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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.
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.
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.
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%.
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.
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.
This paper partly addresses the question "What artificial intelligence (AI) tools are appropriate for which parts of pathology?" by analyzing the structure and components of knowledge in pathology (e.g., observations plus archival and reference data) and which aspects of that knowledge should be expressible in an AI consulting system. The different aspects of uncertainty (observational, prevalence and validity) play an important role in both human and computer-based decision-making processes, as do relationships between the components of knowledge. The design of an AI consultant system is discussed in terms of the way uncertainty is expressed and in how many parameters, the way uncertainty is propagated (Bayes, certainty factors, Dempster-Schafer, logic or Pathfinder heuristic methods), whether the system reasons from data to a conclusion or vice versa and what the aim of the system is. The suitability of an AI tool is determined by the knowable facts of the pathology subfield, by the match with its knowledge structure and by its requirements. While the success of an AI tool will partly depend on an appropriate definition of its scope, the appropriate combinatoric also depends on the expertise of the user.
NEWCHEM, an artificial intelligence system for the control of cancer cell growth, is described. This system takes into account the most recent advances in molecular and cellular biology and in cell-drug interaction, and aims to develop optimal strategies for the selective control of cancer cell through qualitative reasoning from first principles at cellular level.
This article presents a further description on the background, significance, and structure characteristics of Quantitative Medicine Simulation and Operation by Computer (QMSOC). Also some basic operators were recommended for calculations of biomedical events such as estimation of substance concentrations, exploration of etiology, evaluation of biomedical effects, etc. At last some differences of QMSOC from other artificial intelligent systems in the medical field were discussed.
Two procedures for predicting the carcinogenicity of chemicals are described. One of these (CASE) is a self-learning artificial intelligence system that automatically recognizes activating and/or deactivating structural subunits of candidate chemicals and uses this to determine the probability that the test chemical is or is not a carcinogen. If the chemical is predicted to be carcinogen, CASE also projects its probable potency. The second procedure (CPBS) uses Bayesian decision theory to predict the potential carcinogenicity of chemicals based upon the results of batteries of short-term assays. CPBS is useful even if the test results are mixed (i.e. both positive and negative responses are obtained in different genotoxic assays). CPBS can also be used to identify highly predictive as well as cost-effective batteries of assays. For illustrative purposes the ability of CASE and CPBS to predict the carcinogenicity of a carcinogenic and a non-carcinogenic polycyclic aromatic hydrocarbon is shown. The potential for using the two methods in tandem to increase reliability and decrease cost is presented.
Recently Goldring et al. [Mutation Res., 187 (1987) 67-77] reported the synthesis and purification of a series of nitro-substituted cyclopenta-fused polycyclic aromatic hydrocarbons. On the basis of expected charge distributions, these chemicals were predicted to be potent mutagens and, yet, contrary to expectation, they were found to be only weakly mutagenic for Salmonella. In their discussion, the authors suggest that application of CASE, an artificial intelligence system recently developed in these laboratories, would also not predict the low mutagenicity of this group of chemicals. In the present report, it is shown that CASE, in fact, correctly predicts the low mutagenicity of nitro-substituted cyclopenta-fused polycyclic aromatic hydrocarbons.
Devices constructed for potential use as invasive bioprobes incorporate a selective receiving site for molecular or ionic recognition, and a transducer which is capable of translating a perturbation of physical chemistry of the determinant-site reaction (interaction) into a usable signal. Four types are envisioned--implants for general hospital use, transient-use probes to replace classical blood tests, short-term implantable probes and the long-term variety. Performance criteria are selectivity, sensitivity, fast response, site-reversible, small, rugged, inexpensive, biocompatible, calibratible, facile use by non-expert personnel and ease of telemetry. These demands, not surprisingly, create enormous challenges to the sensor specialist. With respect to biocompatibility the sensor must not be involved in infection, clot formation or antigenic response, and, furthermore, protein adsorption, etc., which can affect the sensor response should be avoided. Calibration remains a problem of monumental proportions. Many devices drift from calibrated levels even in in vitro experiments, let alone in the implanted milieu. One solution has been to carry out on-line switching between patient blood and standard solutions. However, this type of approach leaves a lot to be desired with respect to portability. Another method which is attracting increasing attention is the chemometric or artificial intelligence system involving compensation by multi-sensor array configurations. Sensitivity and limit-of-detection have attracted little research due to the overwhelming nature of other difficulties. In the present paper we evaluate a number of these technical problems and discuss the architecture of devices that are currently available. Finally, some thoughts as to priorities for re-directing sensor research in the bioprobe area are presented.
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 = 364 733), 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 < .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.
There was agreement between the experimental results, obtained in the course of the Second UKEMS Collaborative Study, for the mutagenicity in Salmonella typhimurium of benzidine, 4,4"-diaminoterphenyl, 4-dimethylaminoazobenzene and 4-cyanodimethylaniline and the mutagenicity predicted by CASE (Computer Automated Structure Evaluation), a recently developed artificial intelligence system.
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.
An artificial intelligence system, NEWCHEM, for the development of new oncology therapies is described. This system takes into account the most recent advances in molecular and cellular biology and in cell-drug interaction, and aims to guide experimentation in the design of new optimal protocols. Further work is being carried out, aimed to embody in the system all the basic knowledge of biology, physiopathology and pharmacology, to reason qualitatively from first principles so as to be able to suggest cancer therapies.
Computers assist oncology nurses in their roles as "care integrators" and caregivers. Computers assist nurses in their care integrator role by supporting communication with ancillary departments and by aiding in the collection, organization, and storage of data. Computers helps nurses in their role as caregivers through automated care planning, discharge planning, and patient monitoring and by tracking patients' educational, therapeutic, comfort, or other needs. Using computers, nurses can document their assessments and interventions and patient outcomes while receiving cues and reminders about policies, procedures, and standards of care. In the future, oncology nurses can expect to see computer technology in more hospitals and a host of new developments, such as more intelligent systems, nursing and medical knowledge on-line, documentation at the bedside, and use of patient data bases in education and research.
Whilst endoscopic surgical procedures are getting increasingly more complex, in the various surgical disciplines mono- and bifunctional instruments are only slowly being replaced by multifunctional ones. Therefore a complex, intelligent system was developed, the central part of which is a multifunctional instrument. All basic functions necessary for surgical laparoscopy are integrated and comprise: cutting electrodes (unipolar and bipolar) which can be advanced or retracted pneumatically; coagulation forceps with mechanical control; and irrigation and suction devices. All 5 mm instruments can be used and there is an option for others, such as laser or aqua-dissection. The various functions are controlled via the handle of the multifunctional instrument which is connected to the electronic control unit, the MULTILAP system, which supplies the energy, material, and information flow required. In vivo standardised experiments in pigs were first performed to test the new instrument. Operation time was reduced by more than 20% when compared with the same procedure performed conventionally, during which frequent changing of instruments was necessary. Clinical application, without complications in all 30 patients (uterus preserving procedures or reconstructive tubo-ovarian surgery) confirmed the advantages of a multifunctional device, with optimised cutting and coagulation of vessels more than 1-2 mm in diameter, and reduced duration of operation. Safety and ergonomics were improved. Thus, an electronically controlled instrument with multifunctional working channels for lasers, ultrasound appliances, or mechanical instruments is available for application in all domains of operative laparoscopy.