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At least 19 recordsLinked to original sources
PubMed1988

Intelligent systems for human resources.

An intelligent system contains knowledge about some domain; it has sophisticated decision-making processes and the ability to explain its actions. The most important aspect of an intelligent system is its ability to effectively interact with humans to teach or assist complex information processing. Two intelligent systems are Intelligent Tutoring Systems (ITs) and Expert Systems. The ITSs provide instruction to a student similar to a human tutor. The ITSs capture individual performance and tutor deficiencies. These systems consist of an expert module, which contains the knowledge or material to be taught; the student module, which contains a representation of the knowledge the student knows and does not know about the domain; and the instructional or teaching module, which selects specific knowledge to teach, the instructional strategy, and provides assistance to the student to tutor deficiencies. Expert systems contain an expert's knowledge about some domain and perform specialized tasks or aid a novice in the performance of certain tasks. The most important part of an expert system is the knowledge base. This knowledge base contains all the specialized and technical knowledge an expert possesses. For an expert system to interact effectively with humans, it must have the ability to explain its actions. Use of intelligent systems can have a profound effect on human resources. The ITSs can provide better training by tutoring on an individual basis, and the expert systems can make better use of human resources through job aiding and performing complex tasks. With increasing training requirements and "doing more with less," intelligent systems can have a positive effect on human resources.

Artificial Intelligence↗
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↗
PubMed1986

The evaluation of artificial intelligence systems in medicine.

This paper discusses the underlying issues in the evaluation of computer systems which apply artificial intelligence in medicine. Three different levels of evaluation are described: the subjective evaluation of the research contribution of a developmental prototype, the validation of a system's knowledge and performance, and the evaluation of the clinical efficacy of an operational system. The paper outlines a number of evaluation issues at each level, and discusses how previous artificial intelligence in medicine evaluations fit into this framework.

Artificial Intelligence↗
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↗
PubMed1989

Social intelligence, a neurological system?

Social intelligence has been researched for almost 70 yr. without definitive findings. During this period almost no attempts have been made to consider the complexity of the brain's anatomy and functions responsible for social competence. An ecological model focusing on social abilities within a biopsychosocial context is discussed along with supporting literature and an hypothesis for research. This argument invokes social intelligence as an independent brain system. It is suggested that neurological structures and chemical activities controlling social skills are directly influenced by the environment, individual beliefs, personal goals, and physiology.

Animals↗
PubMed1985

Challenges facing the distribution of an artificial-intelligence-based system for nursing.

The marketing and successful distribution of artificial-intelligence-based decision-support systems for nursing face special barriers and challenges. Issues that must be confronted arise particularly from the present culture of the nursing profession as well as the typical organizational structures in which nurses predominantly work. Generalizations in the literature based on the limited experience of physician-oriented artificial intelligence applications (predominantly in diagnosis and pharmacologic treatment) must be modified for applicability to other health professions.

Attitude of Health Personnel↗
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↗
PubMed1987

Artificial intelligence. Expert systems for clinical diagnosis: are they worth the effort?

Modeling the decision-making processes of human experts has been studied by scientists who call themselves psychologists and by scientists who say they are students of artificial intelligence (Al). The psychological research literature suggests that experts' decision-making processes can be adequately captured by simple mathematical models. On the other hand, those in Al who are preoccupied with human expertise maintain that complex computer models, in the form of expert systems, are required to do justice to those same processes. The resultant paradox of simple versus complex decision-making models is investigated here. The relevant literatures in psychology and Al are reviewed and, based on these findings, a resolution of the paradox is offered.

Artificial Intelligence↗
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↗
PubMed1988

An expert system designed as a tutoring tool in cervical cytology: TTCC-1 system.

A knowledge-based intelligent system being developed as a computer-assisted tutoring tool that can be used by teachers and students of cervical cytology is described. This system uses a combination of frames and rules as knowledge representation. Combination data structures called "prototypes" characterize the typical cytoplasmic and nuclear features of classes of cervical cells by using a specific prototype for each hypothesized cell type. A hypothesis-directed approach is used for problem solving. Rules are associated with each prototype or hypothesis. There are two major kinds of rules: deducing rules, used for deducing certainty factors (expressed as degrees of certainty of the hypotheses), and strategy rules, used for guiding the user in reaching a conclusion about the test cell. With an easy-to-use interactive menu, the user inputs a series of sets of qualitative cell features, which the system utilizes at various inference stages and finally arrives at a decision about the cell type.

Cell Biology↗