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PubMed1995

Neurofibromatosis type 1 in childhood: correlation of MRI findings with intelligence.

In a group of 28 children with neurofibromatosis type 1 aged between 4 and 16 years, neuroradiological findings were correlated with intelligence as measured by the Wechsler scales. The presence or specific location in the brain of T2 weighted prolonged signals on MRI was not associated with cognitive problems. No other physical characteristics associated with neurofibromatosis type 1 were found to correlate significantly with IQ. At the present these T2 weighted hyper-intense spots should not be used to predict neurofibromatosis type 1 associated cognitive problems.

Analysis of Variance↗

PubMed2005

Genetics of brain structure and intelligence.

Genetic influences on brain morphology and IQ are well studied. A variety of sophisticated brain-mapping approaches relating genetic influences on brain structure and intelligence establishes a regional distribution for this relationship that is consistent with behavioral studies. We highlight those studies that illustrate the complex cortical patterns associated with measures of cognitive ability. A measure of cognitive ability, known as g, has been shown highly heritable across many studies. We argue that these genetic links are partly mediated by brain structure that is likewise under strong genetic control. Other factors, such as the environment, obviously play a role, but the predominant determinant appears to be genetic.

Animals↗

PubMed2004

Influence of breast-feeding and parental intelligence on cognitive development in the 24-month-old child.

This study was designed to analyze the relationship between breast-feeding and mental development at 24 months of age, independently of the influence of other factors. A total of 238 babies born between October 1995 and February 1998 were enrolled in an observational prospective cohort study. Cognitive development was assessed using the Bayley Infant Development Scale. The results of multiple linear regression analysis showed that infants breast-fed for longer than 4 months scored 4.3 points higher on the mental development scale than those breast-fed for less time. No differences were found in psychomotor development as a function of feeding regimen or duration. The positive linear correlation observed between parental IQ and mental development scores at 24 months was also statistically significant (mother: r = 0.39; p < 0.001; father: r = 0.43; p < 0.001). It may be concluded that breast-feeding for longer than 4 months has a positive effect on the child's mental development at 24 months of age. Parental intelligence also appears to influence cognitive development.

Bottle Feeding↗

PubMed2026

Artificial Intelligence Cannot Replace Peer Reviewers but May Help Editors Triage: A Comparative Analysis of a Large Language Model and Human Reviewer Recommendations at the American Journal of Sports Medicine.

BACKGROUND: The peer review system faces increasing strain from rising manuscript volumes, reviewer fatigue, and well-documented interreviewer disagreement. Large language models (LLMs) have shown potential to support the peer review process, but their ability to replicate editorial decisions at high-impact medical journals and their utility as manuscript screening tools remain unknown. PURPOSE: To compare the agreement between an LLM and the final editorial decision on manuscripts submitted to the American Journal of Sports Medicine and to evaluate the potential of LLMs as a manuscript screening tool. STUDY DESIGN: Cross-sectional agreement study. METHODS: Fifty-four manuscripts randomly selected from submissions to the American Journal of Sports Medicine (September 2024-October 2024) were reviewed by a locally deployed LLM (Ministral 3 14B; Mistral AI) using a standardized prompt. The artificial intelligence (AI) produced a categorical recommendation (reject, cascade, revision, or accept) and a numerical score (0-100) for each manuscript. Agreement with the final editorial decision was assessed by Cohen kappa (4-category model) for pooled human reviewers (n = 139 reviews) and the AI (n = 54). Screening performance was evaluated by positive predictive value (PPV), sensitivity, and specificity. RESULTS: Pooled human reviewers demonstrated fair agreement with the final decision (κ = 0.181 [P < .001]; 42.4% agreement), while the AI demonstrated slight, nonsignificant agreement (κ = 0.126 [P = .099]; 37.0% agreement). The AI recommended revision for 61.1% of manuscripts, of which 72.7% were ultimately rejected or cascaded, demonstrating systematic "revision bias." When the AI recommended rejection, 54.5% of those manuscripts were ultimately rejected and 27.3% were cascaded; when the AI recommended cascade, 50% were rejected and 50% were cascaded. However, when the AI recommended rejection or cascade (n = 21), 90.5% received a final decision of rejection or cascade (PPV, 90.5%; specificity, 81.8%). Manuscripts with an AI score <70 were rejected or cascaded 88.0% of the time (PPV, 88.0%). CONCLUSION: AI cannot replicate the nuanced judgment of human peer reviewers at a high-impact sports medicine journal. When AI recommended rejection or cascade, 90.5% of manuscripts received that final decision (descriptive PPV, 90.5%; 95% CI, 71.1%-97.3%), suggesting potential utility as an exploratory first-pass screening tool warranting further validation in larger cohorts. However, AI could not reliably distinguish manuscripts destined for outright rejection from those that would be cascaded to a sister journal-an important limitation for editorial triage applications.

Sports Medicine↗

PubMed2000

Intelligence after stroke in childhood: review of the literature and suggestions for future research.

Review of published clinical and neuropsychologic outcome studies reveals limited information about intellectual functioning after childhood stroke. The extant data are supplemented here by analysis of intelligence quotient (IQ) results obtained from 38 children in an ongoing study of unilateral middle cerebral artery ischemic stroke. Evidence so far indicates that, after stroke, mean IQ falls significantly below the population mean but remains within the average range. There is no significant difference between hemispheric side of injury; the Verbal and Performance IQ lateralization profile widely recognized in adults with unilateral injury is not apparent in younger children, and there is only a trend toward this profile in older children. The effects of a number of other variables, including sex, site of stroke, and longitudinal assessment, are also considered. Although the generally minor effect of stroke on IQ is encouraging, a number of children do require extra help on return to school. Some suggestions for future research are highlighted in order to encourage further consideration of the issues raised here.

Age Factors↗

PubMed2001

Concurrent validity of the Peabody Picture Vocabulary Test-Third Edition as an intelligence and achievement screener for low SES African American children.

Authors tested the validity of the Peabody Picture Vocabulary Test-Third Edition (PPVT-III) as a screening measure for intelligence and achievement. The PPVT-III and Kaufman Assessment Battery for Children (KABC) were administered to 416 African American children of low socioeconomic status in a counterbalanced design. Results indicated that the PPVT-III correlated .58 with the KABC Mental Processing Composite (MPC) score; however, participants scored significantly lower (M = 8.3 points) on the PPVT-III than on the MPC. Although receiver operating characteristic (ROC) analyses supported the use of the PPVT-III as a valid intellectual and achievement screener, the selection of a single cutoff score for the PPVT-III was problematic. The purpose of the screening program should guide selection of a cutoff score for the PPVT-III.

Achievement↗

PubMed2026

Artificial intelligence for translational personalized neoantigen cancer vaccine development.

Personalized neoantigen cancer vaccine is a promising strategy for precision immunotherapy by targeting patient-specific and mutation-derived tumor antigens. Early clinical studies have demonstrated the feasibility, safety, and immunogenicity of these vaccines across multiple solid tumors, with encouraging outcomes particularly when combined with immune checkpoint blockade. However, broader clinical translation remains limited by sequential bottlenecks across the vaccine development pipeline, including false-positive neoantigen selection,  imperfect modeling of antigen processing and HLA presentation, limited prediction of T-cell receptor recognition, and challenges in formulation, delivery, and manufacturing. Artificial intelligence and advanced computational workflows are increasingly integrated into this pipeline to improve candidate prioritization and support more reproducible decision-making. In this review, we summarize clinical progress and key translational barriers in personalized neoantigen vaccination, and discuss how AI-enabled approaches may contribute across four major stages: multi-omics integration for neoantigen discovery, processing-aware HLA presentation prediction, structure-aware and TCR-informed immunogenicity modeling, and data-driven formulation optimization, particularly for lipid nanoparticle-based delivery systems. These approaches are able to help narrow biological and chemical search spaces, improve prioritization, and provide mechanistic insights into antigen presentation and immune recognition rather than replacing experimental validation. This articlefurther addresses future implementation challenges, including dataset diversity, model interpretability, prospective benchmarking, manufacturing traceability, and evolving regulatory frameworks for individualized mRNA cancer immunotherapies. Integrating computational innovation with rigorous immunological validation, scalable manufacturing, and regulatory oversight will be essential for advancing personalized neoantigen vaccines toward broader clinical implementation.

Cancer Vaccines↗

PubMed2025

Artificial intelligence in healthcare and medicine: clinical applications, therapeutic advances, and future perspectives.

Healthcare systems worldwide face growing challenges, including rising costs, workforce shortages, and disparities in access and quality, particularly in low- and middle-income countries. Artificial intelligence (AI) has emerged as a transformative tool capable of addressing these issues by enhancing diagnostics, treatment planning, patient monitoring, and healthcare efficiency. AI's role in modern medicine spans disease detection, personalized care, drug discovery, predictive analytics, telemedicine, and wearable health technologies. Leveraging machine learning and deep learning, AI can analyze complex data sets, including electronic health records, medical imaging, and genomic profiles, to identify patterns, predict disease progression, and recommend optimized treatment strategies. AI also has the potential to promote equity by enabling cost-effective, resource-efficient solutions in low-resource and remote settings, such as mobile diagnostics, wearable biosensors, and lightweight algorithms. Successful deployment requires addressing critical challenges, including data privacy, algorithmic bias, model interpretability, regulatory oversight, and maintaining human clinical oversight. Emphasizing scalable, ethical, and evidence-driven implementation, key strategies include clinician training in AI literacy, adoption of resource efficient tools, global collaboration, and robust regulatory frameworks to ensure transparency, safety, and accountability. By complementing rather than replacing healthcare professionals, AI can reduce errors, optimize resources, improve patient outcomes, and expand access to quality care. This review emphasizes the responsible integration of AI as a powerful catalyst for innovation, sustainability, and equity in healthcare delivery worldwide.

Humans↗

PubMed1992

Whole-brain irradiation and decline in intelligence: the influence of dose and age on IQ score.

PURPOSE: Decline in intelligence can occur after whole-brain cranial irradiation for childhood malignancy. The purpose of this analysis was to estimate better the impact of dose and age at time of irradiation on IQ decline. PATIENTS AND METHODS: A total of 48 children were studied. We combined two previously reported studies that included 15 patients with pediatric acute lymphocytic leukemia (ALL) and 18 pediatric patients with medulloblastoma/posterior fossa primitive neural ectodermal tumors (PNETs) in whom serial IQ tests were administered. Another 15 patients (nine ALL and six PNET) were studied subsequent to these reports. This experience included ALL patients who were treated with whole-brain irradiation at doses of 18 Gy (n = 9) and 24 Gy (n = 15), and PNET patients who were treated with 18 Gy (n = 5), 22 to 24 Gy (n = 2), and 32 to 40 Gy (n = 17). Multiple regression models were constructed to estimate expected IQ score after treatment based on initial IQ score, age at treatment, and dose of whole-brain irradiation. RESULTS: Using a multiple linear regression model to correct for initial IQ and age at treatment, patients who received a dose of 36 Gy to the whole brain were estimated to score 8.2 points less on IQ testing than those with 24 Gy (95% confidence interval [CI], 1.8 to 14.6) and 12.3 points less than those who received 18 Gy (95% CI, 2.7 to 21.7). Older age at the time of irradiation resulted in less decline in subsequent IQ score. The predicted IQ decline is 11.9 points less in a 10-year-old patient than in a 3-year-old patient (95% CI, 4.2 to 19.6) for equivalent doses of irradiation. The model to predict IQ accounts for half the total variation in IQ score. There was no significant difference between the coefficients that reflected IQ decrease from radiation dose between subgroups who had ALL versus those with PNET. CONCLUSIONS: One can forecast final IQ score based on the initial IQ score, dose of irradiation, and age at time of irradiation. Our findings should aid in the selection of appropriate therapy when whole-brain irradiation is needed.

Adolescent↗

PubMed2003

Exploratory factor analysis of the Wechsler Abbreviated Scale of Intelligence (WASI) in adult standardization and clinical samples.

Exploratory factor analyses were conducted separately on the Wechsler Abbreviated Scale of Intelligence (WASI; The Psychological Corporation, 1999) adult standardization sample (n = 1,145) and a diagnostically heterogeneous adult clinical sample (n = 201). In the latter group, means for age, education, and WASI Full Scale IQ were 59.25 years (SD = 17.52), 12.39 years (SD = 2.88), and 89.91 (SD = 16.00). For each sample, the four WASI subtests were subjected to a principal-axis factor analysis followed by varimax and promax rotations. Two factors were specified to be retained. Verbal Comprehension and Perceptual Organization factors were identified in both analyses. Coefficients of congruence were 0.98 for Factor I and 0.99 for Factor II, suggesting factorial equivalence across the standardization and clinical samples.

Adolescent↗

PubMed1997

Relation between intelligence and psychopathology among preschoolers.

Examined the relation between intelligence and psychopathology in a nonclinical sample of 510 children ages 2 to 5 years. Psychopathology was measured using both quantitative, dimensional methods (Child Behavior Checklist [CBCL]) and taxonomic methods (the Diagnostic and Statistical Manual of Mental Disorders [3rd. ed., Rev.; DSM-III-R; American Psychological Association, 1987]). IQ scores were derived from either the McCarthy Scales of Children's Abilities or the Bayley Scales of Mental Development. Based on quantitative, dimensional data, results support similar findings among older children and clinical populations that lower McCarthy general, verbal, and perceptual-performance IQ scores are associated with various types of psychopathology. Results were also consistent for the DSM-III-R data. Bayley IQ scores did not predict CBCL psychopathology or DSM-III-R Disruptive Disorders, but they did predict the presence of a DSM-III-R diagnosis. Early identification of intellectual deficits among preschoolers ages 3 to 5 may help to prevent later school difficulties and severe psychopathology.

Child Behavior Disorders↗

PubMed1999

Breastfeeding effects on intelligence quotient in 4- and 11-year-old children.

OBJECTIVE: A study of preterm children found an IQ advantage among those who were breastfed as infants after controlling for maternal social class and educational status. However, this advantage needs to be examined in light of other maternal characteristics, such as maternal IQ and parenting skills, which were not measured in that study and which have been found to be related to breastfeeding. METHODOLOGY: IQ was assessed in 323 children at 4 years of age on the McCarthy Scales of Children's Abilities and the Peabody Picture Vocabulary Test-Revised and in 280 children on the Wechsler Intelligence Test for Children-Revised at 11 years of age. RESULTS: Children who were breastfed in infancy had significantly higher scores on IQ tests at both ages, even after adjusting for social class and education, confirming the earlier findings and extending them to a predominantly full-term sample. However, the effect of breastfeeding was no longer significant after adjusting for maternal IQ assessed on the Peabody Picture Vocabulary Test-Revised and for parenting skills assessed on the Home Observation for Measurement of the Environment. Significant relations between breastfeeding and Woodcock Reading Achievement scores at 11 years were also reduced to nonsignificant levels after the inclusion of maternal IQ and the Home Observation for Measurement of the Environment. CONCLUSIONS: These findings suggest that the observed advantage of breastfeeding on IQ is related to genetic and socioenvironmental factors rather than to the nutritional benefits of breastfeeding on neurodevelopment. They should not be interpreted as detracting from the medical benefits associated with breastfeeding.

Bottle Feeding↗

PubMed2026

Enterocutaneous Fistula-Associated Sepsis and Mortality: Development and Validation of a Multimodal Artificial Intelligence Prediction Model.

BACKGROUND: Predicting enterocutaneous fistula (ECF)-associated sepsis and mortality poses significant challenges in digital health care due to the disease's complexity and heterogeneous clinical manifestations. Current approaches that rely on single-modal data or traditional scoring systems often fail to capture the intricate immune-inflammatory dynamics and multisystem involvement in patients with ECF. OBJECTIVE: This study aims to develop an artificial intelligence (AI)-driven multimodal fusion model integrating clinical, imaging, and transcriptomic data for early prediction of ECF-associated sepsis and 28-day mortality, addressing the limitations of conventional single-dimensional models. METHODS: This study leveraged publicly available datasets (Medical Information Mart for Intensive Care III [MIMIC-III], electronic Intensive Care Unit [eICU], and The Cancer Genome Atlas) to construct a multimodal framework. Clinical parameters were processed using Extreme Gradient Boosting, abdominal imaging features were extracted via convolutional neural networks, and transcriptomic profiles were analyzed with variational autoencoders. A Transformer-based fusion network was employed for joint prediction and validated through cross-validation and external testing. Key features were identified using Shapley Additive Explanations and Local Interpretable Model-Agnostic Explanations interpretability algorithms, while immune regulatory mechanisms were explored via weighted gene co-expression network analysis. RESULTS: The multimodal model achieved an area under the curve (AUC) of 0.89 for predicting sepsis and 28-day mortality, outperforming unimodal models (clinical-only model, AUC 0.72, and imaging-only model, AUC 0.78). Critical predictors included Sequential Organ Failure Assessment score, lactate levels, intra-abdominal free fluid on imaging, and immunoregulatory genes (programmed death-ligand 1 [PD-L1] and indoleamine 2,3-dioxygenase 1 [IDO1]). Mechanistic analysis revealed distinct immune reprogramming in patients with sepsis, characterized by increased regulatory T cells and M2 macrophages, along with downregulated cluster of differentiation 8+ (CD8+) T cells. CONCLUSIONS: This multimodal AI model offers an innovative digital solution in medical informatics, enabling precise early risk stratification for ECF-associated sepsis. By integrating multisource data and providing interpretable insights into immune-inflammatory pathways, the model enhances health care quality for patients with ECF and paves the way for personalized intervention strategies.

Humans↗

PubMed1990

Stanford-Binet, Fourth Edition and the WISC--R for children in the lower range of intelligence.

The Stanford-Binet IV and the WISC--R were administered to 30 children, ages 8 to 15 yr., whose scores were in the range of mild mental retardation. The mean interval between testings was 7 mo. The correlation was .83, with a median difference of 4 points. The WISC--R mean IQ was significantly lower than the Stanford-Binet-IV Composite mean score for the group. The disparity in scores points to the need to evaluate measures of intelligence together with other indices of functioning in decision-making for mildly retarded children.

Adolescent↗

PubMed1997

Contributions to the history of psychology: CVIII. On aging and intelligence: history teaches a different lesson.

Early researchers investigating aging have often been accused of forming the foundation for the belief that intelligence peaks in early adulthood and declines steadily thereafter, a concept sometimes known as the classic aging curve. Documentation in this article indicates that at least several early researchers on the subject were actually making very different arguments and that they were well aware of the limitations of their data.

Adolescent↗

PubMed2003

Influence of test anxiety on measurement of intelligence.

In this study a measurement model for a test anxiety questionnaire was investigated in a sample of 207 Dutch students in the first grade of junior secondary vocational education. The results of a confirmatory factor analysis showed that a model for test anxiety with three factors for worry, emotionality, and lack of self-confidence is associated with a significantly better fit than a model comprised of only the first two factors. The relations of the three test anxiety factors to scores on intelligence tests for measuring verbal ability, reasoning, and spatial ability were examined. The results indicated that test anxiety appears to be transitory: the negative relation between test anxiety and test performance promptly fades away. Finally, we examined whether a distinction can be made between highly test anxious students with low performance due to worrisome thoughts (interference hypothesis) or low ability (deficit hypothesis). Results do not support the deficit hypothesis because the scores of all highly test anxious students increased in a less stressful situation.

Adolescent↗

PubMed2006

Regional intelligence and suicide rate in Denmark.

Consistent with evidence from several recent geographical (cross-national and within-nation) studies (by Lester and by Voracek), a positive ecological (aggregate-level) correlation of regional intelligence and suicide rate was found across the seven major geographical regions of Denmark.

Denmark↗

PubMed2026

How Following Medical Artificial Intelligence Advice Can Mitigate Malpractice Liability: Cross-National Insights from a Randomized Trial.

Artificial intelligence (AI) increasingly influences clinical decision-making, yet its recommendations may diverge from standard care. Although malpractice concerns are thought to discourage physicians from following AI advice, experimental evidence from the United States suggests the opposite: lay jurors are more likely to hold physicians liable when they reject AI recommendations. Whether this pattern extends to systems in which court-appointed experts, not lay jurors, determine liability remains unknown. Methods: To examine how physicians and laypeople in expert-based and lay-juror legal systems evaluate physicians' acceptance or rejection of AI recommendations, particularly when those recommendations deviate from standard care, we designed a randomized vignette study: a 2 × 2 factorial design varying the AI recommendation (standard vs. nonstandard care) and a fictional physician's decision (accept vs. reject). The study was conducted online in 2023 among nationally representative samples of U.S. and German adults and from 2023 to 2024 among German physicians. In total, 387 German physicians, 2291 U.S. adults, and 2283 German adults participated; those not completing the survey or failing attention checks were excluded per preregistered criteria. Participants were randomly assigned to 1 of 4 vignettes, varying the AI recommendation (standard vs. nonstandard care) and physician's decision (accept vs. reject). The reasonableness of the fictional physician's decision was measured, rated by participants on a Likert scale. Results: Analysis, following preregistered exclusion criteria, included 248 German physicians, 1202 U.S. adults, and 1358 German adults. Physicians accepting standard-care AI recommendations were rated more reasonable than those rejecting them (U.S. laypeople: t = 5.36; 95% CI, 0.45-0.97; P < 0.001; German physicians: t = 2.47; 95% CI, 0.14-1.30; P = 0.02; German laypeople: t = 4.14; 95% CI, 0.27-0.76; P < 0.001). Ratings of physicians accepting versus rejecting AI nonstandard-care recommendations were statistically equivalent. Equivalence was tested at an α-value of 0.05 using a two 1-sided tests procedure, reported with 90% CIs per standard convention (U.S. laypeople: t = -4.90; 90% CI, -0.1 to 0.36; P < 0.001; German physicians: t = -1.76; 90% CI, -0.12 to 0.67; P = 0.04; German laypeople: t = 5.35; 90% CI, -0.35 to 0.06; P < 0.001). Conclusion: Across the United States and Germany, samples representative of lay jurors and court-appointed experts viewed accepting standard-care AI advice as more reasonable, whereas accepting or rejecting nonstandard-care AI advice was judged similarly. Contrary to predictions, malpractice liability regimes do not necessarily pose a barrier to AI use in precision medicine.

Artificial Intelligence↗