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PubMed1993

Intelligent Medical Record--entry (IMR-E).

This paper describes an automated medical record designed to allow providers to enter patient data at the point of care. The system runs on PCs and Macintoshes and uses a graphical user interface and object-oriented programming to take advantage of current mouse and pen technologies. The provider acquires all relevant patient data by pointing and clicking at selections on input screens, many of which contain anatomical drawings to help the provider quickly and accurately describe patient findings. The system also generates a grammatically correct progress note using the problem-oriented structure. Furthermore, items identified in the assessment and plans portion of the program can be ported to expert systems for medical decisions assistance or to billing systems. The system allows the provider to obtain the necessary information on a focused patient visit in less than 5 min or to enter a complete history and physical.

Artificial Intelligence↗
PubMed2005

Knowledge logistics as an intelligent service for healthcare.

OBJECTIVES: The technology of grid services is developing fast. This paper presents an approach to the implementation of an intelligent grid service that configures a hospital taking advantage of the knowledge logistics idea. METHODS: The presented approach is based on synergistic integration of knowledge acquired from distributed sources in order to obtain new or complement insufficient knowledge. Presented approach uses ontologies and the formalism of object-oriented constraint networks for knowledge representation and applies ILOG to constraint-based problem solving. RESULTS: The application of the approach is illustrated via a decision support system for a fictitious case study of a hospital configuration in the Binni region. The system showed the ability to produce interrelated solutions for subtasks of the hospital configuration task based on the built ontology and input parameters. Besides, such preferences as cost or time minimization are also considered by the implemented fuzzy logic module that finds a feasible solution. CONCLUSIONS: KL would benefit from the grid, and particularly from the concept of a semantic grid. The scalable architecture of the approach enables its extension with regard to knowledge/information sources number and, thereby, with regard to factors taken into account during complex problem solving. Utilizing ontologies allows integration of the approach into existing processes and facilitates knowledge sharing with similar systems. Application of constraint networks allows rapid problem manipulation and usage of such existing efficient technologies as ILOG.

Artificial Intelligence↗
PubMed2001

Application of the Cross Battery Approach in the assessment of American Indian children: a viable alternative.

This article examines current psychometric and testing practices that appear to do a limited job of assessing the intelligence of American Indian individuals. For several reasons, contemporary approaches are found to be inadequate. Unfortunately, these practices are then employed in making educational decisions and placing these same children into Special Education programs. Alternative methods of testing, including the Gf-Gc Cross-Battery Approach, are discussed and evaluated in terms of usefulness in the evaluation of American Indian children and adolescents. The cross-battery method of testing was found to provide more in-depth procedures for bypassing both language and cultural differences among American Indian individuals.

Adolescent↗
PubMed2026

Artificial intelligence for dental caries detection: An umbrella review.

Artificial intelligence (AI) has been proposed as a tool to improve dental caries detection across imaging modalities; however, its clinical value remains uncertain. This umbrella review aimed to synthesize and critically appraise systematic reviews evaluating AI for caries detection and diagnosis. An umbrella review was conducted following PRIOR guidance (PROSPERO CRD420261340728). Searches were performed in MEDLINE, Embase, Scopus, Web of Science, and Google Scholar up to 15 March 2026. Methodological quality was assessed using AMSTAR 2, and overlap of primary studies was quantified using the corrected covered area (CCA). Seventeen systematic reviews were included, of which five reported diagnostic test accuracy meta-analyses using bivariate or HSROC models. Across these meta-analyses, pooled sensitivity ranged from 0.76 to 0.94 and specificity from 0.85 to 0.91. Most systems were based on deep learning models applied to bitewing radiographs and intraoral photographs. However, substantial heterogeneity was observed in imaging modalities, lesion thresholds, analytical tasks, and evaluation metrics. In addition, a high degree of overlap across reviews and recurrent methodological limitations, including reliance on retrospective datasets, limited external validation, and inconsistent reporting, substantially weaken the reliability of the evidence. Although AI models demonstrate high diagnostic performance under experimental conditions, current evidence does not support their use as stand-alone diagnostic tools. Their clinical applicability remains limited, and implementation should be restricted to decision-support contexts until robust prospective validation demonstrates meaningful impact on clinical decision-making and patient outcomes.

Dental Caries↗
PubMed2005

Artifacts and collaborative work in healthcare: methodological, theoretical, and technological implications of the tangible.

Although modeled as knowledge work with emphasis on data flow and decision making, healthcare is delivered in the context of a highly structured physical environment, with much effort and emphasis placed on physical and spatial arrangement and re-arrangement of workers, patients, and materials. The tangible aspects of highly collaborative healthcare work have profound implications for research and development of information and communication technology (ICT) despite the tendency to model work as flow of abstract data items. This article reviews field studies in healthcare and other domains on the role of artifacts in collaborative work and draws implications in three areas: methodological, theoretical, and technological. In regard to methodologies, assessment of new ICT and development of user requirements should take into account how artifacts are used and exploited to facilitate collaborative work. In regard to theories, the framework of distributed cognition provides a starting point for modeling the contribution and exploitation of physical artifacts in supporting collaborative work. In regard to technology, design and deployment of new technology should support the functions provided by physical artifacts replaced or disrupted by new technology, and profitable ways for new technology to support collaborative work by embedding ICT into existing infrastructure of physical artifacts.

Artifacts↗
PubMed2004

Potential impact of advanced clinical information technology on healthcare in 2015.

Clinical information technologies now sporadically available will soon be in routine clinical use, bringing many changes to healthcare. For example, 1) The next generation Internet; 2) Real-time clinical decision support systems; 3) Off-line, population-based systems; 4) Large, integrated, individual patient-level phenotypic and genotypic databases with intelligent data mining capabilities; 5) Wireless, invasive and non-invasive physiologic monitoring devices; 6) Natural Language Processing (NLP) systems; and 7) Mathematical models of complex biological systems have the potential to impact significantly the future healthcare delivery system. While new information management and communication techniques and technologies will reduce many of the inefficiencies and inaccuracies of our present systems, there will be an equal, and potentially far more dangerous, set of unintended consequences. Informatics investigators and health system administrators must focus on the study of what is working and what is not, as well as, on development and testing of the new clinical information management and communication technologies, if we are to be ready for the future.

Databases as Topic↗
PubMedDate not supplied

MICRO-IDEA: improving decisions in epidemiological analysis by a microcomputer.

A microcomputer system has been designed to promote uniform and correct epidemiological data handling in the Local Health Units of the National Health Service in Italy. The system allows to define files of any format, to perform the common data manipulation procedures and to make simple statistical analyses. Two modules, based on artificial intelligence techniques, give the possibility to input from 'experts' and to update domain-specific knowledge concerning facts which characterize the application area covered by the file, and some consultation models which represent the reasoning to follow when solving problems of epidemiological data analysis. A decision support module allows an inexpert user to have access to these models, and to be guided in the choice of the appropriate procedure. The menu-driven interactive approach insures a friendly user-to-system interface, and entails a little training for effective use by health workers inexpert in informatics.

Artificial Intelligence↗
PubMed2003

Behavioral Dyscontrol Scale deficits among traumatic brain injury patients, part II: Comparison to other measures of executive functioning.

The clinical utility of the Behavioral Dyscontrol Scale (BDS) was compared to that of verbal fluency, the Trail Making Test, and the Stroop Color-Word Test, as well as measures of processing speed/cognitive efficiency and manual dexterity. The ability of these measures to classify 49 TBI patients into frontal versus nonfrontal and mild to moderate versus severe groups was examined. The results showed that the Fluid Intelligence Factor of the BDS improved classifications above and beyond traditional executive measures, but was particularly successful at classifying patients who sustained mild injuries. In contrast, traditional executive instruments were successful at lesion location classifications only among the patients with severe injuries. Severity classifications were successful both for traditional measures of processing speed/cognitive efficiency and for the Motor Programming Factor of the BDS, but only among patients with nonfrontal injuries. These results demonstrate that severity of injury may be an important moderator of tests' sensitivity to frontal lobe involvement.

Adult↗
PubMed2026

Beyond antibiotics: artificial intelligence-enabled anti-infective ecosystems for next-generation precision therapeutics against antimicrobial resistance.

The rapid global expansion of antimicrobial resistance (AMR) threatens to undermine decades of progress in infectious disease management and highlights the limitations of conventional antibiotic-centered therapeutic strategies. Although emerging technologies-including antimicrobial peptides, bacteriophage therapy, CRISPR-based antimicrobials, microbiome therapeutics, anti-virulence approaches, nanotechnology-enabled drug delivery, and artificial intelligence (AI)-have individually demonstrated considerable promise, they are predominantly being developed as independent interventions rather than as coordinated components of an integrated therapeutic strategy. This Perspective proposes the Intelligent Anti-Infective Ecosystem (IAIE) as a conceptual systems-level framework that computationally integrates multimodal diagnostics, pathogen genomics, microbiome profiling, AI-assisted decision support, programmable precision therapeutics, ecological monitoring, and longitudinal clinical feedback within a continuously learning dynamically optimized workflow. Unlike existing paradigms that primarily optimize individual technologies or therapeutic decisions, IAIE emphasizes closed-loop coordination among complementary antimicrobial approaches to support precision-guided infection management while preserving microbiome integrity and mitigating resistance selection pressure. We further outline the core components, operational principles, translational challenges, and technology readiness of the major therapeutic platforms that could contribute to such an ecosystem, while distinguishing clinically established interventions from emerging experimental strategies. Importantly, IAIE should be interpreted as a prospective conceptual architecture rather than an existing clinical platform. Its proposed clinical value remains to be established through sequential computational, preclinical, and prospective clinical investigations using standardized microbiological, ecological, and patient-centered outcome measures. By framing antimicrobial innovation within an responsive systems perspective, IAIE provides a roadmap for future multidisciplinary research aimed at integrating artificial intelligence and systems microbiology to enable sustainable management of antimicrobial resistance.

Humans↗
PubMedDate not supplied

Knowledge-based systems in medicine--a Nordic research and development programme.

A Nordic research and development programme, 'KBS in Medicine' (KUSIN-MEDICINE), was run in 1986-89. Its main goal was to acquire an understanding of applying knowledge-based techniques in medicine and of the limitations of present-day artificial intelligence (AI) methodologies. The programme comprised four experimental installation sites (Tampere in Finland, Uppsala and Linköping in Sweden, and Aalborg in Denmark) each prototyping in one or more medical domains. The programme was financed by the Nordic Fund for Technological and Industrial Development, by national funds for applied research and by a number of industries. Prototype decision support systems were produced in the following domains: intensive care (Tampere, Uppsala, Linköping, Aalborg), clinical chemistry (Tampere, Uppsala) and clinical neurophysiology (Aalborg in collaboration with Turku and Uppsala). These served to transfer this technology to industry and helped to identify limitations of this technology.

Artificial Intelligence↗
PubMed1994

Artificial intelligence within the chemical laboratory.

Various techniques within the area of artificial intelligence such as expert systems and neural networks may play a role during the problem-solving processes within the clinical biochemical laboratory. Neural network analysis provides a non-algorithmic approach to information processing, which results in the ability of the computer to form associations and to recognize patterns or classes among data. It belongs to the machine learning techniques which also include probabilistic techniques such as discriminant function analysis and logistic regression and information theoretical techniques. These techniques may be used to extract knowledge from example patients to optimize decision limits and identify clinically important laboratory quantities. An expert system may be defined as a computer program that can give advice in a well-defined area of expertise and is able to explain its reasoning. Declarative knowledge consists of statements about logical or empirical relationships between things. Expert systems typically separate declarative knowledge residing in a knowledge base from the inference engine: an algorithm that dynamically directs and controls the system when it searches its knowledge base. A tool is an expert system without a knowledge base. The developer of an expert system uses a tool by entering knowledge into the system. Many, if not the majority of problems encountered at the laboratory level are procedural. A problem is procedural if it is possible to write up a step-by-step description of the expert's work or if it can be represented by a decision tree. To solve problems of this type only small expert system tools and/or conventional programming are required.(ABSTRACT TRUNCATED AT 250 WORDS)

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↗
PubMed2026

Artificial Intelligence Cannot Replace Peer Reviewers but May Help Editors Triage: A Comparative Analysis of a Large Language Model and Human Reviewer Recommendations at the American Journal of Sports Medicine.

BACKGROUND: The peer review system faces increasing strain from rising manuscript volumes, reviewer fatigue, and well-documented interreviewer disagreement. Large language models (LLMs) have shown potential to support the peer review process, but their ability to replicate editorial decisions at high-impact medical journals and their utility as manuscript screening tools remain unknown. PURPOSE: To compare the agreement between an LLM and the final editorial decision on manuscripts submitted to the American Journal of Sports Medicine and to evaluate the potential of LLMs as a manuscript screening tool. STUDY DESIGN: Cross-sectional agreement study. METHODS: Fifty-four manuscripts randomly selected from submissions to the American Journal of Sports Medicine (September 2024-October 2024) were reviewed by a locally deployed LLM (Ministral 3 14B; Mistral AI) using a standardized prompt. The artificial intelligence (AI) produced a categorical recommendation (reject, cascade, revision, or accept) and a numerical score (0-100) for each manuscript. Agreement with the final editorial decision was assessed by Cohen kappa (4-category model) for pooled human reviewers (n = 139 reviews) and the AI (n = 54). Screening performance was evaluated by positive predictive value (PPV), sensitivity, and specificity. RESULTS: Pooled human reviewers demonstrated fair agreement with the final decision (&#x3ba; = 0.181 [P < .001]; 42.4% agreement), while the AI demonstrated slight, nonsignificant agreement (&#x3ba; = 0.126 [P = .099]; 37.0% agreement). The AI recommended revision for 61.1% of manuscripts, of which 72.7% were ultimately rejected or cascaded, demonstrating systematic "revision bias." When the AI recommended rejection, 54.5% of those manuscripts were ultimately rejected and 27.3% were cascaded; when the AI recommended cascade, 50% were rejected and 50% were cascaded. However, when the AI recommended rejection or cascade (n = 21), 90.5% received a final decision of rejection or cascade (PPV, 90.5%; specificity, 81.8%). Manuscripts with an AI score <70 were rejected or cascaded 88.0% of the time (PPV, 88.0%). CONCLUSION: AI cannot replicate the nuanced judgment of human peer reviewers at a high-impact sports medicine journal. When AI recommended rejection or cascade, 90.5% of manuscripts received that final decision (descriptive PPV, 90.5%; 95% CI, 71.1%-97.3%), suggesting potential utility as an exploratory first-pass screening tool warranting further validation in larger cohorts. However, AI could not reliably distinguish manuscripts destined for outright rejection from those that would be cascaded to a sister journal-an important limitation for editorial triage applications.

Sports Medicine↗
PubMed2005

Symbol-string sensitivity and adult performance in lexical decision.

In this study of adult readers, we used a symbol-string task to assess participants' sensitivity to the position of briefly presented, non-alphabetic but letter-like symbols. We found that sensitivity in this task explained a significant proportion of sample variance in visual lexical decision. Based on a number of controls, we show that this relationship cannot be explained by other factors including: chronological age, intelligence, speed of processing and/or concentration, short term memory consolidation, or fixation stability. This approach represents a new way to elucidate how, and to what extent, individual variation in pre-orthographic visual and cognitive processes impinge on reading skills, and the results suggest that limitations set by visuo-spatial processes constrain visual word recognition.

Adult↗
PubMed2006

Automated identification of adverse events related to central venous catheters.

Methods for surveillance of adverse events (AEs) in clinical settings are limited by cost, technology, and appropriate data availability. In this study, two methods for semi-automated review of text records within the Veterans Administration database are utilized to identify AEs related to the placement of central venous catheters (CVCs): a Natural Language Processing program and a phrase-matching algorithm. A sample of manually reviewed records were then compared to the results of both methods to assess sensitivity and specificity. The phrase-matching algorithm was found to be a sensitive but relatively non-specific method, whereas a natural language processing system was significantly more specific but less sensitive. Positive predictive values for each method estimated the CVC-associated AE rate at this institution to be 6.4 and 6.2%, respectively. Using both methods together results in acceptable sensitivity and specificity (72.0 and 80.1%, respectively). All methods including manual chart review are limited by incomplete or inaccurate clinician documentation. A secondary finding was related to the completeness of administrative data (ICD-9 and CPT codes) used to identify intensive care unit patients in whom a CVC was placed. Administrative data identified less than 11% of patients who had a CVC placed. This suggests that other methods, including automated methods such as phrase matching, may be more sensitive than administrative data in identifying patients with devices. Considerable potential exists for the use of such methods for the identification of patients at risk, AE surveillance, and prevention of AEs through decision support technologies.

Artificial Intelligence↗
PubMed1986

Facial form and function: films versus physical examination.

Criteria for the use of radiography for facial trauma in the emergency department have not been the subject of any critical reviews. Many recent reviews have been done on the advantages of various radiologic techniques to delineate specific facial injuries, both bony and soft tissue. No prospective study has addressed the criteria for ordering radiographs in the ED. Ideally the criteria would need only to consider the utility of studies in making therapeutic decisions. Unfortunately other considerations are involved, such as cost, medicolegal liability, and the real and imagined risks of radiation exposure. The intelligent use of radiologic examinations must be based on a thorough history and physical examination, a knowledge of the likely bony and soft tissue injuries, and an understanding of their concomitant physical signs and symptoms. In addition, the treating physician must know the options available in the radiology department.

Emergencies↗
PubMedDate not supplied

ESP: a method to predict toxicity and pharmacological properties of chemicals using multiple MCASE databases.

We describe here the development of a computer program which uses a new method called Expert System Prediction (ESP), to predict toxic end points and pharmacological properties of chemicals based on multiple modules created by the MCASE artificial intelligence system. The modules are generally based on different biological models measuring related end points. The purpose is to improve the decision making process regarding the overall activity or inactivity of the chemicals and also to enable rapid in silico screening. ESP evaluates the significance of the biophores from a different viewpoint and uses this information for predicting the activity of new chemicals. We have used a unique encoding system to represent relevant features of a chemical in the form of a pattern vector and a genetic artificial neural network (GA-ANN) to gain knowledge of the relationship between these patterns and the overall pharmacological property. The effectiveness of ESP is illustrated in the prediction of general carcinogenicity of a diverse set of chemicals using MCASE male/female rats and mice carcinogenicity modules.

Algorithms↗