Artificial intelligence and expert systems in dentistry.
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Dentistry↗
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Results for “Intelligent Systems”
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.
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Cancer chronotherapy: a drug delivery challenge.
The toxicity and/or efficacy of more than twenty anticancer agents have been shown in various experimental systems to be dependent upon the circadian timing of their bolus administration or the circadian shaping of their continuous infusion. In cancer patients, the toxicity of several single agents, given either as bolus or infusion, and a growing number of drug combinations have been shown to similarly depend upon their timing. While clinical trials currently underway demonstrate that the circadian stage of drug toxicity and dose intensity each depend upon their circadian timing, definitive investigations of whether or not cancer control and patient survival are similarly dependent upon circadian treatment timing are currently under way. Both clinical trials of treatment timing and chronotherapy depend totally upon the development and use of programmable wearable and implantable, single-channel and multi-channel, open and eventually closed loop delivery systems. First generation intelligent delivery systems are currently available, work well, are economical and are destined, for economic reasons, to be more widely used. When used, each system requires temporal input, making it impossible to avoid specification of drug sequence, interval between drugs or treatment cycles and circadian treatment timing. The advent of biological therapy with cytokines and growth factors makes it likely that the precise timing of cancer therapies will be of growing importance.
Animals↗
Medical applications of artificial intelligence and information systems.
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Expert Systems↗
Medical application of artificial intelligence and information systems.
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Expert Systems↗
The goal of PACS in Nagoya University Hospital.
In Nagoya University Hospital, a Radiology Intelligent Information System (RIIS) is under construction which will be linked with the Hospital Intelligent Information System (HIIS). RIIS is composed of the radiation oncology information system and the diagnostic radiology information system which is named Imaging Diagnosis Intelligent Information System (IDIIS). IDIIS consists of three parts: (a) the Imaging Diagnosis Management System (IDMS); (b) the Picture Archiving and Communication System (PACS); (c) the Report Generation Support System for Imaging Diagnosis (RGSS-ID). Artificial intelligence methodology is applied to RGSS-ID and IDMS which includes the ordering and scheduling system of diagnostic imaging. IDIIS has an important role to improve the quality of patient care and medical education as well as image management and is an essential component for the implementation of HIIS.
Computer Systems↗
Computerized measurement of speech intelligibility. I. Development of system and procedures.
An experimental system for the measurement of speech intelligibility has been developed. It uses a Personal Computer (PC), together with appropriate software to handle playback of test words in carrier phrases, presented in a 'closed response' test condition. Information about the intelligibility, based on the correct responses and the confusions, is immediately available due to simultaneous collecting and sorting of subjects' responses. The system works satisfactorily and reliably and has been well received by experimenters as well as by adult test subjects in the age range 18-70 years. From a new Danish standard speech material for audiological purposes, a Multiple Choice speech intelligibility test has been devised. The test is called 4AFC (Four Alternative Forced Choice) and is based on monosyllabic words with consonant confusions. Normative data for the 4AFC test, obtained with the computerized system, are presented in an accompanying paper.
Adolescent↗
[Intelligent instrumentation in medicine].
The introduction of intelligent robots, expert systems and other forms of intelligent automatization in the current practice of medicine seems to be inevitable. It appears interesting to look back to the efforts that have been done, since the former steps, about three decades ago and consider the prospects in this field for both short and long term. Simultaneously it is interesting to reckon the new aspects which are raised with the evolution of these methodologies such as the responsibility of decisions taken by intelligent systems, the probable advantages, at the present stage, of the interactive systems and the risk of self-learning systems. Some efforts carried out in our department in this field are described.
Artificial Intelligence↗
Computer-assisted breast cancer grading.
Morphological tumour differentiation has been shown in numerous studies to give a good prognosis in breast cancer, but as histological grading is based upon a subjective assessment of microscopical appearances, difficulties in consistency and reproducibility are inevitable. A review of the many conventional methods served to highlight a common limitation in their approach; lack of structure. We introduce a new approach which seeks to overcome the problem, by formalizing the methods and identifying aspects which are well suited to computer aided analysis, these being incorporated into a microcomputer system facilitating the collection and appraisal of morphometric data. Within the Information Technology Institute (ITRI) at Brighton Polytechnic a research team is carrying out multidisciplinary work into the elucidation of biological systems. This programme, entitled 'Intelligent Medical Systems', used methods of mathematical signal processing and artificial intelligence, applied to a number of areas, one of which is described in this paper. The aim has been to utilize the inherent skill exercised by the histopathologist in interpreting microscopical images, whilst making quantitization more accurate and reproducible. the system has been developed within a highly structured framework and will have applications in teaching and routine histological analysis. The value of artificial intelligence techniques in the wider issues of this area is discussed.
Artificial Intelligence↗
Receptor multispecificity and similarities between the immune system, the sense of smell, and the nervous system from the standpoint of clinical allergology.
A discussion of possible future trends in the application of allergology to clinical practice is presented. Using the implications of antibody multispecificity as a basis, we compare the immune system and the sense of smell and examine the similarities between the immune system and the nervous system.
Binding Sites, Antibody↗
Development of an expert system for haemodynamic monitoring: computerized symbolization of on-line monitoring data.
The development of intelligent alarm systems for intensive care benefits from the transformation of data from a quantitative to a qualitative mode. We constructed a computerized algorithm for the symbolization of on-line monitoring data of heart rate, systemic arterial, pulmonary arterial and central venous pressures, as well as central and peripheral temperatures. We tested the ability of the algorithm to symbolize the levels of the parameters and to detect significant long-term trends in ten adult patients admitted to the intensive care unit after cardiac surgery. The estimations of an experienced clinician were taken as the 'gold standard'. The symbolization of the levels of the monitored parameters was in agreement with the clinician in 99.4% of the estimations. The algorithm detected 93.0% of the trends correctly and also estimated their reliability. The clinician considered its estimations to be accurate in 96.2% of cases. On the other hand, the clinician considered unreliable 2.4% of all the trends detected and classified as reliable by the algorithm. The computerized algorithm for the symbolization of real-time monitoring data performed efficiently enough for its further use in expert systems for intelligent monitoring.
Algorithms↗
AI-driven CRISPR screening: optimizing gene editing through automation and intelligent decision support.
BACKGROUND: CRISPR-based genetic screening has become a central methodology in functional genomics, enabling systematic interrogation of gene function, genetic interactions and context-dependent vulnerabilities at scale. However, the rapid expansion of screening modalities-including multi-condition designs, combinatorial perturbations, in vivo applications and single-cell readouts-has exposed fundamental limitations of heuristic-driven experimental design and post hoc statistical analysis. MAIN BODY: This Review synthesizes how artificial intelligence is reshaping CRISPR screening by introducing predictive, adaptive and system-level intelligence across the experimental lifecycle. We organize recent advances into two tightly coupled modules. First, machine learning and deep learning (ML/DL) methods optimize experimental design by learning context-dependent perturbation behavior, anticipating confounding effects and enabling iterative, information-efficient screening strategies. Second, large language model-agent (LLM-agent) systems complement these advances by externalizing scientific reasoning, integrating biological knowledge at scale and coordinating analysis and decision-making in human-in-the-loop workflows. CONCLUSIONS: Together, ML/DL and LLM-agent approaches reframe CRISPR screening from a static analytical pipeline into an intelligent experimental system, with important implications for robustness, scalability and biological discovery.
Artificial Intelligence↗
Decision analysis: a framework for critical care decision assistance.
The ultimate goal of medical computer systems is to help clinicians make good decisions. Such systems must be based on sound principles. Decision analysis is a 25-year-old discipline that provides the needed rigorous foundation for decision assistance. Decision analysis comprises the philosophy, procedures, and tools that can correct the flaws in existing critical care decision-making practice. Intelligent decision systems--computer-based systems that automate decision analysis--make it practical to apply decision analysis to critical care. Orchestra is a pilot intelligent decision system (now under development) that coordinates the efforts of the critical care specialist, the bedside physician, and the bedside nurse in building decision models that can provide recommendations and insight for ventilator management decisions. Decision analysis delivered by intelligent decision systems has great potential for improving critical care decision-making.
Algorithms↗
Modeling and artificial intelligence approaches to enzyme systems.
Modeling is a means of formulating and testing complex hypotheses. Useful modeling is now possible with biological laboratory microcomputers with which experimenters feel comfortable. Artificial intelligence (AI) is sufficiently similar to modeling that AI techniques, now becoming usable on microcomputers, are applicable to modeling. Microcomputer and AI applications to physiological system studies with multienzyme models and with kinetic models of isolated enzymes are described. Using an IBM PC microcomputer, we have been able to fit kinetic enzyme models; to extend this process to design kinetic experiments by determining the optimal conditions; and to construct an enzyme (hexokinase) kinetics data base. We have also used a PC to do most of the constructing of complex multienzyme models, initially with small simple BASIC programs; alternative methods with standard spreadsheet or data base programs have been defined. Formulating and solving differential equations in appropriate representational languages, and sensitivity analysis, are soon likely to be feasible with PCs. Much of the modeling process can be stated in terms of AI expert systems, using sets of rules for fitting and evaluating models and designing further experiments. AI techniques also permit critiquing and evaluating the data, experiments, and hypotheses being modeled, and can be extended to supervise the calculations involved.
Artificial Intelligence↗
On a common structure of intelligence in biological and technical systems.
This is a survey of the general structure of an electronic computer-implemented, operation-oriented system, designed by the authors, which uses artificial intelligence mechanisms and is intended for the control of technical objects that function both in predictable and random environments. The system is based on processing knowledge, which is stored in a hierarchically arranged Knowledge Bank, and program mechanisms for adapting to and interacting with the External and Internal Worlds. The system has distributed program mechanisms, which are 'designed' with a constant structure. It is independent of the purpose and environment of the system operation and the specific features of the controlled object. None of the program mechanisms are concentrated in any program module. They are distributed in many modules, and therefore there is no single module responsible for the execution of a particular external function. The system is structured into separate program modules by internal procedures. The conceptual organisation of the Knowledge Base presupposes that the framework is structured according to functional, semantic and tier indications, i.e. the structured description of knowledge, by the system, of the external environment and its possible behaviour in it. The possibility of multiple use of the same elements of the lower tiers of the Knowledge Base by higher-tier elements makes the proposed Knowledge Base very efficient. If the cerebrum is considered at a structural level, there appears to be an amazing similarity between the structure and the mechanisms of the above system and the structure of the cerebral cortex, as suggested previously by Edelman and Mountcastle. Each mechanism of the cerebral cortex structure has a structural analogue in the described system.(ABSTRACT TRUNCATED AT 250 WORDS)
Algorithms↗
Improving insurance deduction identification: a hybrid artificial intelligence model using machine learning and expert systems.
PURPOSE: Financial challenges in healthcare systems worldwide, especially in low- and middle-income countries like Iran, have increased hospitals' reliance on insurance reimbursements. Unrecognized insurance deductions often cause severe financial shortages, making efficient deduction management crucial. This study aimed to design a hybrid intelligent system for identifying and predicting insurance deductions by combining machine learning and expert system frameworks. DESIGN/METHODOLOGY/APPROACH: A mixed-methods design was applied in four stages. First, a scoping review identified the causes and patterns of insurance deductions. Second, interviews with 15 insurance experts produced a validated checklist and a dataset from inpatient billing records. Third, using the CRISP-DM methodology, machine learning algorithms were developed and tested in SPSS Modeler alongside a fuzzy expert system developed in MATLAB. Finally, the model was validated using the holdout method. FINDINGS: Four categories of deduction drivers were identified: service provision, registration errors, document submission issues, and revenue conversion processes. The CHAID decision tree outperformed other algorithms with a 99% precision rate and the lowest Mean Absolute Error (9.43). A brief assessment of potential overfitting was conducted to ensure that the CHAID model's high accuracy was interpreted cautiously and supported by the validation results. The fuzzy expert system with validated rules was adaptable for deduction classification, especially for cases unsuitable for quantitative modeling. ORIGINALITY/VALUE: The hybrid model improves detection and prevention of deductions, offering actionable insights for hospital administrators, insurers, and policymakers. Its implementation can enhance hospital information systems, streamline claims processing, and optimize revenue management amid financial constraints.
Machine Learning↗
Databases, artificial intelligence and knowledge-based systems for molecular biology.
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Artificial Intelligence↗
Self-organisation and living systems: Is DNA an 'artificial intelligence'?
There seems little doubt that the maintenance and development of living systems is crucially dependent on an internal organisation of monumental complexity--particularly in higher living species. It is suggested that current thinking--particularly relating to the role of DNA in the total process cannot explain the underlying mechanisms and that a radical rethinking will be necessary. To this end it is proposed that DNA has a unique molecular electronic structure enabling it to operate as a computer analogue system for the highly efficient storage of information and as a type of artificial intelligence through which the information is translated and implemented to organise and control all aspects of the construction and activity of living systems.
Artificial Intelligence↗
Validation of the pediatric speech intelligibility test in children with central nervous system lesions.
The pediatric speech intelligibility (PSI) test was administered to 21 children with a variety of documented central nervous system (CNS) lesions. Ages ranged from 3 to 8 years. PSI test results demonstrated both high sensitivity and high specificity. Results were consistently (1) abnormal in children with lesions in areas of the brain important for auditory function (CNS auditory disorders) and (2) normal in children with lesions in areas anatomically remote from auditory nuclei and pathways (nonauditory CNS disorders).
Auditory Threshold↗