The role of medical informatics in establishing an integrated and intelligent medical information system.
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Information Systems↗
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Artificial intelligence and expert systems in dentistry.
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Dentistry↗
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↗
Automated anesthesia data management and recordkeeping.
It is apparent that judicious application of computer technology to the design and implementation of an automated anesthesia recordkeeping system could afford increased ease of use to the anesthesiologist compared to a manually kept record. Although prototype systems have been developed at academic institutions, and commercially available operating room physiological monitoring systems show increasing capability for some recordkeeping functions, production of an effective AAD-MARKS will depend on the development of suitable display formats and capabilities, markedly improved user interfaces for data input and system control, intelligent graduated alarm systems, and demonstrated reliability, with provision for preservation of critical data and recordkeeping functions and basic physiological data monitoring despite system failure.
Anesthesia↗
Toward an intelligent wound assessment system.
There is general agreement regarding the need for pressure ulcer assessment methodology which more discretely reflects relevant aspects of wound status than does the commonly used staging system. The Pressure Sore Status Tool (PSST) is one such instrument which was developed with consensual expert input. While the psychometric properties of the PSST have been reported in the literature, the instrument was validated using ET nurses, highly trained wound care specialists, and existed only in manual form. This paper reports results from attempts to establish reliability estimates for healthcare practitioners without extraordinary wound care training or experience. The paper further describes the automation of the PSST and provides examples of pressure ulcer profiles tracked over time. Results indicate that inter-rater reliability with general healthcare practitioners was .78 and intra-rater reliability was .89. The practitioners were able to use the PSST for over six months and the automated system allowed analysis of wound healing profiles that would have been difficult using a manual system. These results imply that movement toward an automated system which makes discriminations regarding the effects of various treatment and intervention strategies is possible and practical.
Aged↗
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↗
Artificial intelligence in radiology: decision support systems.
Computer-based systems that incorporate artificial intelligence techniques can help physicians make decisions about their patients' care. In radiology, systems have been developed to help physicians choose appropriate radiologic procedures and to formulate accurate diagnoses. These decision support systems use techniques such as rule-based reasoning, artificial neural networks, hypertext, Bayesian networks, and case-based reasoning. This article reviews these artificial intelligence techniques, describes their application in radiology, and discusses the role that decision support systems may play in radiology's future.
Artificial Intelligence↗
[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↗
MONI: an intelligent database and monitoring system for surveillance of nosocomial infections.
Recording, recognition, and prevention of nosocomial infections are the primary responsibilities of the hospital infection control unit. To perform these tasks, this unit needs information from diverse sources--the patient's symptoms and signs, microbiological and virological test results, and information regarding antibiotics and treatment come from different levels of healthcare delivery. Because of the large amount of data (e.g., about 300 microbiological requests daily) a computer system is required to store this information and to provide a means for subsequent evaluation. MONI (Monitoring of nosocomial infections) is an intelligent database and monitoring system for surveillance and detection of nosocomial infections. Data can be entered into the system manually as well as transferred automatically from external information systems. The central feature of the system is the automatic detection of and calling attention to conditions that may be a detriment to patient recovery, such as possible hospital-acquired infections, risk factors, diseases to be reported, etc. By using this system, we seek to reduce the frequency of infection and the frequency of nosocomial deaths by improving the quality of patient treatment, shortening the length of stay in a hospital, and the use of fewer and/or cheaper antibiotics. MONI provides a means to access relevant medical data (names of infectious agents, antibiotics, department names, monitoring rules, etc.) from a library. This library can be updated or otherwise modified, even during use. An infection control team using this system can customize it to suit the demands of that particular unit. Automatic data transfer from external information systems is made possible by tables that translate between different code systems. The system also offers flexibility; the program can be configured to adapt it for use in other hospitals and institutions. The core element of MONI is the monitoring module, which is implemented as a layer between data input and the database. Upon data acquisition, the system checks the input against several monitoring tools and alerts the user to matches, which may indicate an infection risk. Processing of a rule may be deferred, depending on complexity of the rule and the actual and estimated workload of the system. Examples of the monitoring guidelines are: (1) suspicion of nosocomial infection; (2) infection at a normally sterile site; (3) infection due to bacteria with unusual antibiotic sensitivity patterns; (4) lab report indicates that patient is treated with ineffective antibiotic; (5) possible choice for less expensive antibiotic; (6) infection which is required to be reported to state and/or health authorities; (7) patients receiving prophylactic antibiotics longer than medically indicated; and (8) infections of two or more patients in different wards with the same bacteria (cf. Evans 85). The MONI system was developed at one of the largest hospitals in Europe, the Vienna General Hospital (2,200 beds). This facility serves as the teaching hospital of the University of Vienna Medical School. The size of the hospital and the large amount of data made it necessary to introduce such a system into clinical routine. MONI was programmed in C and C++ with a state-of-the-art graphical user interface (Presentation Manager, Workplace Shell) for OS/2. IBM Database 2 for OS/2 (dB 2/2) was used in constructing the database. The layer between the database and the monitoring application is driven by the multitasking and interprocess communication abilities of OS/2. A pen-based support system that assists in mobile data acquisition is currently under development.
Anti-Bacterial Agents↗
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↗