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PubMed2004

Normative data for the Boston Naming Test in native Dutch-speaking Belgian children and the relation with intelligence.

This paper reports the results of a normative study of the 60-item version of the Boston Naming Test (BNT) in a group of 371 native Dutch-speaking Flemish children between the ages of 6 and 12 years. Analysis of test results revealed that BNT performance was significantly affected by age and gender. The gathered norms were shown to be significantly lower than published norms for comparable North-American children. Error analysis disclosed remarkable similarities with data from elderly subjects, with verbal semantic paraphasias and 'don't know' responses occurring most frequently. Finally, BNT scores were shown to correlate strongly with general intelligence as measured with the Raven Progressive Matrices. The relation between both measures can be of help in the diagnosis of identification naming deficits and impaired word-retrieval capacities.

Belgium↗

PubMed2005

Cerebral lateralization and general intelligence: gender differences in a transcranial Doppler study.

The present study evaluated cerebral lateralization during Raven's progressive matrices (RPM) paradigm in female and male subjects. Bilateral simultaneous transcranial Doppler (TCD) ultrasound was used to measure mean blood flow velocities (MBFV) in the right and left middle cerebral arteries (MCAs) in 24 (15 females and 9 males) right-handed normal subjects. The female subjects used a left hemisphere strategy, while males used a right hemisphere strategy to successfully solve RPM tasks. This implies that general intelligence is associated with neural systems within one hemisphere that are accessible to a variety of cognitive processes.

Adult↗

PubMed2006

Synergy between Competitive Intelligence (CI), Knowledge Management (KM) and Technological Foresight (TF) as a strategic model of prospecting--the use of biotechnology in the development of drugs against breast cancer.

The aim of this paper is to demonstrate the synergy between Competitive Intelligence, Knowledge Management and Technological Foresight, and to emphasize the proposal of a strategic model of data prospecting as a mechanism to support decision-making in regard to three approaches for sustainable development and innovation: technological, social and economic. The use of biotechnology in the development of drugs against breast cancer is the case study. The article shows the results of data and text mining in specialized medical and patent databases, identifying the most frequently cited drugs, as well as the authors of research, and the inventors of new technology at the beginning of the 21st century. In addition, the study includes reference to Brazilian competence in breast cancer area, the international trends in drugs for treatment of this cancer, leading international institutions and Brazilian competencies. A framework is presented, which could serve as a guide and support for the decision-making process.

Biotechnology↗

PubMed2004

Predicting traumatic stress using emotional intelligence.

The study investigated whether emotional intelligence (EI) can predict how individuals respond to traumatic experiences. A random sample of 414 participants (181 male, 233 female) were administered a measure of EI along with the Impact of Event Scale--revised [IES-R; Weiss, D. S. & Marmar, C. R. (1997). The Impact of Events Scale--revised. In J.P. Wilson & T.M. Keane (Eds.), Assessing psychological trauma and PTSD (pp. 399-411). New York: Guilford Press], and the monitoring and blunting questionnaire [MBQ, Anxiety Stress Coping 7 (1994) 53]. The results showed that participants with higher NEIS scores report fewer psychological symptoms relating to their traumatic experiences, that monitors are more likely to have higher NEIS scores than blunters. Traumatic events had a greater impact on females than males, and males had higher EI than females. The implications of these findings for using EI as a predictor for individuals who may experience traumatic stress are discussed.

Adaptation, Psychological↗

PubMed2005

An intelligent system for automatic detection of gastrointestinal adenomas in video endoscopy.

Today 95% of all gastrointestinal carcinomas are believed to arise from adenomas. The early detection of adenomas could prevent their evolution to cancer. A novel system for the support of the detection of adenomas in gastrointestinal video endoscopy is presented. Unlike other systems, it accepts standard low-resolution video input thus requiring less computational resources and facilitating both portability and the potential to be used in telemedicine applications. It combines intelligent processing techniques of SVMs and color-texture analysis methodologies into a sound pattern recognition framework. Concerning the system's accuracy this was measured using ROC analysis and found to exceed 94%.

Adenoma↗

PubMed2006

Intelligent agents technology applied to tasks scheduling and communications management in a critical care telemonitoring system.

This work presents an important part of our telemedicine system for critical care units: a distributed module based on intelligent agents technology that is dedicated to the process management of a network for medical monitoring, including distribution and control of processing tasks and bandwidth management. The system provides the real-time acquisition and analysis of physiological data, the graphical visualisation of these data, and their transmission to a central system charged with the collection and control of all the information concerning the patient, including knowledge-based systems (KBS) for medical reasoning.

Artificial Intelligence↗

PubMed2026

Artificial Intelligence-Driven Multi-Omics Analysis Reveals Hydroxytyrosol Targeting of the TXNIP-NLRP3 Inflammasome Axis in Traumatic Brain Injury.

Traumatic brain injury (TBI) induces secondary neuroinflammation driven by oxidative stress, inflammasome activation, and immune remodeling, yet specific mechanism-guided pharmacological interventions remain limited. This study established an artificial intelligence (AI)-integrated network pharmacology and multi-omics framework to evaluate whether hydroxytyrosol (HT), an olive-derived natural polyphenol, may regulate TBI-related neuroinflammatory targets centered on the TXNIP/NLRP3 inflammasome axis. Starting from the SMILES structure of HT, potential targets were predicted using PharmMapper, SwissTargetPrediction, and the Similarity Ensemble Approach and were standardized to UniProt identifiers. TBI-associated genes were integrated from GeneCards, DisGeNET, OMIM, and the Therapeutic Target Database. The overlapping target set was analyzed using STRING-based protein-protein interaction (PPI) networks, MCODE, CytoHubba, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. Public GEO transcriptomic datasets (GSE123831 and GSE104687) were used for cross-platform expression validation, differential expression analysis, and exploratory CIBERSORT-based immune infiltration estimation. Random forest (RF), multilayer perceptron (MLP), graph convolutional network (GCN), graph attention network (GAT), SHAP/LIME explainability analysis, LASSO inflammatory-risk scoring, and two-sample Mendelian randomization (MR) were further applied for target prioritization, immune phenotype mapping, and genetic association analysis. Seventy-three overlapping HT-TBI targets were identified. PPI and topology analyses prioritized TXNIP, NLRP3, CASP1, MAPK1, and TP53 as key hubs enriched in inflammasome activation, oxidative stress, apoptosis, and NOD-like receptor signaling. TXNIP, NLRP3, and CASP1 were consistently upregulated in both TBI transcriptomic datasets. LM22-based immune deconvolution suggested increased pro-inflammatory immune signatures and a positive TXNIP-M1 macrophage association (r = 0.63, p < 0.001), which should be interpreted as a transcriptome-derived hypothesis rather than validated murine immune-cell proportions. AI-based models consistently ranked TXNIP/NLRP3 as high-contribution features under internal validation, and removal of these targets reduced model performance. A five-gene inflammatory score achieved an internally evaluated AUC of 0.87, while two-sample MR supported positive genetic associations involving TXNIP expression, TBI risk, NLRP3 and IL-1β expression. Collectively, these findings prioritize the TXNIP/NLRP3/CASP1 module as a computationally supported candidate mechanism through which HT may influence oxidative stress-inflammasome-immune coupling in TBI. This study provides an interpretable drug-target-pathway-phenotype framework and identifies TXNIP, NLRP3, and CASP1 as priority nodes for future experimental validation.

Artificial Intelligence↗

PubMed2005

Intelligent dental identification system (IDIS) in forensic medicine.

This study reports the design and development of the intelligent dental identification system (IDIS), including its efficiency and reliability. Five hundred patients were randomly selected from the Dental Department at Police General Hospital in Thailand to create a population of 3000 known subjects. From the original 500 patients, 100 were randomly selected to create a sample of 1000 unidentifiable subjects (400 subjects with completeness and possible alterations of dental information corresponding to natural occurrences and general dental treatments after the last clinical examination, such as missing teeth, dental caries, dental restorations, and dental prosthetics, 100 subjects with completeness and no alteration of dental information, 500 subjects with incompleteness and no alteration of dental information). Attempts were made to identify the unknown subjects utilizing IDIS. The use of IDIS advanced method resulted in consistent outstanding identification in the range of 82.61-100% with minimal error 0-1.19%. The results of this study indicate that IDIS can be used to support dental identification. It supports not only all types of dentitions: primary, mixed, and permanent but also for incomplete and altered dental information. IDIS is particularly useful in providing the huge quantity and redundancy of related documentation associated with forensic odontology. As a computerized system, IDIS can reduce the time required for identification and store dental digital images with many processing features. Furthermore, IDIS establishes enhancements of documental dental record with odontogram and identification codes, electrical dental record with dental database system, and identification methods and algorithms. IDIS was conceptualized based on the guidelines and standards of the American Board of Forensic Odontology (ABFO) and International Criminal Police Organization (INTERPOL).

Adolescent↗

PubMed2026

Artificial intelligence-assisted clinical exome sequencing: Insights and outcomes from 822 pediatric diagnoses.

PURPOSE: This retrospective study examined the clinical and genetic characteristics of pediatric patients undergoing clinical exome sequencing (ES) and evaluated the performance of a commercially available artificial intelligence (AI) platform that was integrated into our analysis pipeline. METHODS: ES was performed in 822 consecutive patients at a single clinical laboratory. AI-based tools were used to jointly assess genetic information and the proband's Human Phenotype Ontology terms to support variant prioritization during the initial case review. RESULTS: A definitive molecular diagnosis was established in 22% (181 of 822) of index cases, while 40% (325 of 822) had variants of uncertain significance. Among those with a definitive diagnosis, 93% (168 of 181) had a single finding and 7% (13 of 181) had multiple findings. Of the 152 reported pathogenic/likely pathogenic variants in the fully resolved cases, 98.7% were successfully flagged by AI, and 75.0% ranked among the top 10 "most likely" variants. CONCLUSION: Clinical ES provides a substantial diagnostic yield in complex pediatric disorders. Integration of AI-powered platforms can accelerate phenotype-driven variant prioritization and facilitate rare disease diagnostics, but underscores the need for careful validation and optimization in clinical workflows.

Artificial intelligence↗

PubMed2005

The oncological nurse assistant: a web-based intelligent oncological nurse advisor.

When a person gets a cancer diagnosis the need for medical guidance often appears. In Norway, one of the providers of medical guidelines is the Norwegian Cancer Association where oncological nurses assist people with a cancer diagnosis or their relatives. The nurses search through both national and internal guidebooks and web pages. The input to this process is mostly discharge letters. The whole process is time consuming. To serve more patients, PaSent, a web-based intelligent oncological nurse advisor, has been developed. Through using discharge letters as input to our neural network-based information retrieval system PaSent, we have been able to provide relevant medical information to the patient as well as to the health personnel themselves. The PaSent search method uses predefined knowledge about the context, paired with the vocabulary of the input document, to compute a relevance measure for a potential result document. The system has been validated by oncological nurses and medical doctors. In the reported experiments, the PaSent system is able to recommend literature, in the top section of the search result list, that our judges also found highly relevant.

Artificial Intelligence↗

PubMed2006

A novel two-dimensional echocardiographic image analysis system using artificial intelligence-learned pattern recognition for rapid automated ejection fraction.

OBJECTIVES: We sought to test the hypothesis that a novel 2-dimensional echocardiographic image analysis system using artificial intelligence-learned pattern recognition can rapidly and reproducibly calculate ejection fraction (EF). BACKGROUND: Echocardiographic EF by manual tracing is time consuming, and visual assessment is inherently subjective. METHODS: We studied 218 patients (72 female), including 165 with abnormal left ventricular (LV) function. Auto EF incorporated a database trained on >10,000 human EF tracings to automatically locate and track the LV endocardium from routine grayscale digital cineloops and calculate EF in 15 s. Auto EF results were independently compared with manually traced biplane Simpson's rule, visual EF, and magnetic resonance imaging (MRI) in a subset. RESULTS: Auto EF was possible in 200 (92%) of consecutive patients, of which 77% were completely automated and 23% required manual editing. Auto EF correlated well with manual EF (r = 0.98; 6% limits of agreement) and required less time per patient (48 +/- 26 s vs. 102 +/- 21 s; p < 0.01). Auto EF correlated well with visual EF by expert readers (r = 0.96; p < 0.001), but interobserver variability was greater (3.4 +/- 2.9% vs. 9.8 +/- 5.7%, respectively; p < 0.001). Visual EF was less accurate by novice readers (r = 0.82; 19% limits of agreement) and improved with trainee-operated Auto EF (r = 0.96; 7% limits of agreement). Auto EF also correlated with MRI EF (n = 21) (r = 0.95; 12% limits of agreement), but underestimated absolute volumes (r = 0.95; bias of -36 +/- 27 ml overall). CONCLUSIONS: Auto EF can automatically calculate EF similarly to results by manual biplane Simpson's rule and MRI, with less variability than visual EF, and has clinical potential.

Aged↗

PubMed2025

Foundations of Artificial Intelligence in Hepatology: What a Clinician Needs to Know.

This review focuses on foundational knowledge about artificial intelligence (AI) in hepatology, exploring how AI, including machine learning and deep learning, leverages large-scale clinical data to transform the diagnosis, risk assessment, prognostication, and management of liver diseases. Online resources are described to offer fundamental AI knowledge and essential technical skills and to facilitate clinician participation across the entire AI lifecycle, ensuring they contribute not only as end users but also in development and deployment. Unlike traditional statistical approaches that prioritize interpretable parameters and clinical insight, AI focuses on maximizing predictive accuracy by identifying complex, often non-linear patterns using high-dimensional data, albeit often at the cost of model interpretability. AI is demonstrating clinical utility in liver histopathology and radiological imaging, significantly improving detection accuracy for cirrhosis, clinically significant portal hypertension, and hepatocellular carcinoma. Beyond diagnostics, AI-driven prediction models are emerging to provide personalized risk stratification for the development of liver-related complications and treatment guidance, based on complex data including longitudinal laboratory results, comorbidities, and co-medication use to monitor disease progression and therapy response. The field is rapidly expanding into novel areas such as analyzing patient-reported outcomes, genomic data, and real-time liver function monitoring, offering deeper mechanistic insights alongside clinical tools. Despite the potential to revolutionize hepatology practice and research, successful integration into routine care faces challenges. These include seamless workflow integration with existing electronic health records, establishing clear liability frameworks, and guaranteeing protection of patient privacy. Addressing these hurdles requires collaborative efforts from clinicians, researchers, and regulators to develop best practices and governance. Understanding the transformative capabilities, current applications, emerging frontiers, and essential implementation considerations is crucial for clinicians navigating the evolving AI landscape and responsibly utilizing its power for improved patient outcomes.

PROBAST+AI↗

PubMed2026

Artificial intelligence-driven advancements in agricultural biotechnology.

The need for faster and more informative data processing for better decision-making is driving the adoption of artificial intelligence (AI) in the agricultural sector. Thanks to recent advancements in computer science and the increase in computational powers of modern computers, AI is not only augmenting traditional solutions, but also helping in developing novel solutions to existing challenging matters. AI-driven models have an exceptional ability to identify patterns and combine a diverse collection of data together and make inference. The increasing pressure on farmlands posed by the growing global population and climate change is lessening growth, yield, and productivity ultimately posing risk to food security worldwide. Incorporation of AI in agriculture has the potential to drive farming efficiency to new heights. This comprehensive review critically evaluates the evolution of AI in agricultural biotechnology from a theoretical concept to a global phenomenon. A comprehensive literature search was performed using major scientific databases, including PubMed, Web of Science, Embase, Scopus, Lens and the Cochrane Library. In this review, we empirically demonstrate the fields advancement toward more capable AI systems and discuss the current applications of AI across crop improvement and precision agriculture such as crop improvement and genetic engineering, genomic selection and plant breeding, pest and disease detection, precision agriculture and smart farming, soil health and nutrient management, climate resilient crop development, livestock biotechnology, challenges and ethical considerations in AI based agricultural biotechnology. Furthermore, this review addresses the exponential growth of commercial intellectual property in the field and contrast it with academic publication outputs. Finally, we critically assess the ethical challenges impeding equitable adoption of AI including data sovereignty and digital divide, while projecting future frontiers involving quantum computing. This review will help build sustainable agricultural systems capable of adapting to climate change, contribute to the development of climate-resilient and high-yielding crops, and address global food security challenges.

Agriculture↗

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

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↗

PubMed2005

Rehabilitation device with variable resistance and intelligent control.

Resistance exercise has been widely reported to have positive rehabilitation effects for patients with neuromuscular and orthopaedic conditions. This paper presents an optimal design of magneto-rheological fluid dampers for variable resistance exercise device in the form of a knee brace. An intelligent supervisory control for regulating the resistive force or torque of the knee brace has also been studied. The device provides both isometric and isokinetic strength training for the knee.

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↗

PubMed2006

The androgen receptor gene: a major modifier of speed of neuronal transmission and intelligence?

Humans show considerable additive genetic variance in cognitive ability or general intelligence (g) but the genes that influence this variation are largely unknown. It is suggested here that the X-linked androgen receptor gene (AR) has a major modifying effect on speed of neuronal transmission and thus on g. The AR is polymorphic in its N-terminal transactivation domain which encodes a polyglutamine tract (CAGn) with a parametric mean of n=21 CAG repeats and normal variation between n=11 and n=30 repeats . Very low repeat numbers are associated with mental retardation, repeat numbers above 30 with reduced cognitive function, and CAGn greater than 40 with spinal and bulbar muscular atrophy. Within the range of 11-30 repeats short CAG chains are associated with high androgen sensitivity and high sperm counts. Despite this, all human populations contain many individuals with n>21 repeats. I suggest that within the range of 11-30 repeats there is a positive association with speed of neuronal transmission and values of g. The advantage of high g and the consequent spread of alleles for high CAGn will be countered by the negative effects on sperm production. Below CAGn=11 and above CAGn=30 neuronal speed may reduce, thus leading to reductions in g and loss of function of neurons. In support of the model I discuss the link between the X-chromosome and g, the comparative structure of the AR gene in the primates, and the variation in CAGn and g in human ethnic groups.

Animals↗