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PubMed1988

CASE, the computer-automated structure evaluation system, as an alternative to extensive animal testing.

CASE, an artificial intelligence system with demonstrated ability to predict biological activity based on structural considerations, correctly predicts animal carcinogenicity. It can, therefore, play a pivotal role in classifying chemicals as carcinogens and prioritizing them for further testing. Additionally, CASE shows promise in the design of pharmacologically active agents by reducing the number of drugs that need to be synthesized and tested. For both of these applications, CASE provides a mechanism to conserve animal and other testing resources.

Animal Testing Alternatives↗
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↗
PubMed1986

A new strategy for clinical decision making: censors and neuroendocrinological diseases.

A patient rarely has a single, isolated disease. The situation is usually much more complex since the different parts of the human organism and its metabolism interact with each other on multiple levels and follow several feedback patterns. These interactions and feedback patterns become even more complex when the effects of the external environment are considered. When several diseases are present, the first steps in medical diagnosis are to determine whether one of the diseases interacts with ("Censors") or changes the significant symptoms, signs, or results of the laboratory tests of the other diseases. We will try, within this paper, to go beyond the scope of the first generation of Artificial Intelligence systems in medicine to determine the effects of two diseases on each other. One important part of the effect of two diseases on each other is the effect of Censors. In addition, causal reasoning, reasoning by analogy, and learning from precedents are important and necessary for a human-like expert in medicine. Their application to thyroid diseases, with an implemented system, are considered in this paper.

Artificial Intelligence↗
PubMed1995

Computer modeling of adaptive depression.

Mild, delimited, and adaptive depression may be a specific example of a more general class of mechanism by which intelligent systems--individual, social, and artificial--adapt to dynamic, uncertain, and dangerous environments. Computer modeling, based on connectionist and artificial intelligence planning and learning programming techniques, supports this hypothesis by generating both adaptive behavior and analogs for 10 phenomena associated with depression: global, stable, and internal failure explantation, a cognitive loop of failure rumination, decreased motivation, self-esteem, and self-efficacy, and increased realism, negative generalization, and cognitive change. The idea of adaptive depression can be applied to more than one level of living systems. A better understanding of normal and adaptive depression may lead to a better understanding of clinical depression.

Adaptation, Psychological↗
PubMed1993

A service-oriented information sources database for the biological sciences.

Researchers in the biological sciences require access to a variety of information sources located in various places on different computer networks. In order to satisfy the information needs of a researcher, appropriate information sources must be selected and access to these information sources and the computing services supporting them must be provided in a way that does not distract the researcher from problems of real interest. At the University of Missouri-Columbia a service-oriented information sources database is being developed as a key component of a layered-model design of an intelligent system which will provide a research environment appropriate to the needs of researchers in the biological sciences.

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↗
PubMed1995

Virtual reality and telepresence for military medicine.

The profound changes brought about by technology in the past few decades are leading to a total revolution in medicine. The advanced technologies of telepresence and virtual reality are but two of the manifestations emerging from our new information age; now all of medicine can be empowered because of this digital technology. The leading edge is on the digital battlefield, where an entire new concept in military medicine is evolving. Using remote sensors, intelligent systems, telepresence surgery and virtual reality surgical simulations, combat casualty care is prepared for the 21st century.

Artificial Intelligence↗
PubMed1995

Computer systems in dysmorphology.

A number of computer systems have been dedicated to some aspect of dysmorphology. These systems have generally been developed in isolation and demonstrate much variation in their design. Some systems are purely database applications designed to take advantage of computer technology in order to provide up to date syndrome compendia. Conversely, a number of research projects have endeavoured to build an intelligent system that can formulate a diagnosis, either through its own knowledge-base, or through interaction with an expert user. This report reviews computer systems in dysmorphology with reference to these two alternative design methodologies. The London Dysmorphology Database and POSSUM provide case studies in a standard database approach. The Skeletal Dysplasia Diagnostician (SDD) is described in order to demonstrate how an expert system might operate in dysmorphology. Other work in the field is reviewed in terms of the common and distinctive aspects of their design with respect to the three aforenamed systems.

Databases, Factual↗
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↗
PubMed1986

The neurobiology of learning and memory.

Study of the neurobiology of learning and memory is in a most exciting phase. Behavioral studies in animals are characterizing the categories and properties of learning and memory; essential memory trace circuits in the brain are being defined and localized in mammalian models; work on human memory and the brain is identifying neuronal systems involved in memory; the neuronal, neurochemical, molecular, and biophysical substrates of memory are beginning to be understood in both invertebrate and vertebrate systems; and theoretical and mathematical analysis of basic associative learning and of neuronal networks in proceeding apace. Likely applications of this new understanding of the neural bases of learning and memory range from education to the treatment of learning disabilities to the design of new artificial intelligence systems.

Animals↗
PubMed1986

A microcomputer-based system for data acquisition and analysis of step-like current jumps due to the opening of single ionic channels in model membranes.

At the cell surface, passive transport is controlled by membrane proteins forming hydrophilic pores (or channels) which span the hydrophobic core of the lipid bilayer. Gating mechanisms determine the occurrence of very small and fast step-like current jumps. We developed an intelligent system to acquire and to analyze signals due to the opening and closing of ionic single channels. A specific algorithm allows us to recognize channel current transitions and to discard false events due to noise peaks or to unwelcome fluctuations of the signal. Statistics of the single channel amplitude and mean life-time can be performed. We report the results obtained from analyzing the characteristics of the gramicidin A single channel in phosphatidylserine model membranes. Mean current values of 2.08 +/- 0.01 pA and life-times of 101 +/- 3 ms. were measured at 100mV applied potentials in KC1 100mM solution.

Biological Transport, Active↗
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↗
PubMed1990

Relationship between carcinogenicity in rodents and the induction of sister chromatid exchanges and chromosomal aberrations in Chinese hamster ovary cells.

Two independent analyses were carried out to compare the induction of sister chromatid exchanges and of chromosomal aberrations as predictors of carcinogenicity. Using both a classical and a Bayesian approach, as well as by analysis of the structural fragments generated by CASE, an artificial intelligence system, it is included that individually neither of these tests is a satisfactory predictor of carcinogenicity. However, because the analysis revealed that each of the cytogenetic assays responds to a different set of structural features associated with carcinogenicity, it can be concluded that the assays can be included in a battery of tests to improve predictivity.

Animals↗
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↗