SEARCH · PubMed Health

Results for “artificial intelligence”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 541 records · Page 30Linked to original sources

PubMed2026

Challenges and future directions in AI-driven biomaterials for microbiome-associated oral infectious diseases: A systematic review.

Oral biofilm-induced antimicrobial resistance is the core pathogenic mechanism of microbiome-associated oral infectious diseases (dental caries, periodontitis, peri-implantitis, and endodontic infection). Traditional therapies and biomaterials are limited by poor biofilm penetration, drug resistance induction, single functionality, and inadequate adaptation to dynamic oral microenvironmental changes (e.g., pH fluctuations, salivary rinsing, masticatory stimulation). Artificial intelligence (AI) has transformed the field by integrating materials science, microbiology, and stomatology data. Via machine learning, deep learning, and multi-physics simulation, AI optimizes biomaterial physicochemical properties, decodes microenvironmental signals, constructs precise sensing-response loops, and supports the full chain of material design, performance prediction, and action simulation, advancing treatment from empirical intervention to precision regulation. This systematic review retrieved literature from PubMed, Embase, and Web of Science (January 2016-January 2026) using keywords across three dimensions: AI, biomaterials, and oral microbiome. Following inclusion/exclusion criteria, 99 articles were included. It elaborates on five core mechanisms of AI-driven oral biomaterials (precise oral microbiome analysis, targeted material design/optimization, performance prediction/simulation, targeted delivery/intervention, effect evaluation/dynamic regulation), analyzes their applications in microbiome-targeted biomaterial research and development (R&D) and clinical practice for the four major oral infectious diseases, addresses technical bottlenecks (insufficient targeting specificity and precision of biomaterials, poor stability and durability in complex oral microenvironments, inadequate biofilm disruption capacity, and clinical translation obstacles), and proposes future directions (multimodal design to enhance targeting specificity, structural and component optimization to improve stability/durability, development of multi-mechanism synergistic biofilm disruption strategies, strengthening translational research for clinical application, and deep integration of AI in the full chain of biomaterial R&D). This work provides comprehensive theoretical and practical support for the R&D, optimization, and clinical translation of AI-driven microbiome-targeted oral biomaterials.

Humans↗

PubMed1995

Computer modeling of adaptive depression.

Mild, delimited, and adaptive depression may be a specific example of a more general class of mechanism by which intelligent systems--individual, social, and artificial--adapt to dynamic, uncertain, and dangerous environments. Computer modeling, based on connectionist and artificial intelligence planning and learning programming techniques, supports this hypothesis by generating both adaptive behavior and analogs for 10 phenomena associated with depression: global, stable, and internal failure explantation, a cognitive loop of failure rumination, decreased motivation, self-esteem, and self-efficacy, and increased realism, negative generalization, and cognitive change. The idea of adaptive depression can be applied to more than one level of living systems. A better understanding of normal and adaptive depression may lead to a better understanding of clinical depression.

Adaptation, Psychological↗

PubMed2003

Combining artificial neural networks and transrectal ultrasound in the diagnosis of prostate cancer.

Arguably the most important step in the prognosis of prostate cancer is early diagnosis. More than 1 million transrectal ultrasound (TRUS)-guided prostate needle biopsies are performed annually in the United States, resulting in the detection of 200,000 new cases per year. Unfortunately, the urologist's ability to diagnose prostate cancer has not kept pace with therapeutic advances; currently, many men are facing the need for prostate biopsy with the likelihood that the result will be inconclusive. This paper will focus on the tools available to assist the clinician in predicting the outcome of the prostate needle biopsy. We will examine the use of "machine learning" models (artificial intelligence), in the form of artificial neural networks (ANNs), to predict prostate biopsy outcomes using prebiopsy variables. Currently, six validated predictive models are available. Of these, five are machine learning models, and one is based on logistic regression. The role of ANNs in providing valuable predictive models to be used in conjunction with TRUS appears promising. In the few studies that have compared machine learning to traditional statistical methods, ANN and logistic regression appear to function equivalently when predicting biopsy outcome. With the introduction of more complex prebiopsy variables, ANNs are in a commanding position for use in predictive models. Easy and immediate physician access to these models will be imperative if their full potential is to be realized.

Biopsy↗

PubMed1991

[Revolution of paradigm in clinical diagnosis--from the mechanization to the intelligent being].

The medical advancements, during the 20th century symbolize the industrialization of medical technologies, i.e., many clinical tests are carried out by the highly advanced automated machines. Also, the concept of intelligent processing of clinical diagnosis seems to have been established in the practice. However, it may be an illusion caused from the term artificial intelligence (AI) which attracts the attention of not only specialists of computer science but also clinicians. The essential nature of AI, especially of expert consultation systems is the same as the existing theories, such as Bayes' theorem, Boolean algebra, multivariate statistical analysis, and Fussy theorem, i.e., the evaluation of a weighted sum of multiple parameters. The weak point of these theories is the lack of time parameter. Therefore, the models using a time parameter including physiological simulation, dynamics model, Weibull model and Markov process are important to realize the revolution of clinical diagnosis from the standpoint of intelligent science and technology.

Artificial Intelligence↗

PubMed1988

Intelligent computer-based assessment and psychotherapy. An expert system for sexual dysfunction.

New and converging developments in the areas of artificial intelligence, intelligent tutoring systems, and cognitive therapy have made possible a new approach to computer-assisted assessment and psychotherapy. This new approach combines the capacity for intelligent therapeutic dialogue with the presentation of individualized therapeutic interventions. Previous attempts at computerized psychotherapy are reviewed to highlight a newly developed rule-based expert system, Sexpert, which assesses and treats sexual dysfunction. Preliminary observations concerning couples' reaction to and acceptance of Sexpert are presented.

Artificial Intelligence↗

PubMed2026

AI-enabled viral genomics: from virus discovery to host prediction and emerging variant forecasting.

The rapid expansion of metagenomic sequencing has generated vast repositories of viral sequence data that far outpace our capacity to interpret them using conventional approaches. Highly divergent sequences, sparse functional annotation, and taxonomically uneven sampling present fundamental challenges for reference-dependent methods, which lose sensitivity precisely for novel and understudied viruses with high public health relevance. Artificial intelligence (AI) provides a new avenue to address these challenges by enabling predictive inference from viral genomes and proteins while reducing dependence on sequence similarity. In this Review, we discuss representative advances in AI for virus discovery, taxonomic classification and functional annotation, prediction of host range and zoonotic potential, and efforts toward forecasting emerging variants. These advances are transforming viral genomics from a largely descriptive discipline into one with increasing predictive capability. We also critically assess the major challenges that constrain current approaches, including the availability of high-quality and representative datasets, rigorous model evaluation, biological interpretability and responsible governance for increasingly capable AI models.

Artificial Intelligence↗

PubMed2026

AI-Driven Multi-Omics Integration of Synthetic Colon Adenocarcinoma for Cluster-Guided PROTAC Candidate Design Targeting KRASG12D.

Colorectal cancer is a leading cause of cancer death, yet its molecular heterogeneity remains poorly translated into individualized treatment. We present a reproducible artificial intelligence (AI) framework that integrates multi-omics benchmarking, sample-level drug prioritization, E3 ubiquitin ligase selection, and shape-anchored Proteolysis Targeting Chimera (PROTAC) design for KRASG12D in colon adenocarcinoma (COAD). A controlled synthetic benchmark comprising 425 tumor and 41 simulated normal profiles, parameterized to match The Cancer Genome Atlas (TCGA) distributions, was used for pipeline verification. Among sixteen methods, the Balanced Latent Integration with Stability Selection (BLISS) model achieved the highest silhouette width (0.86) and competitive agreement (Adjusted Rand Index, ARI, 0.90). The pipeline was validated on real data: a TCGA COAD cohort (186 tumors) with independent Consensus Molecular Subtype (CMS) labels and a CPTAC cohort (104 tumors). Integration modestly recovered CMS (ARI 0.28), and stage, not molecular cluster, drove survival (log-rank p = 0.005 versus 0.81). Sample-level prioritization differed from cluster-level ranking in 82.6% of profiles, below chance (p < 0.0001), without indicating efficacy. Candidate NOVEL00489 showed a good MM-GBSA estimate, matching the reference ASP3082. Compounds are computational candidates requiring experimental validation. This establishes a transparent benchmark for in silico degrader generation in precision oncology.

Humans↗

PubMed2026

Psychological consequences of AI-assisted training and the buffering role of mindfulness.

The integration of artificial intelligence (AI) into athletic training is accelerating, yet its psychological implications for athletes remain insufficiently understood. Drawing on the transactional model of stress and the stress-buffering framework of mindfulness, this study examined whether mindfulness training can mitigate adverse psychological responses associated with AI-assisted training. Using a randomized controlled factorial design, 160 collegiate athletes were assigned to AI-assisted training or standard training, with or without concurrent mindfulness intervention, and assessed at baseline, week 4, and week 8. Athletes exposed to AI-assisted training without psychological support exhibited increases in perceived stress and AI dependence over time. In contrast, these stress increases were substantially attenuated when mindfulness training was implemented alongside AI-assisted training. A significant AI × Mindfulness × Time interaction emerged for perceived stress at post-intervention, and difference-in-differences analyses corroborated a robust buffering effect. Mediation analyses further indicated that mindfulness training reduced stress partially through enhancing mindful awareness; a three-wave cross-lagged analysis showed that mindful awareness and stress were reciprocally related over time, with the hypothesized awareness-to-stress pathway remaining robust. Together, these findings suggest that AI-assisted training introduces a distinct form of evaluative pressure, and that mindfulness training may serve as an effective psychological buffer during the adoption of continuous algorithmic performance evaluation systems.

Humans↗

PubMed2026

A Sentiment-Based Comparison of AI- and Physician-Generated Empathic Statements in Palliative Care.

CONTEXT: Empathic communication promotes trust in patient-provider relationships. As healthcare integrates artificial intelligence (AI) into patient communication, we have yet to understand how these models' communication compares to that of physicians. OBJECTIVES: Our primary objectives were to examine patient preferences for AI-generated vs. palliative care physician-generated empathic statements addressing fear and anxiety around cancer treatment, and to analyze associations between linguistic features and patient preferences. METHODS: We conducted a secondary analysis of the PALL-AI trial, a randomized controlled survey comparing cancer patients' preferences of AI- to physician-generated empathic statements. Physicians and AI were provided the same prompt with a maximum sentence length. Patient preferences for each statement were measured in blinded surveys. We analyzed sentiment of the statements using the Valence Aware Dictionary and Sentiment Reasoner (VADER) and the National Research Council Canada (NRC) Emotion Lexicon. We evaluated associations between sentiment scores and patient preferences using Spearman's correlation coefficients. RESULTS: A total of 105 patients completed blinded surveys, preferring the AI-generated statement 72.4% of the time. VADER sentiment analysis showed all three AI statements displayed positive sentiment, while all three physician statements displayed negative sentiment. Controlling for statement length, AI statements used twice as many positive words as human statements. However, they contained a similar number of negative words. Of the eight NRC emotions, "trust" and "joy" demonstrated the strongest correlations with patient preference. CONCLUSION: Patients preferred AI-generated statements around cancer care over those from palliative care physicians when standardized for prompt and statement length. Analysis shows AI-generated statements contain more positive language which may be the factor driving patient preference toward AI.

Humans↗

PubMed2026

Applications of quantum AI in brain disorder diagnosis: A systematic review.

BACKGROUND AND OBJECTIVE: Brain disorder diagnosis and prediction remain challenging because neuroimaging, electrophysiological, behavioral, and multimodal data are high-dimensional, noisy, heterogeneous, and limited by small clinical cohorts. This systematic review synthesised applications of quantum artificial intelligence (QAI) for brain disorder diagnosis, prediction, detection, and monitoring. METHODS: Following PRISMA guidelines, studies published from 2016 to 13 January 2026 were retrieved from Scopus, Web of Science, and IEEE Xplore. After screening, 36 studies met the eligibility criteria and were qualitatively analysed according to disorder category, data modality, QAI method, implementation setting, validation strategy, and performance. RESULTS: At the broader disease-group level, neurodegenerative disorders were the most frequently investigated, followed by mental health and psychiatric disorders. At the individual level, Parkinson's disease and schizophrenia were the leading applications, followed by depression, anxiety, Alzheimer's disease, and stress-related tasks. MRI-based modalities were the most frequently used data source, followed by multimodal data and EEG. Methodologically, primary QAI approaches were dominated by quantum neural and QDL architectures, followed by quantum-inspired optimization or feature-selection methods and quantum-kernel/conventional QML classifiers. Qiskit/IBM Quantum and PennyLane were the most frequently reported quantum software frameworks. However, most studies relied on simulators, classical quantum-inspired implementations, or unclear implementation settings, with limited real-hardware evaluation. CONCLUSIONS: QAI shows emerging potential for brain disorder analysis, particularly through hybrid quantum-classical learning, quantum neural architectures, quantum-kernel methods, and quantum-inspired optimization. Nevertheless, current evidence remains preliminary and requires larger datasets, subject-level and external validation, fair classical benchmarking, noise-resilient circuits, real quantum hardware evaluation, explainability, and clinical validation.

Humans↗

PubMed2003

Putting the Predictive Toxicology Challenge into perspective: reflections on the results.

MOTIVATION: Chemical carcinogenicity is of primary interest, because it drives much of the current regulatory actions regarding new and existing chemicals, and its experimental determination involves time-consuming and expensive animal testing. Both academia and private companies are actively trying to develop SAR and QSAR models. This paper reviews the new Predictive Toxicology Challenge (PTC) results, by putting them into the context of previous attempts. RESULTS: A marked dependency of the prediction ability of the different algorithms on the training sets was observed, pointing to a still insufficient coverage of the chemical carcinogens 'universe'. A theoretical treatment of the possible developments of the Artificial Intelligence approaches is sketched.

Algorithms↗

PubMed1990

Computerized neuropsychological assessment of cognitive functioning in children with epilepsy.

The value of a range of computer-aided tests in the neuropsychological assessment was investigated in 94-177 children with epilepsy, aged 8-18 years, compared with 68-161 controls in the same age group. Children from the age of 8 years could cope with rather complex tests in a wide range of functions: reaction time measurements, motor speed, information processing, and memory. The speed of performance tended to increase with age in both groups, with differences in information processing becoming apparent from the age of 12 years. The precise control of stimulus and response required to define the mainly minor differences between the epilepsy and control groups can only be fulfilled by computerized testing, which should undergo further refinement including voice and language recognition, followed by artificial intelligence.

Adolescent↗

PubMed2026

From fear to empowerment: the impact of employees AI awareness on workplace well-being - a new insight from the JD-R model.

PURPOSE: The primary purpose of the study was to explore the impact of health workers' awareness of artificial intelligence (AI) on their workplace well-being, addressing a critical gap in the literature. By examining this relationship through the lens of the Job demands-resources (JD-R) model, the study aimed to provide insights into how health workers' perceptions of AI integration in their jobs and careers could influence their informal learning behaviour and, consequently, their overall well-being in the workplace. The study's findings could inform strategies for supporting healthcare workers during technological transformations. DESIGN/METHODOLOGY/APPROACH: The study employed a quantitative research design using a survey methodology to collect data from 420 health workers across 10 hospitals in Ghana that have adopted AI technologies. The study was analysed using OLS and structural equation modelling. FINDINGS: The study findings revealed that health workers' AI awareness positively impacts their informal learning behaviour at the workplace. Again, informal learning behaviour positively impacts health workers' workplace well-being. Moreover, informal learning behaviour mediates the relationship between health workers' AI awareness and workplace wellbeing. Furthermore, employee learning orientation was found to strengthen the effect of AI awareness on informal learning behaviour. RESEARCH LIMITATIONS/IMPLICATIONS: While the study provides valuable insights, it is important to acknowledge its limitations. The study was conducted in a specific context (Ghanaian hospitals adopting AI), which may limit the generalizability of the findings to other healthcare settings or industries. Self-reported data from the questionnaires may be subject to response biases, and the study did not account for potential confounding factors that could influence the relationships between the variables. PRACTICAL IMPLICATIONS: The study offers practical implications for healthcare organizations navigating the digital transformation era. By understanding the positive impact of health workers' AI awareness on their informal learning behaviour and well-being, organizations can prioritize initiatives that foster a learning-oriented culture and provide opportunities for informal learning. This could include implementing mentorship programs, encouraging knowledge-sharing among employees and offering training and development resources to help workers adapt to AI-driven changes. Additionally, the findings highlight the importance of promoting employee learning orientation, which can enhance the effectiveness of such initiatives. ORIGINALITY/VALUE: The study contributes to the existing literature by addressing a relatively unexplored area - the impact of AI awareness on healthcare workers' well-being. While previous research has focused on the potential job displacement effects of AI, this study takes a unique perspective by examining how health workers' perceptions of AI integration can shape their informal learning behaviour and, subsequently, their workplace well-being. By drawing on the JD-R model and incorporating employee learning orientation as a moderator, the study offers a novel theoretical framework for understanding the implications of AI adoption in healthcare organizations.

Humans↗

PubMed2006

On the legacy of W.S. McCulloch.

In this paper, we review McCulloch's legacy, from his early work in neurophysiology, and its relationship to his philosophical quest for an 'experimental epistemology' to his role in the cybernetics movement during the 1940s and 1950s and his contributions to the development of computer science and communication theory. There are three parts in chronological sequence. First, the period up to his work at Yale University with Dusser de Barenne, where he concentrated on the experimental study of the functional organization of sensory cortex. Second, the time of his Psychiatric Chair at the University of Chicago and the organization of the Macy Foundation Conferences. To this period corresponds the genesis and publication of the most influential and quoted work by McCulloch and Pitts: A Logical Calculus of the Ideas Immanent in Neurons Activity. Third, the period of his research activity at the Massachussetts Institute of Technology where he, Lettvin, Maturana and Pitts produced epochmaking papers on epistemological neurophysiology, the modelling of the reticular formation and other work with da Fonseca and Moreno-Díaz. We finally refer to the International Conference that took place in McCulloch's memory at the 25th anniversary of his death. Our main conclusion is that McCulloch's writings are still a source of inspiration from neurophysiology to artificial intelligence and robotics.

Artificial Intelligence↗

PubMed2003

Recent developments in broadly applicable structure-biodegradability relationships.

Biodegradation is one of the most important processes influencing concentration of a chemical substance after its release to the environment. It is the main process for removal of many chemicals from the environment and therefore is an important factor in risk assessments. This article reviews available methods and models for predicting biodegradability of organic chemicals from structure. The first section of the article briefly discusses current needs for biodegradability estimation methods related to new and existing chemicals and in the context of multimedia exposure models. Following sections include biodegradation test methods and endpoints used in modeling, with special attention given to the Japanese Ministry of International Trade and Industry test; a primer on modeling, describing the various approaches that have been used in the structure/biodegradability relationship work, and contrasting statistical and mechanistic approaches; and recent developments in structure/biodegradability relationships, divided into group contribution, chemometric, and artificial intelligence approaches.

Artificial Intelligence↗

PubMed1990

[Cognitive processes and neuronal networks].

It is clear that computers are but a poor brain models: the nervous system has many "processors" (neurons) in parallel, whereas von Neuman's machines work sequentially on a single processor. In complex systems, emergent properties cannot be inferred from the behaviour of single elements. Anthills display collective "meaningful" moves, while each ant seems to obey local interactions only. Likewise, large parallel networks of processing elements elicit emergent properties. Like brains, some of them are self-organizing systems. In large parallel processing networks, each unit performs an elementary computation: adding inputs from other units. Large nets display surprising spontaneous computational abilities: associative memories, classes, generalizations may be seen as emergent properties of the network. Symbols are dynamical entities, whose handing is driven by local interactions of activation/inhibition of related representations. In such models, representations (memories) are distributed in the whole network, as stable configurations. Indeed, the basic properties of representation in connectionist models seem closer to human mental objects than the classic Artificial Intelligence concepts. Connectionist models have been used in many fields, namely simulations of real neural networks, pattern recognition and artificial vision, speech recognition, language understanding and knowledge representation, problem solving... Connectionist models have been thus used in neurobiology as well as cognition. One basic structure seems indeed able to account for a range of cognitive functions, from perception to problem solving and high level cognitive tasks. Nevertheless studies about "pathological" networks are yet rare, still an open field... We explore some of these fields.

Artificial Intelligence↗

PubMed2003

Temporal consistency checking in clinical guidelines acquisition and execution: the GLARE's approach.

GLARE (GuideLine Acquisition, Representation and Execution) is a domain-independent system for the acquisition, representation and execution of clinical guidelines. Temporal constraints play an important role within clinical guidelines (e.g. to specify therapies). The treatment of such constraints is one of the distinguishing features of GLARE. During acquisition, GLARE supports (i) the representation and (ii) the check of the consistency of the temporal constraints. Moreover, it (iii) automatically checks that the times of execution of specific actions respect the general temporal constraints described in the guideline. Such a treatment of temporal constraints involves the extension of various Artificial Intelligence techniques.

Algorithms↗

PubMed1992

On a common structure of intelligence in biological and technical systems.

This is a survey of the general structure of an electronic computer-implemented, operation-oriented system, designed by the authors, which uses artificial intelligence mechanisms and is intended for the control of technical objects that function both in predictable and random environments. The system is based on processing knowledge, which is stored in a hierarchically arranged Knowledge Bank, and program mechanisms for adapting to and interacting with the External and Internal Worlds. The system has distributed program mechanisms, which are 'designed' with a constant structure. It is independent of the purpose and environment of the system operation and the specific features of the controlled object. None of the program mechanisms are concentrated in any program module. They are distributed in many modules, and therefore there is no single module responsible for the execution of a particular external function. The system is structured into separate program modules by internal procedures. The conceptual organisation of the Knowledge Base presupposes that the framework is structured according to functional, semantic and tier indications, i.e. the structured description of knowledge, by the system, of the external environment and its possible behaviour in it. The possibility of multiple use of the same elements of the lower tiers of the Knowledge Base by higher-tier elements makes the proposed Knowledge Base very efficient. If the cerebrum is considered at a structural level, there appears to be an amazing similarity between the structure and the mechanisms of the above system and the structure of the cerebral cortex, as suggested previously by Edelman and Mountcastle. Each mechanism of the cerebral cortex structure has a structural analogue in the described system.(ABSTRACT TRUNCATED AT 250 WORDS)

Algorithms↗