology. Expert systems are the most interesting and futuristic applications of artificial intelligence. These systems are able to reproduce on a computer the behavior of an expert human being. Some of the recent innovations introduced in the field of breast, thoracic and mainly neural pathology are illustrated.

Artificial Intelligence↗
PubMed2001

AI in medicine on its way from knowledge-intensive to data-intensive systems.

The last 20 years of research and development in the field of artificial intelligence in medicine (AIM) show a path from knowledge-intensive systems, which try to capture the essential knowledge of experts in a knowledge-based system, to data-intensive systems available today. Nowadays enormous amounts of information is accessible electronically. Large datasets are collected continuously monitoring physiological parameters of patients. Knowledge-based systems are needed to make use of all these data available and to help us to cope with the information explosion. In addition, temporal data analysis and intelligent information visualization can help us to get a summarized view of the change over time of clinical parameters. Integrating AIM modules into the daily-routine software environment of our care providers gives us a great chance for maintaining and improving quality of care.

Artificial Intelligence↗
PubMed2001

Which craft is best in bioinformatics?

'Silicon-based' biology has gathered momentum as the world-wide sequencing projects have made possible the investigation and comparative analysis of complete genomes. Central to the quest to elucidate and characterise the genes and gene products encoded within genomes are pivotal concepts concerning the processes of evolution, the mechanisms of protein folding, and, crucially, the manifestation of protein function. Our use of computers to model such concepts is limited by, and must be placed in the context of, the current limits of our understanding of these biological processes. It is important to recognise that we do not have a common understanding of what constitutes a gene; we cannot invariably say that a particular sequence or fold has arisen via divergence or convergence; we do not fully understand the rules of protein folding, so we cannot predict protein structure; and we cannot invariably diagnose protein function, given knowledge only of its sequence or structure in isolation. Accepting what we cannot do with computers plays an essential role in forming an appreciation of what we can do. Without this understanding, it is easy to be misled, as spurious arguments are often used to promote over-enthusiastic notions of what particular programs can achieve. There are valuable lessons to be learned here from the field of artificial intelligence, principal among which is the realisation that capturing and representing complex knowledge is time consuming, expensive and hard. If bioinformatics is to tackle biological complexity meaningfully, the road ahead must therefore be paved with caution, rigour and pragmatism.

Amino Acid Sequence↗
PubMed1991

Parallel distributed processing and neural networks: origins, methodology and cognitive functions.

Parallel Distributed Processing (PDP), a computational methodology with origins in Associationism, is used to provide empirical information regarding neurobiological systems. Recently, supercomputers have enabled neuroscientists to model brain behavior-relationships. An overview of supercomputer architecture demonstrates the advantages of parallel over serial processing. Histological data provide physical evidence of the parallel distributed nature of certain aspects of the human brain, as do corresponding computer simulations. Whereas sensory networks follow more sequential neural network pathways, in vivo brain imaging studies of attention and rudimentary language tasks appear to involve multiple cortical and subcortical areas. Controversy remains as to whether associative models or Artificial Intelligence symbolic models better reflect neural networks of cognitive functions; however, considerable interest has shifted towards associative models.

Artificial Intelligence↗
PubMed2007

Walking: technology and biology.

If all the signs are to be believed, then the twenty-first century will technologically be characterized by machine walking and its relevant products, which possess all chances to become real bulk goods in the course of the next decades. With several university institutes and with Honda and Sony from the industrial side, Japan is today and without any doubt the leading nation in research and development of walking machines. The US and Europe follow at some distance. Walking machines will influence all areas of daily and industrial life and, with the fast evolution of artificial intelligence, will become a real partner of human beings. All relevant technologies are highly interdisciplinary, they will push the future technologies of all technical fields. The special issue on this topic gives a selection of walking machine research and development including some aspects from biology.

Artificial Intelligence↗
PubMed2006

Imitating manual curation of text-mined facts in biomedicine.

Text-mining algorithms make mistakes in extracting facts from natural-language texts. In biomedical applications, which rely on use of text-mined data, it is critical to assess the quality (the probability that the message is correctly extracted) of individual facts--to resolve data conflicts and inconsistencies. Using a large set of almost 100,000 manually produced evaluations (most facts were independently reviewed more than once, producing independent evaluations), we implemented and tested a collection of algorithms that mimic human evaluation of facts provided by an automated information-extraction system. The performance of our best automated classifiers closely approached that of our human evaluators (ROC score close to 0.95). Our hypothesis is that, were we to use a larger number of human experts to evaluate any given sentence, we could implement an artificial-intelligence curator that would perform the classification job at least as accurately as an average individual human evaluator. We illustrated our analysis by visualizing the predicted accuracy of the text-mined relations involving the term cocaine.

Abstracting and Indexing↗
PubMed1991

Laboratory systems integration: robotics and automation.

Robotic technology is going to have a profound impact on the clinical laboratory of the future. Faced with increased pressure to reduce health care spending yet increase services to patients, many laboratories are looking for alternatives to the inflexible or "fixed" automation found in many clinical analyzers. Robots are being examined by many clinical pathologists as an attractive technology which can adapt to the constant changes in laboratory testing. Already, laboratory designs are being altered to accommodate robotics and automated specimen processors. However, the use of robotics and computer intelligence in the clinical laboratory is still in its infancy. Successful examples of robotic automation exist in several laboratories. Investigators have used robots to automate endocrine testing, high performance liquid chromatography, and specimen transportation. Large commercial laboratories are investigating the use of specimen processors which combine the use of fixed automation and robotics. Robotics have also reduced the exposure of medical technologists to specimens infected with viral pathogens. The successful examples of clinical robotics applications were a result of the cooperation of clinical chemists, engineers, and medical technologists. At the University of Virginia we have designed and implemented a robotic critical care laboratory. Initial clinical experience suggests that robotic performance is reliable, however, staff acceptance and utilization requires continuing education. We are also developing a robotic cyclosporine which promises to greatly reduce the labor costs of this analysis. The future will bring lab wide automation that will fully integrate computer artificial intelligence and robotics. Specimens will be transported by mobile robots. Specimen processing, aliquotting, and scheduling will be automated.(ABSTRACT TRUNCATED AT 250 WORDS)

Artificial Intelligence↗
PubMed2026

Agentic AI for Spatial Omics.

This highlight summarises recent advances in agentic artificial intelligence (AI) systems for spatial omics analysis. These systems are compared along two central tensions: autonomy versus accountability, and adaptability versus reproducibility. We argue that progress will depend not on maximising automation, but on defining where autonomy is appropriate.

Artificial Intelligence↗
PubMedDate not supplied

Decision-theoretic refinement planning in medical decision making: management of acute deep venous thrombosis.

Decision-theoretic refinement planning is a new technique for finding optimal courses of action. The authors sought to determine whether this technique could identify optimal strategies for medical diagnosis and therapy. An existing model of acute deep venous thrombosis of the lower extremities was encoded for analysis by the decision-theoretic refinement planning system (DRIPS). The encoding represented 6,206 possible plans. The DRIPS planner used artificial intelligence techniques to eliminate 5,150 plans (83%) from consideration without examining them explicitly. The DRIPS system identified the five strategies that minimized cost and mortality. The authors conclude that decision-theoretic planning is useful for examining large medical-decision problems.

Algorithms↗