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PubMed1997

[Signal processing in electromyography: a review].

Automatic analysis of electromyography (EMG), signals, first operated in 1950 with analogic machines, steeply expanded from 1980 when fast computers and worthwhile programs became available. On-line measurement of response area and latency, averaging of low amplitude waves, fast sorting of motor unit potential shape parameters, computation of the "jitter" between two muscle fibers, turns/amplitude and spectral analysis of interferential pattern records, are some examples of programs currently offered in modern EMG machines. Other techniques are still reserved for research purposes: scanning EMG, decomposition of nerve and muscle compound potentials, measurement of the threshold and firing rate of motor units, trace analysis using tracking models. Finally, the credit for artificial intelligence systems (knowledge based systems, fuzzy logic, neuronal networks) is still not clearly stated.

Artifacts↗

PubMed2026

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↗

PubMed2026

Application of emerging technologies in the antiviral field.

Viral diseases pose a serious threat to global public health, agriculture, and biosecurity. Conventional antiviral strategies are often limited by an incomplete understanding of disease mechanisms, poor targeting precision, and slow response times. Emerging technologies are now reshaping the landscape of antiviral research. This review examines the roles of four key frontiers, including organoid models, gene editing, AI-driven molecular design, and synthetic biology. Organoids provide physiologically relevant platforms that model virus-host interactions and disease progression. Viral infections remain a major challenge to human and animal health, agriculture, and biosecurity. Progress in antiviral research is constrained by the complexity of viral pathogenesis, the diversity and rapid evolution of viruses, and the limited translational relevance of some traditional model systems. Recent advances in organoid technology, gene editing, artificial intelligence, and synthetic biology are expanding the toolkit available for antiviral research and development. In this review, we discuss how these four technological frontiers contribute to disease modeling, target discovery, molecular design, and translational innovation. Organoids, in particular, provide physiologically relevant systems for investigating viral infection, tissue tropism, host responses, and pathogenesis. Gene editing tools, such as CRISPR, enable precise manipulation of host and viral genomes, facilitating the development of resistant organisms and next-generation vaccine platforms. AI technologies, including AlphaFold for structure prediction and platforms for de novo protein design, address long-standing bottlenecks in structural biology and offer powerful means to engineer antiviral proteins, antibodies, and vaccine antigens. Synthetic biology, guided by the Design-Build-Test-Learn cycle, integrates computational design, genetic assembly, and functional validation into a cohesive pipeline. Together, these technologies form a synergistic workflow that spans disease modeling, target discovery, molecular design, construction, testing, and iterative optimization. This integrated approach is shifting antiviral development from traditional empirical methods toward more precise, intelligent strategies. The review also highlights ongoing challenges in integration and scalability, stressing that high-quality biological datasets and stronger interdisciplinary collaboration are essential for realizing translational potential. By presenting a cohesive view of these converging methodologies, this review offers a framework to guide the intelligent evolution of antiviral strategies in both human and animal health.

Antiviral↗

PubMed1999

Laparoscopy in the new century.

Minimally invasive surgery already has established itself as a useful tool in the management of trauma. The future holds exciting possibilities for this field, borne and fostered by innovative developments in imaging, computer technology, and artificial intelligence. The next millennium may witness the disappearance of trauma surgery as it is known today.

Artificial Intelligence↗

PubMed1990

Artificial neural networks and their use in quantitative pathology.

A brief general introduction to artificial neural networks is presented, examining in detail the structure and operation of a prototype net developed for the solution of a simple pattern recognition problem in quantitative pathology. The process by which a neural network learns through example and gradually embodies its knowledge as a distributed representation is discussed, using this example. The application of neurocomputer technology to problems in quantitative pathology is explored, using real-world and illustrative examples. Included are examples of the use of artificial neural networks for pattern recognition, database analysis and machine vision. In the context of these examples, characteristics of neural nets, such as their ability to tolerate ambiguous, noisy and spurious data and spontaneously generalize from known examples to handle unfamiliar cases, are examined. Finally, the strengths and deficiencies of a connectionist approach are compared to those of traditional symbolic expert system methodology. It is concluded that artificial neural networks, used in conjunction with other nonalgorithmic artificial intelligence techniques and traditional algorithmic processing, may provide useful software engineering tools for the development of systems in quantitative pathology.

Artificial Intelligence↗

PubMed2026

Use of synthetic data, a novel paradigm for immunopathology.

The complexity and heterogeneity of autoimmune diseases are only partially captured by current analytic tools, even when deep learning techniques are employed to intercept patterns beyond existing dogma. Synthetic data offer a newer paradigm through machine-generated reconstructions of real-world data that faithfully attempt to recapitulate biological and clinical patterns without creating duplicates and maintaining the privacy of the original ones. Synthetic data act as a magnifying lens, allowing predictions otherwise not possible on disease classification, progression, and therapeutic response. This approach has several advantages and is currently underutilized. Firstly, it provides cohort enrichment and equilibrates group imbalances. Second, it generates synthetic arms for both in vitro studies and human clinical trials, relevant to disentangle the rarity and heterogeneity of autoimmune diseases. Third, the platform allows applications beyond tabular registries, including medical images, genomics, and flow cytometry data. Last, 'digital twins' act through dynamic bidirectional links with the biological/clinical system counterpart, lending themselves to transformative opportunities for precision medicine. Herein, we discuss the current status of this fast-moving novel component of artificial intelligence and its implications for autoimmune diseases.

Humans↗

PubMedDate not supplied

A blackboard architecture for automating cephalometric analysis.

This paper describes a principled attempt to use artificial intelligence methodologies for interpretation of lateral skull X-ray images. Lateral skull X-ray images are routinely used in cephalometric analysis to provide quantitative measurements useful to clinical orthodontists. Manual and interactive methods of analysis are known to be error-prone, and time-consuming. Previous attempts have been made to automate this analysis, using conventional algorithmic approaches. Unfortunately such systems typically fail to capture the expertise and adaptability required to cope with the variability in biological structure and X-ray image quality found in cephalograms. The present system makes use of a blackboard architecture and multiple knowledge sources within an integrated model-based system. A data-gathering system allows models of feature appearance and location to be built from examples. Blackboard and task control modules allow specific knowledge-based modules to act on information available to the blackboard. Knowledge-based modules include location hypothesis, intelligent segmentation, and constraint propagation systems. Results from a working experimental system are given, and compare favourably with previous algorithmic solutions.

Artificial Intelligence↗

PubMed2005

An intelligent tutoring system for trauma management (Trauma-Teach): a preliminary report.

Trauma-Teach is an interactive software for tutoring surgical trainees on medical trauma management procedures. Users of the system interact with a virtual patient suffering from trauma injuries. The task of the user is to stabilise the virtual patient, discover the underlying injuries and decide on an appropriate management plan. Artificial intelligence techniques are used to simulate the patient's pulmonary and cardiovascular systems in real time, determine the responses and results of treatments and diagnostics accordingly, model the patient deterioration if wrong actions are taken, and give a measure of reality to the system by selecting actual trauma cases from the hospital's database.

Artificial Intelligence↗

PubMed1989

Software engineering education in medical informatics.

Requirements and approaches of Software Engineering education in the field of Medical Informatics are described with respect to the impact of (1) experiences characterizing the "software misery", (2) status and tendencies in software methodology, and (3) educational status and needs in computer science education influenced by the controversy "theoretical versus practical education". Special attention is directed toward the growing importance of analysis, design methods, and techniques in the professional spectrum of Medical Informatics, the relevance of general principles of systems engineering in health care, the potential of non-procedural programming paradigms, and the intersection of Artificial Intelligence and education. Realizations of and experiences with programs in the field of Software Engineering are reported with respect to special requirements in Medical Informatics.

Artificial Intelligence↗

PubMed2001

Intelligent systems in the context of surrounding environment.

We investigate the behavioral patterns of a population of agents, each controlled by a simple biologically motivated neural network model, when they are set in competition against each other in the minority model of Challet and Zhang. We explore the effects of changing agent characteristics, demonstrating that crowding behavior takes place among agents of similar memory, and show how this allows unique "rogue" agents with higher memory values to take advantage of a majority population. We also show that agents' analytic capability is largely determined by the size of the intermediary layer of neurons. In the context of these results, we discuss the general nature of natural and artificial intelligence systems, and suggest intelligence only exists in the context of the surrounding environment (embodiment).

Artificial Intelligence↗

PubMed2004

Intelligent software for laboratory automation.

The automation of laboratory techniques has greatly increased the number of experiments that can be carried out in the chemical and biological sciences. Until recently, this automation has focused primarily on improving hardware. Here we argue that future advances will concentrate on intelligent software to integrate physical experimentation and results analysis with hypothesis formulation and experiment planning. To illustrate our thesis, we describe the 'Robot Scientist' - the first physically implemented example of such a closed loop system. In the Robot Scientist, experimentation is performed by a laboratory robot, hypotheses concerning the results are generated by machine learning and experiments are allocated and selected by a combination of techniques derived from artificial intelligence research. The performance of the Robot Scientist has been evaluated by a rediscovery task based on yeast functional genomics. The Robot Scientist is proof that the integration of programmable laboratory hardware and intelligent software can be used to develop increasingly automated laboratories.

Algorithms↗

PubMed1996

Research takes on a second life. Interview by John McCornack.

The health care industry is embracing the artificial intelligence concepts that Edward H. Shortliffe, M.D., developed in the 1970s. His current work on Web-based information dissemination may yield applications for the next century.

Artificial Intelligence↗

PubMed1990

Eliciting principles of hazard identification from experts.

National experts in the field of developmental toxicology were interviewed in order to elicit the principles, or rules-of-thumb, they use in determining if a compound or agent is likely to be a developmental hazard during pregnancy. Several levels of individual and cumulative consensus activity were carried out that resulted in consensus in 71 rules and partial consensus in an additional 24 rules of 145 rules initially elicited. Rules could be divided generically into those affecting the expert's confidence in a piece of scientific evidence and those determining the weight of importance of that evidence in deciding about hazard identification. Topically, the rules also divided into those about the general nature or characteristics of a compound, animal studies testing for an effect of the compound, and human reports about the presence of absence of developmental effects associated with the compound. Several conclusions about the methodology include the following: 1) expert systems must be based on the knowledge of more than one expert; 2) considerable human effort is expended in evaluating the certainty of scientific evidence before combining the evidence for problem solving; 3) how experts use evidence of different degrees of uncertainty in their decisions is a major area that is yet to be determined and that may greatly affect subsequent efforts in artificial intelligence; and 4) knowledge elicitation by interview has limitations but is a workable methodology for medical decision making.

Animal Testing Alternatives↗

PubMed1999

Prognostic methods in medicine.

Prognosis--the prediction of the course and outcome of disease processes--plays an important role in patient management tasks like diagnosis and treatment planning. As a result, prognostic models form an integral part of a number of systems supporting these tasks. Furthermore, prognostic models constitute instruments to evaluate the quality of health care and the consequences of health care policies by comparing predictions according to care norms with actual results. Approaches to developing prognostic models vary from using traditional probabilistic techniques, originating from the field of statistics, to more qualitative and model-based techniques, originating from the field of artificial intelligence (AI). In this paper, various approaches to constructing prognostic models, with emphasis on methods from the field of AI, are described and compared.

Artificial Intelligence↗

PubMed2001

An intelligent tele-healthcare environment offering person-centric and wellness-maintenance services.

Worldwide healthcare delivery trends are undergoing a subtle paradigm shift--patient centered services as opposed to provider centered services and wellness maintenance as opposed to illness management. In this paper we present a Tele-Healthcare project TIDE--Tele-Healthcare Information and Diagnostic Environment. TIDE manifests an 'intelligent' healthcare environment that aims to ensure lifelong coverage of person-specific health maintenance decision-support services--i.e., both wellness maintenance and illness management services--ubiquitously available via the Internet/WWW. Taking on an all-encompassing health maintenance role--spanning from wellness to illness issues--the functionality of TIDE involves the generation and delivery of (a) Personalized, Pro-active, Persistent, Perpetual, and Present wellness maintenance services, and (b) remote diagnostic services for managing noncritical illnesses. Technically, TIDE is an amalgamation of diverse computer technologies--Artificial Intelligence, Internet, Multimedia, Databases, and Medical Informatics--to implement a sophisticated healthcare delivery infostructure.

Artificial Intelligence↗

PubMed2026

Next-generation brain proteomics: Integrating single-cell, spatial, and multi-omics for clinical biomarker discovery.

The mammalian brain's functional complexity arises from the sophisticated architecture of neurons and glia. This network is essentially defined by its dynamic proteome, which reveals the functional execution underlying neural computation and disease. This review integrates the technological leap in neuroproteomics. It has moved beyond bulk tissue proteome cataloguing to high-sensitivity single-cell and spatial resolution. We detail how next-generation platforms, such as TIMS-PASEF and Orbitrap-Astral, have enabled deeper and faster phenotypic profiling of limited brain samples. However, the proteome coverage remains constrained by dynamic range, sample loss, ionisation bias and incomplete detection of low-abundance regulatory proteins. We further examine how such studies have revealed the proteomic remodelling that drives lineage specification and synaptic plasticity by linking temporal protein expression waves to biological function. Crucially, we delineate the clinical translational trajectory, illustrating how aberrant signatures are verified in cerebrospinal fluid (CSF) and validated in plasma to support precision medicine. Finally, we argue for the necessity of "fused" multi-omics integration and Artificial Intelligence (AI) to decode the non-linear molecular logic of brain pathology.

Humans↗

PubMed2003

Bioinformatics goes back to the future.

The need to turn raw data into knowledge has led the bioinformatics field to focus increasingly on the manipulation of information. By drawing parallels with both cryptography and artificial intelligence, we can develop an understanding of the changes that are occurring in bioinformatics, and how these changes are likely to influence the bioinformatics job market.

Artificial Intelligence↗

PubMedDate not supplied

Intelligent dialogue based on statistical models of clinical decision-making.

The independence Bayesian model has been used widely in computer programs designed to support clinical decision-making. A reasoning strategy has been developed to enable these programs to conduct clinically pertinent dialogue and explain their reasoning. It has been implemented in a program for the diagnosis of acute abdominal pain based on the Bayesian model of de Dombal et al. Several features of the dialogue design have been adopted from artificial intelligence research, including shared initiative and critiquing. The program adopts a flexible goal-driven strategy, attempting to confirm the clinician's diagnosis or rule out the likeliest alternative. Symptoms and signs are selected in order of their expected weights of evidence in favour of the hypothesized disease.

Abdomen↗