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PubMed1978

Modifiability of fluid intelligence in aging: a short-term longitudinal training approach.

The aim of this study was to examine to what degree fluid intelligence can be modified in aged subjects. The effectiveness of a cognitive training program designed to enhance one primary component of fluid intelligence, Figural Relations, was assessed by comparing the posttraining performances of 15 experimental and 15 control subjects (mean age: 69; age range 59-85) using a transfer paradigm and three posttraining assessments conducted approximately 1 week, 1 mo, and 6 mo following training. The post-training performance of the two groups was compared on three near (fluid intelligence) and one far (crystallized intelligence) transfer measures. A hierarchical pattern was predicted with the magnitude of training effects ordering themselves in descending order from near to far transfer measures. The training program was successful in enhancing performance on the fluid-nearest measure on all three posttests and for the next fluid-near measure on the first posttest. In addition, significant retest effects resulted which, however, were neither task-specific nor hierarchically ordered, but general and therefore indicative of ability-extraneous factors, such as test sophistication. The findings contribute to a position implying that intellectual performance in old age is more modifiable through short-term behavioral intervention than traditionally assumed.

Aged↗
PubMed2026

A systematic approach to standardizing the visual appearance of endometriotic lesions for artificial intelligence recognition.

INTRODUCTION: Numerous studies have shown that the diagnostic performance and reproducibility of visual recognition of endometriosis during laparoscopy are poor. The use of artificial intelligence (AI) seems relevant for exhaustive lesion recognition. Standardization of the visual classification of lesions, in the form of an ontology, is an essential prerequisite to enable medical experts to annotate surgical data consistently and subsequently allow engineers to train and build an artificial intelligence tool for endometriosis recognition. MATERIAL AND METHODS: A systematic search was conducted in the MEDLINE (via PubMed), EMBASE, and the Cochrane Library databases up to May 2022, aiming to identify studies describing the laparoscopic visual appearance of superficial endometriosis, endometriomas, and deep infiltrating endometriosis. The accumulated data in the literature concerning the visual appearance of the different forms of endometriosis were used to create an ontology that could be used for artificial intelligence applications. RESULTS: Out of 932 articles screened, 35 studies were selected based on the inclusion criteria of human subjects with histologically confirmed endometriosis lesions visualized via laparoscopy. The selected studies were reviewed to develop a visual ontology of endometriosis lesions observed via laparoscopy. The lesions were categorized into 4 classes and further subdivided into 11 subclasses: superficial (black, red, white, or subtle), adhesions (dense or filmy), deep (obliteration, retraction, or deformation), and ovarian (endometrioma or chocolate fluid). The positive predictive value (PPV) varied across lesion types: black lesions (PPV 47%-97%), red lesions (PPV 33%-100%), white lesions (PPV 20%-81%), and ovarian endometriosis (PPV 42%-98%). Nonspecific lesions such as adhesions (PPV 16%-50%) and subtle superficial lesions (PPV 0%-67%) presented lower PPVs. Deep endometriosis lesions, often buried within organs, required indirect signs (obliteration, retraction, deformation) for identification. CONCLUSIONS: The visual ontology proposed in this systematic search could facilitate the detection and classification of endometriosis lesions using artificial intelligence. This study highlights the challenges of reaching a consensus on lesion recognition and classification in AI projects due to the diverse visual presentations of endometriosis.

Humans↗
PubMed1978

A comparison of techniques for measuring intelligibility of dysarthric speech.

Eight techniques for quantifying intelligibility of dysarthric speech were compared. Eight dysarthric speakers who represented a wide range of severity were recorded producing single words and sentences. Thirty-two college students performed the following intelligibility quantification tasks: percentage estimates, rating scale estimates, work and sentence transcriptions, word and sentence completions, and word and sentence multiple-choice tasks. Intelligibility scores for transcriptions were compared to estimates and to other objective tasks with the following results: (1) all measurement techniques, except word completion, rank ordered speakers similarly to transcriptions, (2) mean estimates of intelligibility closely parallel transcription scores, but dispersion of listener estimates was large, and (3) objective tasks form a hierarchy with speakers receiving lowest scores on transcriptions, intermediate scores on completions, and highest scores on multiple-choice tasks. Mean scores for words and sentences were similar. Implications of results for clinical management of dysarthria are discussed.

Adult↗
PubMed1978

Learning theory, intelligence, and mental development.

The current state of experimental research on mental retardation was considered from a historical perspective. The early position that defined intelligence as the ability to learn was presented. Subsequent refinements were traced as intelligence was related first to stages and, subsequently, to subprocesses of learning. Research on learning in retarded persons, which mainly dates from the late 1950s, took little account of this history. The methodological errors that flawed much modern mental retardation research were made explicit. Attempts to isolate the roles of MA and of intelligence were reviewed. It was shown that with growing understanding sophisticated designs emerged. Kappauf's three-dimensional model relating performance to IQ and CA was discussed and some models of retardation presented. Research on the development of intelligence was related to these models and to the design of intervention strategies.

Child↗
PubMed2026

Beyond antigen matching: compatibility intelligence theory for transfusion as an emergent biological system.

BACKGROUND: Despite major advances in serologic testing, extended phenotyping, and blood group genomics, clinically similar transfusion exposures may result in markedly different immune and clinical outcomes. Existing compatibility strategies do not fully explain this biological variability. OBJECTIVES: To examine transfusion compatibility as an emergent donor-recipient biological state and propose a systems-level conceptual framework that integrates established biological determinants into a testable model for future precision transfusion medicine. METHODS: This narrative review critically synthesizes current evidence from blood group genomics, recipient immunobiology, inflammation, disease-specific biology, transfusion medicine, and computational prediction. The proposed framework distinguishes Compatibility Intelligence Theory (CIT) as a biological interpretation from Precision Transfusion Intelligence (PTI) as its potential clinician-supervised translational application. RESULTS: The review argues that transfusion compatibility is shaped by interactions among donor genetics, recipient immune biology, inflammatory physiology, disease context, transfusion history, and longitudinal adaptation rather than by antigen matching alone. CIT provides an organizational framework for integrating these determinants, whereas PTI describes a possible clinician-supervised translation. To address current feasibility, the revised framework separates variables into routinely measurable, contextually available but incompletely standardized, and research-stage domains, and proposes a staged strategy for deriving rather than assuming their quantitative weights. Any clinical implementation would require comparative validation against current serologic, phenotypic, and genotype-based practice. CONCLUSIONS: Compatibility Intelligence Theory offers a testable systems-level framework for understanding transfusion compatibility without replacing established transfusion practices. The framework is not presented as a ready-to-use score: currently measurable variables can be organized for structured risk review, whereas inflammatory, immunogenetic, and multi-omic inputs require prospective standardization and validation. If future studies demonstrate incremental predictive and patient-centered benefit, CIT-informed PTI could support an adaptive, evidence-based extension of current precision transfusion practice.

Humans↗
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↗
PubMed1977

The House-Tree-Person Test as a measure of intelligence and creativity.

The House-Tree-Person test and a verbal test of mental ability, the Basic Word Vocabulary Test, were administered to 23 male and 27 female, university undergraduates and to 27 boys and 38 girls in Grades 3 to 8. The drawings were given three separate and independent scorings by judges who computed intelligence scores according to the House-Person manual; rated them impressionistically on intelligence, using a forced-distribution method; or rated them impressionistically on creativity, using the same forced-distribution method. The three House-Tree-Person measures were highly intercorrelated for all groups of subjects. All three House-Tree-Person scores also correlated positively and significantly with vocabulary test scores for female university students, as did both Impressionistically derived House-Tree-Person scores for grade-school girls. Male students' and boys' vocabulary scores were unrelated to any of the House-Tree-Person scores. Results suggest that competence in graphic expression operates independently of verbal intelligence in males but validity as a nonverbal test of mental ability and that it can be scored efficiently and reliably by using a global, impressionistic method.

Adult↗
PubMed2026

Beyond antibiotics: artificial intelligence-enabled anti-infective ecosystems for next-generation precision therapeutics against antimicrobial resistance.

The rapid global expansion of antimicrobial resistance (AMR) threatens to undermine decades of progress in infectious disease management and highlights the limitations of conventional antibiotic-centered therapeutic strategies. Although emerging technologies-including antimicrobial peptides, bacteriophage therapy, CRISPR-based antimicrobials, microbiome therapeutics, anti-virulence approaches, nanotechnology-enabled drug delivery, and artificial intelligence (AI)-have individually demonstrated considerable promise, they are predominantly being developed as independent interventions rather than as coordinated components of an integrated therapeutic strategy. This Perspective proposes the Intelligent Anti-Infective Ecosystem (IAIE) as a conceptual systems-level framework that computationally integrates multimodal diagnostics, pathogen genomics, microbiome profiling, AI-assisted decision support, programmable precision therapeutics, ecological monitoring, and longitudinal clinical feedback within a continuously learning dynamically optimized workflow. Unlike existing paradigms that primarily optimize individual technologies or therapeutic decisions, IAIE emphasizes closed-loop coordination among complementary antimicrobial approaches to support precision-guided infection management while preserving microbiome integrity and mitigating resistance selection pressure. We further outline the core components, operational principles, translational challenges, and technology readiness of the major therapeutic platforms that could contribute to such an ecosystem, while distinguishing clinically established interventions from emerging experimental strategies. Importantly, IAIE should be interpreted as a prospective conceptual architecture rather than an existing clinical platform. Its proposed clinical value remains to be established through sequential computational, preclinical, and prospective clinical investigations using standardized microbiological, ecological, and patient-centered outcome measures. By framing antimicrobial innovation within an responsive systems perspective, IAIE provides a roadmap for future multidisciplinary research aimed at integrating artificial intelligence and systems microbiology to enable sustainable management of antimicrobial resistance.

Humans↗
PubMed1978

Psychological mindedness, intelligence, and item subtlety endorsement patterns on the MMPI.

Investigated the relationship between psychological mindedness (measured by the Psychological Mindedness [Py] scale of the California Psychological Inventory [CPI]), intelligence (estimated by American College Test [act] scores), and item subtlety endorsement patterns of Ss asked to answer the MMPI under standard, fake-good, and fake-bad response sets. Male (N equal to 30) and female (N equal to 30) undergraduate students completed the Py scale and two MMPI protocols--one under standard test-taking instructions and the other under either a fake-good or a fake-bad set with order of administration and sex counterbalanced. Under the standard response set, Ss who scored high on the CPI-Py endorsed more very subtle and somewhat subtle items and fewer neutral, somewhat obvious, and very obvious items than Ss who scored low on this scale. Intellectual ability was not related to the endorsement of subtle or obvious items under standard or fake-good response sets. Under instructions to fake-bad, more intelligent individuals endorsed fewer somewhat subtle items and more very obvious items than less intelligent individuals. These results were discussed in reference to the utility of subtle items as unobstrusive measures of personality or as indicators of certain response sets.

Adolescent↗
PubMed1979

Haptic visual discrimination and intelligence.

Investigated the relationship between tactual-visual discrimination and intelligence from a neuropsychological perspective. Parieto-occipital areas were conceptualized as mediating centers for the integration of tactile and visual information, as well as for the processing of higher cortical functions. A recently designed test of tactual-visual information processing, the Haptic Visual Discrimination Test (HVDT), was administered to a group of 39 first-grade school children. Standard tests of intelligence (Wechsler Intelligence Scale for Children-Revised, WISC-R), academic potential (Metropolitan Readiness Test, MRT), and spatial integration (Bender Visual Motor Gestalt Test, BVMGT) also were given. The obtained correlation coefficients revealed consistently significant associations between the importance of parieto-occipital areas of the cortex for organizing sensory data as well as for the processing of intellectual information.

Child↗
PubMed2026

Decoding cancer with artificial intelligence: Transforming research, diagnosis, and therapy with future insights.

Cancer remains one of the leading global health burdens, with increasing complexity in genomic, imaging, and clinical datasets presenting significant challenges for effective management. Artificial intelligence (AI) has emerged as a powerful tool to address these challenges by enabling pattern recognition, knowledge integration, and data-driven decision-making. This review highlights recent advances in the application of AI across cancer research, diagnosis, and therapy. In research, AI accelerates drug discovery and repurposing, enhances genomic data interpretation, and facilitates biomarker identification through multi-omics integration. In diagnosis, AI has demonstrated high technical performance in radiology for lesion detection and image segmentation, in pathology for tumour grading and molecular prediction, and in liquid biopsy for non-invasive biomarker analysis. In therapy, AI supports precision medicine by predicting treatment responses, monitoring disease progression, and optimizing clinical trial design. Despite these advances, barriers such as data heterogeneity, algorithmic bias, interpretability, and regulatory challenges remain. Future directions, including explainable AI, federated learning, multimodal modelling, and digital twins, hold promise for translating AI-driven innovations into routine oncology practice. Significance Statement This review provides a timely synthesis of recent (2020-2025) advances in artificial intelligence across cancer research, diagnosis, and therapy, highlighting applications in drug discovery, genomics, multi-omics biomarker identification, and clinical decision-making. By integrating technological progress with translational and clinical relevance, this work serves as a valuable resource for bridging AI innovation with precision oncology practice. As a narrative review, the literature was identified through targeted PubMed, Scopus, and Google Scholar searches, combining terms for artificial intelligence, machine learning, and deep learning with cancer-related keywords, with priority given to peer-reviewed studies published between 2020 and 2025, seminal earlier works, and official regulatory or guideline documents. Within each domain, representative studies were selected to illustrate methodological diversity, clinical context, and current translational readiness rather than to provide exhaustive coverage of an extremely rapidly evolving field.

Artificial intelligence↗
PubMed1978

Dementia: the estimation of premorbid intelligence levels using the New Adult Reading Test.

The NART is a new word-reading test which was specifically designed for use with adults: the 50 words were selected in order to assess familiarity with words rather than the ability to phonetically decode unfamilar words, i.e. for each word intelligent guesswork alone would not result in a correct response. The results from a group of patients with cortical atrophy and a control group demonstrated the superiority of the NART over the best previously available word list (the Schonell GWRT) in enabling higher and more accurate levels of intelligence to be predicted. The evidence implied that the reading of the NART words was not significantly affected by the dementing processes in the patients with cortical atrophy, and therefore that the NART reading score can provide an accurate estimate of premorbid intelligence levels in these patients.

Adult↗
PubMed1976

Intelligence structure and personality in various types of physical handicap in childhood and adolescence.

Intelligence structure and personality were assessed in a sample of 104 physically disabled children of normal intelligence (IQ is greater than or equal to 85). Findings were compared to those of healthy controls matched by age, sex, rank order and number of siblings and socioeconomic status. Physically handicapped children had lower scores in all subtests of a multi-factorial intelligence test. Furthermore five different subgroups of physically disabled children could be discriminated along one factor marked by different variables of visual perception. Personality of the total group of handicapped children was different when compared to normal controls. This specific personality pattern may be labeled: lack of emotional integration into social environment without conflict. Various subgroups of physically handicapped children differed only little as far as personality is concerned.

Adolescent↗
PubMed1977

A comparison of three intelligence tests for the assessment of mental retardation.

Thirty-six mentally retarded adolescents were administered the Wechsler Intelligence Scale for Children (WISC), the Peabody Picture Vocabulary Test (PPVT), and the Slosson Intelligence Test (SIT) to determine reliability and comparable performance among the three intelligence tests. Correlations as a function of IQ and MA, by sex, were computed among the three tests. All were significant (p less than .01), but the WISC and SIT had consistently higher relationships than any other comparisons on both IQ and MA. Analyses also indicated that the PPVT produced higher IQ scores than the WISC or SIT and that MA in the PPVT was influenced by the previous administration of the SIT. It was concluded that, as a screening device, the SIT is more reliable and approximates WISC scores more consistently than the PPVT.

Adolescent↗
PubMed1975

Benign epilepsy of children with centrotemporal EEG foci: intelligence, behavior, and school adjustment.

Sixteen children aged 7-12 years with benign epilepsy of childhood and centrotemporal (Rolandic) EEG foci were investigated as regards intelligence, visuomotor coordination, behavior, and school adjustment. They were compared with partly the same-sex class-population, partly randomly selected class controls of the same sex and age. There were no differences between the children and their class controls regarding intelligence, behavior, and school adjustment. The epileptic seizures did not influence the children's intelligence. The visuomotor coordination was impaired in most children (tested by Bender's test), but this was not true for their verbal and nonverbal functions.

Age Factors↗
PubMed1978

Relationship of role identification, self-esteem, and intelligence to sex differences in field independence.

The relationships among perceptual field independence, biological sex, sex-role identity, self-esteem, and intelligence were explored. Tests measuring these variables were administered to 50 male and 50 female volunteers. The results, obtained by standard multiple regression and analysis of covariance procedures, indicate that (a) males are significantly more field-independent than females, (b) regardless of biological sex, subjects with relatively masculine role-identities are more field-independent than subjects with relatively feminine role-identities, (c) self-esteem is not significantly related to perceptual style, and (d) although intelligence has a significant positive relationship to perceptual field independence, intelligence does not account for the sex differential or for the role sex-identification plays in perceptual style.

Female↗
PubMedDate not supplied

Effects of whitening and peak-clipping on speech intelligibility in the presence of a competing message.

The purpose of this experiment was to determine the effects of peak clipping on speech intelligibility when both a target speech and a competing message were simultaneously peak-clipped. A competing message composed of 5 talkers was electrically mixed with CNC discrimination words. This composite signal was presented to normally hearing subjects in three ways: unmodified, whitened, and whitened and peak-clipped. Discrimination functions were obtained for the CNC material by varying the signal-to-competition ratio. Under these conditions, essentially identical discrimination functions were yielded by the unmodified and whitened speech, whereas substantially reduced discrimination scores were obtained with the whitened/clipped speech. These results would suggest that speech intelligibility is reduced by whitening and peak clipping when more than one talker is present. This is true even though earlier studies have shown that whitening and peak clipping do not reduce speech intelligibility when only a single talker is present. Such a finding has implications for wearable amplification.

Acoustic Stimulation↗
PubMed2026

Multimodal artificial intelligence and machine learning in oncology: from data integration to precision cancer care.

Cancer remains a major global health burden, with approximately 20 million new cases and 9.7 million cancer-related deaths reported globally in 2022. While advances in radiological imaging, molecular profiling, and clinical data have enhanced the interpretation of disease progression, the availability of multiple such modalities still does not meet the needs of a large patient population. This narrative review focuses on the role of multimodal artificial intelligence and machine learning in bridging the gap in interpreting heterogeneous modalities to improve risk prediction, prognostic assessment, and treatment decision-making in precision oncology. Multimodal frameworks such as Pathomic Fusion illustrate how complementary histopathological and genomic information can be integrated for cancer diagnosis and prognostic modeling. Multimodal models have demonstrated potential in virtual biopsy, cancer screening, prognostic prediction, radiotherapy planning, intraoperative guidance, and clinical-trial design using digital twins and synthetic control arms. The major limitations of incorporating multimodal artificial intelligence and machine learning in oncology include data heterogeneity, demographic or institutional biases, and reproducibility challenges that hinder translation. Accordingly, appropriate data-governance strategies, fairness audits, and privacy-preserving approaches such as federated learning should be considered where appropriate. Future progress will depend on the development of standardized benchmarking datasets, robust external validation, seamless integration with electronic health records and picture archiving and communication systems, and the implementation of explainable, secure, and clinically validated multimodal artificial intelligence frameworks that support precision oncology in routine clinical practice.

deep learning↗