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PubMed1992

[Artificial intelligence methods for support of medial patient education before surgical interventions in the region of the neck-nose-ear].

As a rule, curative operations require the patient's consent. Determined by the expansion of surgical possibilities, the kind and frequency of specific complications are subjected to constant changes. The physician is encouraged to explain therapeutic methods as well as the probability of complications within the patient's grasp. It has been investigated to what extent methods of artificial intelligence (AI) are suited for assisting the physician in this task. For this purpose, a comprehensive list of surgical complications as reported in research literature has been compiled. The list has been transferred into a hierarchical structure which can be depicted as a rule tree classified according to topographic aspects. In each otolaryngological operation, the reported complications can be classed with these rules. By employing an expert system (Fig. 1), the physician is capable of compiling an individualized document of agreement (Fig. 2) which serves as a basis for the explanatory talk with the patient.

Artificial Intelligence↗

PubMedDate not supplied

A microcomputer teaching and decision-support system for emergency medicine: use of hypermedia and artificial intelligence in combination.

Hospital emergency units are submitted to a continuous intensive and polyvalent practice of medicine. In addition to the few experienced physicians, the medical staff is often made up of young and unskilled students and residents. The ability to reach at any time a wide and flexible knowledge is of the utmost importance to improve the quality of care given to patients and to perfect bedside teaching. The purpose of this work was to present a computerized system, a kind of shell, using, in combination, artificial intelligence and hypertext/hypermedia tools. A modular architecture is presented integrating two entities: an illustrated encyclopedic hypertext network and several expert modules based on production rules concerning well-limited fields of medicine (basic clinical problem-solving, metabolic and acid-base disorders). An interface using the World Wide Web (WWW) will soon be proposed.

Artificial Intelligence↗

PubMed2006

W. Grey Walter, pioneer in the electroencephalogram, robotics, cybernetics, artificial intelligence.

With the announcement by William Lennox at the 1935 London International Neurology Congress of the use of electroencephalography in the study of epilepsy, it became evident that a new and powerful technique for the investigation of seizures had been discovered. William Grey Walter, a young researcher finishing his post-graduate studies at Cambridge, was selected to construct and study the EEG in clinical neurology at the Maudsley Hospital, London. His hugely productive pioneering career in the use of EEG would eventually lead to groundbreaking work in other fields --the emerging sciences of robotics, cybernetics, and early work in artificial intelligence. In this historical note his pioneering work in the fields of clinical neurophysiology is documented, both in the areas of epileptology and tumour detection. His landmark contributions to clinical neurophysiology are worthy of documentation.

Artificial Intelligence↗

PubMed2026

Decoding the spatiotemporal patterns of food spoilage microbial communities: Integrating multi-omics and artificial intelligence to enable precision preservation.

In the global food supply chain, food wastage caused by spoilage has resulted in significant economic losses, food shortages, and environmental pressure. This process is fundamentally driven by the spatiotemporal dynamics of microbial communities. However, traditional research methods struggle to elucidate the complex mechanisms of spatial heterogeneity, interspecies interactions, and functional succession. This limits the development of effective preservation strategies. This review systematically reviews the cutting-edge progress of integrating multi-omics technologies and artificial intelligence (AI) to study food spoilage microbial communities, breaking through this bottleneck. We propose an intelligent theoretical framework that could potentially analyze microbial metabolic activities and predict dynamic shelf life if implemented. The conceptual framework integrates multidimensional data, including spatial metabolomics, temporal metatranscriptomics, single-cell transcriptomics, and longitudinal metagenomics. It can also be combined with AI models, such as graph neural networks. The article elaborates on the principles and applications of spatio-temporal monitoring technologies, such as nano secondary ion mass spectrometry, hyperspectral imaging, and the Internet of Things sensing. Through illustrative cases of typical perishable foods, it also explores how such a multi-omics - AI system might be applied to spoilage warning and precise intervention. Additionally, the article addresses the current challenges in data coverage, model generalization, and federated learning implementation. Then the research further explores emerging areas such as engineered probiotics, edge AI, and microfluidic sensing. These areas are targeted at transforming food preservation from an empirical control approach to a data-driven, precise regulatory framework. This transformation provides theoretical support and technical approaches for developing a smart, sustainable food preservation system.

Multiomics↗

PubMed2004

Estimation of some transducer parameters in a broadband piezoelectric transmitter by using an artificial intelligence technique.

An estimation procedure to efficiently find approximate values of internal parameters in ultrasonic transducers intended for broadband operation would be a valuable tool to discover internal construction data. This information is necessary in the modelling and simulation of acoustic and electrical behaviour related to ultrasonic systems containing commercial transducers. There is not a general solution for this generic problem of parameter estimation in the case of broadband piezoelectric probes. In this paper, this general problem is briefly analysed for broadband conditions. The viability of application in this field of an artificial intelligence technique supported on the modelling of the transducer internal components is studied. A genetic algorithm (GA) procedure is presented and applied to the estimation of different parameters, related to two transducers which are working as pulsed transmitters. The efficiency of this GA technique is studied, considering the influence of the number and variation range of the estimated parameters. Estimation results are experimentally ratified.

Algorithms↗

PubMed1990

Application of artificial intelligence techniques to a well defined clinical problem: jaundice diagnosis.

Jaundice is a very common medical condition, in which pathophysiological knowledge has been quite well assessed and clinical features are usually well known. However, incorrect conclusions are sometimes reached in medical practice which can lead to serious implications. Thus, jaundice diagnosis appears as one of the medical situations which might be substantially improved by computer assistance. The present study is aimed at describing and discussing in what way a computer program supporting medical decision making in jaundiced patients can be developed on the basis of advanced Artificial Intelligence methods. To this extent methodological problems concerned with the organization and the formalization of medical knowledge have been outlined with some detail. The resulting expert system is expected to become a well assessed and potentially useful tool for both medical decision making and medical education.

Algorithms↗

PubMed2026

Artificial Intelligence for Natural Products Discovery and Development.

Natural products (NPs) remain a cornerstone of modern drug discovery, offering stereochemical complexity and diverse bioactivities that precisely modulate therapeutic targets, refined through billions of years of evolution. However, their research has long been hindered by inefficient, empirical workflows, high resource consumption, structural complexity, and the "multicomponent, multi-target" nature of their mechanisms. The exponential growth of genomic, metabolomic, and spectral data has overwhelmed conventional analytical methods, exposing critical bottlenecks in handling high-dimensional, heterogeneous datasets that exceed human interpretive capacity. Artificial intelligence (AI) is emerging as a transformative paradigm to address these challenges, integrating multi-omics and chemical data to shift NP research from fragmented empiricism toward mechanism-driven, precision-oriented development. By leveraging deep learning architectures- including graph neural networks, Transformers, and diffusion-based generative models-AI enables systematic decoding of NP biosynthesis, automated structure elucidation, rational target identification, knowledge extraction from vast unstructured scientific literature, and de novo molecular design. This review comprehensively surveys recent advances in AI applications across the full NP discovery and development pipeline, encompassing genome mining, structure-based and ligand-based virtual screening, multimodal structural characterization, lead optimization, and biosynthetic pathway engineering. We further examine the emerging roles of protein-centric, molecule- centric, and multimodal foundation models, as well as large language models, in bridging genotype-to-chemotype gaps and unlocking unstructured scientific knowledge. Finally, we discuss critical challenges including data scarcity, representational limitations for complex stereochemistry, physical plausibility in generative models, and the urgent need for experimental validation, while outlining future directions toward autonomous experimentation, closed-loop optimization, and human-AI collaborative discovery.

Artificial intelligence↗

PubMed1998

An artificial intelligence system for computer-assisted menu planning.

Planning nutritious and appetizing menus is a complex task that researchers have tried to computerize since the early 1960s. We have attempted to facilitate computer-assisted menu planning by modeling the reasoning an expert dietitian uses to plan menus. Two independent expert systems were built, each designed to plan a daily menu meeting the nutrition needs and personal preferences of an individual client. One system modeled rule-based, or logical, reasoning, whereas the other modeled case-based, or experiential, reasoning. The 2 systems were evaluated and their strengths and weaknesses identified. A hybrid system was built, combining the best of both systems. The hybrid system represents an important step forward because it plans daily menus in accordance with a person's needs and preferences; the Reference Daily Intakes; the Dietary Guidelines for Americans; and accepted aesthetic standards for color, texture, temperature, taste, and variety. Additional work to expand the system's scope and to enhance the user interface will be needed to make it a practical tool. Our system framework could be applied to special-purpose menu planning for patients in medical settings or adapted for institutional use. We conclude that an artificial intelligence approach has practical use for computer-assisted menu planning.

Artificial Intelligence↗

PubMed2005

Humans can consciously generate random number sequences: a possible test for artificial intelligence.

Computer algorithms can only produce seemingly random or pseudorandom numbers whereas certain natural phenomena, such as the decay of radioactive particles, can be utilized to produce truly random numbers. In this study, the ability of humans to generate random numbers was tested in healthy adults. Subjects were simply asked to generate and dictate random numbers. Generated numbers were tested for uniformity, independence and information density. The results suggest that humans can generate random numbers that are uniformly distributed, independent of one another and unpredictable. If humans can generate sequences of random numbers then neural networks or forms of artificial intelligence, which are purported to function in ways essentially the same as the human brain, should also be able to generate sequences of random numbers. Elucidating the precise mechanism by which humans generate random number sequences and the underlying neural substrates may have implications in the cognitive science of decision-making. It is possible that humans use their random-generating neural machinery to make difficult decisions in which all expected outcomes are similar. It is also possible that certain people, perhaps those with neurological or psychiatric impairments, are less able or unable to generate random numbers. If the random-generating neural machinery is employed in decision making its impairment would have profound implications in matters of agency and free will.

Adult↗

PubMed2001

Processing and representation of meta-data for sleep apnea diagnosis with an artificial intelligence approach.

In this article, we revise and try to resolve some of the problems inherent in questionnaire screening of sleep apnea cases and apnea diagnosis based on attributes which are relevant and reliable. We present a way of learning information about the relevance of the data, comparing this with the definition of the information by the medical expert. We generate a predictive data model using a data aggregation operator which takes relevance and reliability information about the data into account to produce a diagnosis for each case. We also introduce a grade of membership for each question response which allows the patient to indicate a level of confidence or doubt in their own judgement. The method is tested with data collected from patients in a Sleep Clinic using questionnaires specially designed for the study. Other artificial intelligence predictive modeling algorithms are also tested on the same data and their predictive accuracy compared to that of the aggregation operator.

Adult↗

PubMed1998

Galen-In-Use: using artificial intelligence terminology tools to improve the linguistic coherence of a national coding system for surgical procedures.

GALEN has developed a language independent common reference model based on a medically oriented ontology and practical tools and techniques for managing healthcare terminology including natural language processing. GALEN-IN-USE is the current phase which applied the modelling and the tools to the development or the updating of coding systems for surgical procedures in different national coding centers co-operating within the European Federation of Coding Centre (EFCC) to create a language independent knowledge repository for multicultural Europe. We used an integrated set of artificial intelligence terminology tools named CLAssification Manager workbench to process French professional medical language rubrics into intermediate dissections and to the Grail reference ontology model representation. From this language independent concept model representation we generate controlled French natural language. The French national coding centre is then able to retrieve the initial professional rubrics with different categories of concepts, to compare the professional language proposed by expert clinicians to the French generated controlled vocabulary and to finalize the linguistic labels of the coding system in relation with the meanings of the conceptual system structure.

Artificial Intelligence↗

PubMedDate not supplied

Clinical pictures of unknown origin in neurology: past, present and future usefulness of artificial intelligence.

Although, in the course of the last 50 years, the achievements in the medical field have been astonishing, at the beginning of the third millennium a number of clinical pictures are still left without a precise nosographic origin. In the past, the delay in scientific communication was the main explanation presented for the lack of understanding of clinical pictures of unknown nosographic origin. The history of medicine provides excellent examples of this dispersion of human capital, even if the history of clinical neurology presents "exceptions" (the pictures that we now call de la Tourette's syndrome and Parkinson's disease) that indicate that major clinical syndromes could be clearly detected and relatively rapidly diffused even in the 19th century. Contrary to the past, the delay in scientific communication no longer seems an obstacle to the sharing of medical knowledge. Nevertheless, the problem of the in-depth comprehension of clinical pictures of unknown nosographic origin still remains dominant, mainly because of the limited spread of ample and flexible online accessible databases of unknown nosographic origin clinical syndromes. The need for interactive electronic archives and other artificial intelligence resources in order to promote progress in clinical knowledge is discussed in this paper.

Artificial Intelligence↗

PubMedDate not supplied

Choice and explanation in medical management: a multiattribute model of artificial intelligence approaches.

This paper explores a model of choice and explanation in medical management and makes clear its advantages and limitations. The model is based on multiattribute decision making (MADM) and consists of four distinct strategies for choice and explanation, plus combinations of these four. Each strategy is a restricted form of the general MADM approach, and each makes restrictive assumptions about the nature of the domain. The advantage of tailoring a restricted form of a general technique to a particular domain is that such efforts may better capture the character of the domain and allow choice and explanation to be more naturally modelled. The uses of the strategies for both choice and explanation are illustrated with analyses of several existing medical management artificial intelligence (AI) systems, and also with examples from the management of primary breast cancer. Using the model it is possible to identify common underlying features of these AI systems, since each employs portions of this model in different ways. Thus the model enables better understanding and characterization of the seemingly ad hoc decision making of previous systems.

Algorithms↗

PubMed2026

Cancer of unknown primary: the evolution of tissue of origin identification in the artificial intelligence era.

Cancer of Unknown Primary (CUP) presents substantial diagnostic and therapeutic challenges owing to its heterogeneous nature and the absence of an identifiable primary tumor site. This review provides a structured search of the pathogenesis, epidemiological characteristics, and limitations of traditional diagnostic and therapeutic approaches for CUP, with an emphasis on the evolution of Tissue of Origin (TOO) identification techniques. Recent advances in precision medicine have accelerated the development of machine learning-based TOO identification tools, representing a paradigm shift in CUP diagnostics. Deep learning (DL) algorithms that integrate multi-omics data (such as genomics and transcriptomics) with clinical features have markedly enhanced the accuracy of tracing tumor origin, and artificial intelligence (AI) driven TOO models are increasingly being incorporated into clinical practice, offering new insights for pathological diagnosis, treatment selection, and prognostic evaluation. Nevertheless, several challenges remain, including issues of data standardization, model generalizability, and interpretability. Ethical considerations related to data privacy, algorithmic fairness, and clinical implementation also warrant careful attention. Future research should focus on establishing standardized multi-center databases, developing more interpretable AI models, and fostering multidisciplinary collaborative strategies for CUP management. Through continued refinement of technical solutions and regulatory guidelines, TOO identification is anticipated to progress from research to routine clinical application, ultimately supporting precise and personalized care for patients with CUP.

Artificial intelligence↗

PubMed2005

Prospects of second generation artificial intelligence tools in calibration of chemical sensors.

Multivariate data driven calibration models with neural networks (NNs) are developed for binary (Cu++ and Ca++) and quaternary (K+, Ca++, NO3- and Cl-) ion-selective electrode (ISE) data. The response profiles of ISEs with concentrations are non-linear and sub-Nernstian. This task represents function approximation of multi-variate, multi-response, correlated, non-linear data with unknown noise structure i.e. multi-component calibration/prediction in chemometric parlance. Radial distribution function (RBF) and Fuzzy-ARTMAP-NN models implemented in the software packages, TRAJAN and Professional II, are employed for the calibration. The optimum NN models reported are based on residuals in concentration space. Being a data driven information technology, NN does not require a model, prior- or posterior- distribution of data or noise structure. Missing information, spikes or newer trends in different concentration ranges can be modeled through novelty detection. Two simulated data sets generated from mathematical functions are modeled as a function of number of data points and network parameters like number of neurons and nearest neighbors. The success of RBF and Fuzzy-ARTMAP-NNs to develop adequate calibration models for experimental data and function approximation models for more complex simulated data sets ensures AI2 (artificial intelligence, 2nd generation) as a promising technology in quantitation.

Artificial Intelligence↗

PubMed1992

Hemodynamic and oxygen transport patterns for outcome prediction, therapeutic goals, and clinical algorithms to improve outcome. Feasibility of artificial intelligence to customize algorithms.

A generalized decision tree or clinical algorithm for treatment of high-risk elective surgical patients was developed from a physiologic model based on empirical data. First, a large data bank was used to do the following: (1) describe temporal hemodynamic and oxygen transport patterns that interrelate cardiac, pulmonary, and tissue perfusion functions in survivors and nonsurvivors; (2) define optimal therapeutic goals based on the supranormal oxygen transport values of high-risk postoperative survivors; (3) compare the relative effectiveness of alternative therapies in a wide variety of clinical and physiologic conditions; and (4) to develop criteria for titration of therapy to the endpoints of the supranormal optimal goals using cardiac index (CI), oxygen delivery (DO2), and oxygen consumption (VO2) as proxy outcome measures. Second, a general purpose algorithm was generated from these data and tested in preoperatively randomized clinical trials of high-risk surgical patients. Improved outcome was demonstrated with this generalized algorithm. The concept that the supranormal values represent compensations that have survival value has been corroborated by several other groups. We now propose a unique approach to refine the generalized algorithm to develop customized algorithms and individualized decision analysis for each patient's unique problems. The present article describes a preliminary evaluation of the feasibility of artificial intelligence techniques to accomplish individualized algorithms that may further improve patient care and outcome.

Algorithms↗

PubMed2005

Designed strength identification of concrete by ultrasonic signal processing based on artificial intelligence techniques.

This paper presents a pattern recognition method to identify the designed strength of concrete by evidence accumulation based on artificial intelligence techniques with multiple feature parameters. Concrete specimens in this experiment, which were designed to have the strengths of 180, 210, 240, 300, and 400 kg/cm2, respectively, have been considered. Variance, zero-crossing, mean frequency, autoregressive (AR) model coefficients, and linear cepstrum coefficients are extracted as feature parameters from ultrasonic signals of concretes. Pattern recognition is carried out through the evidence accumulation procedure using the distances measured with reference parameters. A fuzzy mapping function is introduced to transform the distance for the application of the evidence accumulation method. Results are presented to support the feasibility of the suggested approach for concrete pattern identification.

Journal Article↗

PubMed1995

An artificial intelligent diagnostic system with neural networks to determine genetical disorders and fetal health by using maternal serum markers.

OBJECTIVE: To develop an artificial intelligent diagnostic system with neural networks to determine genetical disorders and fetal health problems by using maternal serum markers ('Triple Test') and maternal age. STUDY DESIGN: A total of 112 pregnant women were referred to Fetal Medicine Unit of Hacettepe University Hospital for fetal ultrasonography and chromosome analysis with different indications. All patients underwent genetic amniocentesis or fetal blood sampling under ultrasound guidance. Gross malformations and hydrops fetalis were detected in 15 and 5 fetuses, respectively. We have found chromosomal abnormality in 7 cases. 'Triple Test' is offered to all patients and serum levels of alpha-fetoprotein, human chorionic gonadotropin and unconjugated estriol were analyzed by radioimmunoassay. In this study, we have used supervised artificial neural network structure to develop a diagnostic system. Our system's input parameters are maternal age, gestational age and 'Triple Test' results. Our system consists of two different artificial neural network modules whose decision-making logics are different. One of them is designed to search genetical disorders while the other one is for the assessment of fetal well-being. Confusion matrix is used for statistical evaluation. RESULTS: The discriminatory power of the artificial neural network to search genetical disorders and fetal well-being is found to be highly significant (z = 10.583 and z = 10.424, respectively). CONCLUSION: This system brings objectively to the evaluation of 'Triple Test' results and can be used both for the detection of genetical disorders and fetal well-being. Nevertheless, the analysis program's performance is limited to input information and knowledge and medical expert expert can not get more than he or she has donated the system.

Adolescent↗