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At least 19 recordsLinked to original sources
PubMed2026

How AI-supported intelligent systems support infection prevention and control training in healthcare: A systematic review of educational functions and outcomes.

AIMS: Artificial intelligence (AI)-supported intelligent systems have been increasingly incorporated into infection prevention and control (IPC) education and training, primarily to support the monitoring of observable behaviors and the provision of feedback. However, existing evidence has focused largely on short-term compliance outcomes, with limited synthesis of the educational role of AI-supported intelligent systems in supporting sustained IPC competence. This systematic review examined how AI-supported intelligent systems have been designed and used to support IPC education and training, with a focus on system characteristics, educational functions, and reported outcomes. DESIGN: A systematic literature search was conducted across the PubMed/MEDLINE, Embase, Cochrane, and CINAHL databases. DATA SOURCES: A total of 18 studies met the inclusion criteria. Findings were qualitatively synthesized according to system design characteristics, educational functions, and outcome domains. REVIEW METHODS: Methodological quality was appraised using the Mixed Methods Appraisal Tool. RESULTS: Most AI-supported intelligent systems focused on hand hygiene and relied on fully automated monitoring systems to capture behaviors and provide performance feedback. Educational functions were predominantly limited to performance assessment, automated feedback, and reminders. Outcomes were mainly measured using compliance or performance metrics, whereas sustained behavioral change and decision quality were rarely assessed. CONCLUSIONS: AI-supported intelligent systems have been used primarily to reinforce short-term IPC performance and compliance. However, their current applications for supporting sustained competence over time remain limited. The findings of this review suggest that AI-supported intelligent systems may serve as maintenance-oriented educational support by extending learning beyond initial instruction through repeated practice and feedback. Future research should prioritize outcome measures that capture the durability of performance and decision-making processes to better align AI-supported intelligent systems used in IPC education and training with the educational demands of clinical practice.

Humans↗
PubMed1991

Intelligent systems: how can they help?

Expert systems are a branch of the computer science of artificial intelligence. Their ability to mimic experts by applying their domain knowledge has led to the construction of a number of medical applications. A brief resumé of the structure and the processes involved in constructing knowledge-based expert systems is given. Reasons are given for the failure of these successful programs to be widely implemented. It is to be expected that improvements in other areas of artificial intelligence will make them more widely acceptable to the non-expert.

Artificial Intelligence↗
PubMed1991

A model for designing intelligent tutoring systems.

A model for the design of an intelligent tutoring system is presented using artificial intelligence techniques and cognitive processing theories. The model of cognitive processing known as the Knowledge Acquisition and Recall Theory is derived from Anderson's Adaptive Control of Thought Theory. The model is used as a basis for a methodology for the design of an intelligent tutoring system that teaches problem-solving strategies for blood grouping discrepancies. The system developed was tested to determine the effectiveness of the methodology developed and to provide support for the concepts in the model of cognition developed. The preliminary results provide some evidence that an intelligent tutoring system designed using the methodology developed may aid in the knowledge acquisition process. (knowledge acquisition, artificial intelligence, computer-assisted instruction, cognition, intelligent tutoring system).

Artificial Intelligence↗
PubMed2026

Development and Crossover Evaluation of an Artificial Intelligence-Assisted System for Solid Pancreatic Lesion Detection and Pancreatic Parenchyma Recognition in Endoscopic Ultrasonography (With Video).

BACKGROUND AND STUDY AIMS: Pancreatobiliary endoscopic ultrasonography (EUS) is technically demanding, and supervised training opportunities are limited. We developed an artificial intelligence (AI) overlay system for detecting solid pancreatic lesions (SPL) and recognizing pancreatic parenchyma (PP) and evaluated its effect on reader performance. PATIENTS AND METHODS: Across six centers, two deep learning-based models were trained using expert-annotated EUS frames. We then conducted a randomized, two-sequence, two-period crossover reader study in which eight endosonographers (five novices and three experts) interpreted image sets with and without AI assistance. The primary endpoint was superiority of sensitivity for SPL detection among novices; key secondary endpoints included specificity and PP recognition. RESULTS: From 118 patients, 120 SPL-positive/negative image sets and 160 PP-positive/negative image sets were constructed. Among novices, AI assistance improved SPL detection sensitivity (88.7% vs. 76.8%, p&#x2009;<&#x2009;0.001) and accuracy (86.4% vs. 78.7%), while specificity met the predefined noninferiority criterion (84.2% vs. 80.5%, p&#x2009;<&#x2009;0.001). For PP recognition, sensitivity increased numerically (86.3% vs. 83.3%) but did not meet the predefined superiority criterion (p&#x2009;=&#x2009;0.095); specificity met the noninferiority criterion (87.8% vs. 81.0%), and accuracy increased from 82.1% to 87.0%. Among experts, sensitivity was maintained for both tasks, whereas specificity increased with AI assistance. CONCLUSIONS: AI assistance improved SPL detection among novice endosonographers. For PP recognition, sensitivity increased without reaching statistical superiority, whereas specificity met the predefined noninferiority criterion. These findings support a potential adjunctive role for AI in EUS interpretation.

Humans↗
PubMed1976

Model-driven interpretation in intelligent vision systems.

With a constructive knowledge-based theory of perception as its foundation, this paper starts with a review and critique of some artificial-intelligence programs that purport to see. It is then argued that these computer programs for scene analysis offer the hope of providing a more adequate account oo the current psychological theories. This thesis has several aspects. The one emphasized here is that those programs have explored a variety of methods of incorporating a priori knowledge of objects through the use of models. After outlining the range of models used, presenting a set of criteria for evaluating the use of model information, and sketching some psychological theories, the various proposals are contrasted. This discussion leads to two new proposals for exploiting model information that involve elaborations of an existing program, POLY.

Humans↗
PubMed2026

Electrospun Nanofiber Dressings for Diabetic Wounds: From Single-Layer to Intelligent Composite Systems.

Diabetic chronic wounds have become a major challenge for clinical treatment due to their complex pathological microenvironment, including persistent inflammatory response, angiogenesis disorder, excessive oxidative stress, and susceptible infection. Traditional dressings as a passive barrier have difficulty meeting the above multiple treatment needs. Electrospinning technology, with its ability to mimic the fibrous network structure of the natural extracellular matrix (ECM), offers a high specific surface area, controllable porosity, and excellent drug-loading capacity, making it an ideal platform for developing a new generation of multifunctional wound dressings. This article provides a systematic review of the research progress on electrospun nanofiber dressings in the treatment of diabetic wounds, focusing on the design evolution from basic single-layer structures to advanced complex structures and elucidating the mechanisms of action and quantifiable effects of each structural type in addressing specific pathological challenges. We also compared the current status of clinical translation for electrospun dressings with that of other advanced wound care platforms and proposed a standardized preclinical evaluation framework. A large number of research data show that these advanced designs can effectively improve the quality of healing. Finally, this paper points out the challenges faced by this field, such as scalable fabrication, in vivo reliability of smart systems, and long-term biosafety, and provides theoretical basis and technical reference for the design of efficient and intelligent electrostatic spinning diabetic wound dressings.

Nanofibers↗
PubMed1991

Enzymes and theoretical biology: sketch of an informational perspective of the cell.

In the theoretical scenarios of biology, new insights can be gained by the introduction of information-processing and artificial intelligence concepts, helping to organize the explanation of the many intra- and inter-cellular phenomena that molecular biology is accumulating. Enzymes contain some of the immediate clues; the whole informational processing of prokaryotic cells is another central subject of search. Additionally, prolonging the informational perspective of the cell, a significant parallel can be drawn between informational processes in biological, social and artificial intelligence systems. A more tangible definition of biological complexity and biological intelligence emerges.

Cells↗
PubMed1992

A fitness analysis system with an intelligent interface.

This paper describes the development of a system with an intelligent interface for analysis of physiological correlates of athletes' physical performance capacities. The system improves the interface between the physiologist and the coach and provides scientific information in a systematic and coherent fashion. The recommendations provided are based on the results of a series of physiological tests. The implementation of the system is described with emphasis placed on recognition of the internal structure of the knowledge, independence from a particular shell, design for future expansion and maintenance and the integration with existing information resources.

Artificial Intelligence↗
PubMed2026

BioMedGraphica: an all-in-one platform for joint textual biomedical prior knowledge and numeric graph generation.

MOTIVATION: Multiomics data analysis is essential for scientific discovery in precision medicine. However, translating analysis results of omics data analysis into novel scientific hypotheses remains a significant challenge. Human experts must manually review analysis results and generate new hypotheses based on extensive and interconnected biomedical prior knowledge, which is subjective and not scalable. While large language models can accelerate the discovery, their reasoning improves when grounded in structured, auditable, and comprehensive biomedical prior knowledge. However, biomedical knowledge is scattered across heterogeneous databases that use diverse and inconsistent nomenclature systems, making it difficult to integrate resources into a unified format for scalable analysis. This fragmentation limits the ability of artificial intelligence systems to fully leverage biomedical data for scientific discovery. RESULTS: We developed BioMedGraphica, a novel all-in-one platform that harmonizes fragmented biomedical resources by integrating 11 entity types and 30 relation types from 43 databases into a unified textual prior knowledge graph containing 2 306 921 entities and 27 232 091 relations. In addition, we present a novel textual-numeric graph (TNG) data structure concept, where textual information captures prior biological knowledge (e.g. transcription start sites, functions, mechanisms), numeric values represent quantitative biomedical features, and the integrated relations can help uncover mechanisms. By bridging prior knowledge with user-specific data, TNG is a novel and ideal data structure for developing novel graph analysis models. AVAILABILITY AND IMPLEMENTATION: The code is available at: https://github.com/FuhaiLiAiLab/BioMedGraphica and BioMedGraphica knowledge graph database can be downloaded from huggingface dataset: https://huggingface.co/datasets/FuhaiLiAiLab/BioMedGraphica.

Humans↗
PubMed1992

Implementing guidelines in ambulatory practice.

As we understand the process of ambulatory care better, the need to effectively implement standards of practice becomes more apparent. To facilitate successful use of practice guidelines, we have integrated an artificial intelligence system of Medical Logic Modules into our computerized medical record. A rule shell allows rapid development and prototyping of rules which can be practice reminders, information gathering utilities, or standing orders. A set of utilities allows non-programmer clinicians to develop and maintain the rule set. We will demonstrate these enhancements in the context of the comprehensive patient record.

Ambulatory Care↗
PubMedDate not supplied

Obligations of the expert system builder: meeting the needs of the user.

Builders of expert systems have generally accepted the principle that computer software should not be subject to government regulation if health care practitioners can be expected to interpret and apply the systems intelligently. The purpose of this paper is to identify the information that builders must make available to permit health care practitioners to exercise their clinical judgment in interpreting and applying the output of computing systems.

Diagnosis, Computer-Assisted↗
PubMed1991

The future of laboratory automation.

Among the many factors that will define the laboratory of the future are the development of advanced computer communications systems, artificial intelligence, robotic systems, and material storage and retrieval systems. This article examines some of these factors and challenges current automation justification procedures in light of the greater competitive environment of today.

Artificial Intelligence↗
PubMed1977

Medical data base system with an ability of automated diagnosis.

We carried out an experiment on a medical information system in which a clinical data base is combined organically with computer programs for automated diagnosis. In this system, the parameters for automated diagnosis are devised to be renewed as the contents of the data base (patient's information) increase. This system can be regarded as a data base possessing a kind of diagnosing ability which grows up with time. We have named this system "Intelligent Data Base". The algorithm for computer diagnosis used in this study is based on maximum likelihood method, and each likelihood is weighted with a prior probability of each disease. The discrimination efficiency of this method is logically equal to that of the Bayes rule. First 27 cases were learnt by the system and correct diagnosis was obtained in 78% of the cases. When cases for learning increased to 82, the percentage of correct diagnosis was improved to 95%.

Brain Stem↗
PubMed2026

Automated CEAP Classification of Venous Duplex Reports Using Multimodal Artificial Intelligence.

OBJECTIVE: To develop and internally validate a prototype multimodal artificial intelligence system for automated CEAP (Clinical, Etiological, Anatomical and Pathophysiological) classification of venous duplex ultrasound (VDUS) reports, integrating natural language processing of free-text components with computer vision analysis of hand-drawn anatomical diagrams. METHODS: Single centre retrospective observational study using routinely collected clinical data. One thousand consecutive venous duplex ultrasound reports from Cambridge University Hospitals NHS Foundation Trust, UK (July 2024 - May 2025) were labelled according to the CEAP classification, excluding the Etiological component, which could not be reliably determined from duplex reports alone. Transfer learning was applied using ClinicalBERT for text and MobileNetV3 for diagrammatic data. Clinical classes were predicted from request line text. Text- and image-based pathophysiological models were developed for four anatomical territories (Great Saphenous Vein, Small Saphenous Vein, Deep system, Perforators), combined using late fusion with probability averaging. RESULTS: The clinical CEAP model achieved accuracy of 0.91, macro-F1 of 0.82, and macro-AUC of 0.98. Pathophysiological prediction varied, with text models broadly outperforming image models. Fusion yielded heterogeneous benefits, improving SSV performance but reducing Deep system accuracy. The performance of the final pathophysiological CEAP fusion models varied across anatomical territories: accuracy ranged from 0.70-0.92 and macro-AUC from 0.80-0.92. CONCLUSION: This study demonstrates the feasibility of automated CEAP classification from VDUS reports. Despite class imbalance affecting minority class predictions, the strong discriminatory performance validates this multimodal ML model for extracting clinically meaningful information from real-world data. This approach offers potential, pending external validation, to streamline vascular services through automated triage and guideline-compliant decision making.

Artificial intelligence↗
PubMed2025

Integrating explainable artificial intelligence with multiomics systems biology and electronic health record data mining for personalized drug repurposing in Alzheimer's disease.

Alzheimer's disease (AD) is characterized by region- and patient-specific molecular heterogeneity, which hinders therapeutic design. In this study, we introduce PRISM-ML (PRecision-medicine using Interpretable Systems and Multiomics with Machine Learning), an open-source integrated analysis pipeline that combines interpretable machine learning with systems biology and electronic health records data mining to elucidate the molecular diversity of AD and predict promising drug repurposing opportunities. First, we integrated and harmonized transcriptomic (bulk RNA-seq) and genomic (genome-wide association study) data from 2105 brain samples, each with matched data from the same individual (1363 AD patients, 742 controls; 9 tissues), sourced from three independent studies. Random forest classifiers with SHapley Additive exPlanations identified patient-specific biomarkers; unsupervised clustering resolved 36 molecularly distinct subtissues (defined as clusters of samples within a brain tissue that share a specific expression pattern); and gene-gene coexpression networks prioritized 262 high-centrality bottleneck genes as putative regulators of dysregulated pathways. Next, knowledge graph-based drug repurposing predicted six Food and Drug Administration (FDA)-approved drugs that simultaneously target multiple bottleneck genes and multiple AD-relevant pathways. Notably, in a large US de-identified insurance-claims database (n&#x2009;=&#x2009;364&#xa0;733), exposure to promethazine, one of the candidate drugs, was associated with a 57%-62% lower incidence of AD versus an active antihistamine comparator (adjusted hazard ratio 0.38; inverse-probability weighted 0.43; both P&#x2009;<&#x2009;.001), providing real-world support for its repurposing potential. In summary, PRISM-ML, as an explainable multiomics analysis pipeline, is readily transferable to other complex diseases, advancing precision medicine.

Alzheimer Disease↗
PubMed1978

Uses of coronary heart attack registers.

By studying all coronary heart attacks presenting within defined communities it should be possible to avoid the distortions and omissions inherent in hospital-based case series. In practice the technique presents several problems. Measures of frequency and outcome are very sensitive to the diagnostic criteria used. Data of varying quality are mixed and specific attack rates can be calculated only for items for which the census provides a denominator. Patients presenting to different medical services have different outcomes, but probably less because of treatment than because the severity of the attack affects behaviour in it. Despite these problems, some such intelligence system is of value in any comprehensive strategy for coronary heart disease.

Adult↗
PubMed1992

Intelligent alarms reduce anesthesiologist's response time to critical faults.

The proliferation of monitors and alarms in the operating room may lead to increased confusion and misdiagnosis unless the information provided is better organized. Intelligent alarm systems are being developed to organize these alarms, on the assumption that they will shorten the time anesthesiologists need to detect and correct faults. This study compared the human response time (the time between the sounding of an alarm and the resolution of a fault) when anesthesiologists used a conventional alarm system and when they used an intelligent alarm system. In a simulated operating room environment, we asked 20 anesthesiologists to resolve seven breathing circuit faults as quickly as possible. Human response time was 62% faster, decreasing from 45 to 17 s, when the intelligent alarm system was used. The standard deviations in response time were only half as large for the intelligent alarm system. It appears that the computer-based neural network in the intelligent alarm system diagnosed faults more rapidly and consistently than did the anesthesiologists. This study indicates that breathing circuit faults may be more rapidly corrected when the anesthesiologist is guided by intelligent alarms.

Anesthesiology↗