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PubMed2026

Development and Crossover Evaluation of an Artificial Intelligence-Assisted System for Solid Pancreatic Lesion Detection and Pancreatic Parenchyma Recognition in Endoscopic Ultrasonography (With Video).

BACKGROUND AND STUDY AIMS: Pancreatobiliary endoscopic ultrasonography (EUS) is technically demanding, and supervised training opportunities are limited. We developed an artificial intelligence (AI) overlay system for detecting solid pancreatic lesions (SPL) and recognizing pancreatic parenchyma (PP) and evaluated its effect on reader performance. PATIENTS AND METHODS: Across six centers, two deep learning-based models were trained using expert-annotated EUS frames. We then conducted a randomized, two-sequence, two-period crossover reader study in which eight endosonographers (five novices and three experts) interpreted image sets with and without AI assistance. The primary endpoint was superiority of sensitivity for SPL detection among novices; key secondary endpoints included specificity and PP recognition. RESULTS: From 118 patients, 120 SPL-positive/negative image sets and 160 PP-positive/negative image sets were constructed. Among novices, AI assistance improved SPL detection sensitivity (88.7% vs. 76.8%, p < 0.001) and accuracy (86.4% vs. 78.7%), while specificity met the predefined noninferiority criterion (84.2% vs. 80.5%, p < 0.001). For PP recognition, sensitivity increased numerically (86.3% vs. 83.3%) but did not meet the predefined superiority criterion (p = 0.095); specificity met the noninferiority criterion (87.8% vs. 81.0%), and accuracy increased from 82.1% to 87.0%. Among experts, sensitivity was maintained for both tasks, whereas specificity increased with AI assistance. CONCLUSIONS: AI assistance improved SPL detection among novice endosonographers. For PP recognition, sensitivity increased without reaching statistical superiority, whereas specificity met the predefined noninferiority criterion. These findings support a potential adjunctive role for AI in EUS interpretation.

Humans↗

PubMed2026

Artificial intelligence for anticancer drug discovery from natural products of macroalgae and sponges: A systematic review.

Marine natural products (MNPs) from macroalgae and marine sponges have inspired clinically important anticancer agents, including the cytarabine pharmacophore and the eribulin scaffold, while cyanobacterial dolastatin chemistry supplies the auristatin payloads of several marine-inspired antibody-drug conjugates (ADCs) such as brentuximab vedotin. Artificial intelligence (AI) methods, encompassing both classical machine learning (ML) with hand-engineered features and modern deep learning (DL) with many-layered neural networks, are increasingly supporting key decisions in natural-product anticancer drug discovery, including bioactivity prediction, target identification, absorption, distribution, metabolism, excretion and toxicity (ADMET) filtering, generative analogue design, and the selection of preclinical candidates. DL architectures relevant to this field include graph neural networks, transformer-based molecular generators, diffusion models for protein-ligand docking, and convolutional networks for mass spectrometry, while classical ML contributes interpretable fingerprint-based bioactivity models and molecular networking for dereplication. This review follows a systematic literature review methodology to organize the landscape of AI methods now applied to MNP anticancer discovery, distinguishing ML and DL approaches where relevant, situating them within the chemical context of macroalgal and sponge-derived oncology leads, and critically examining published case studies, including validation level (computational, in vitro, in vivo, clinical). The principal bottleneck for medical translation has shifted partly from algorithmic capability toward data infrastructure and experimental validation. Sparse, heterogeneous, and taxonomically biased bioactivity records limit what current models can learn and reduce the reliability of AI-prioritized candidates entering the preclinical pipeline. A roadmap is proposed that prioritizes open MNP-specific benchmarks, symbiont-aware modeling, and active learning loops with synthesizability and ADMET constraints. These AI workflows may accelerate the prioritization of marine-derived anticancer leads and support earlier, more evidence-based translational decisions in oncology drug development.

Biological Products↗

PubMed2025

The application of AI-driven and engineered intratumoral microbes in cancer therapy.

BACKGROUND: Although investigations of the intratumoral microbiota date back thousands of years, breakthrough transformations have only recently been achieved through high-throughput sequencing and multiomic technologies. These advances have revealed diverse and tumor type-specific microbial communities that drive carcinogenesis via immunomodulation, metabolic reprogramming, and genomic instability. Current cornerstones of cancer therapies-including chemotherapy, radiotherapy, immunotherapy, and targeted therapy-are limited by systemic toxicity, localized tissue damage, drug resistance, and low patient response rates. These constraints underscore the urgent need for more effective and precise therapeutic strategies. MAIN BODY: This review comprehensively integrates artificial intelligence (AI) technologies into the characterization of the intratumoral microbiota, facilitating the development of novel computational pipelines for mapping microbe-host crosstalk. We systematically summarize recent advances in engineered microbial therapeutics, including bacteria designed for targeted antitumor activity and engineered microorganisms that enable the localized delivery of therapeutic agents. Furthermore, this review critically evaluates the safety profiles of microbiota-based interventions and discusses key challenges in clinical translation. CONCLUSIONS: By combining cutting-edge computational technologies, biological research, and clinical insights, this review aims to bridge the gap between microbiome science and oncological practice, pioneering innovative strategies for microbiota-guided diagnostics and personalized cancer therapy.

Humans↗

PubMed1997

Does diesel exhaust cause human lung cancer?

Recent reviews of epidemiological evidence on the relation between exposure to diesel exhaust (DE) and lung cancer risk have reached conflicting conclusions, ranging from belief that there is sufficient evidence to conclude that DE is a human lung carcinogen (California EPA, 1994) to conclusions that there is inadequate evidence to support a causal association between DE and human lung cancer (Muscat and Wynder, 1995). Individual studies also conflict, with both increases and decreases in relative risks of lung cancer mortality being cited with 95% statistical confidence. On balance, reports of elevated risk outnumber reports of reduced risk. This paper reexamines the evidence linking DE exposures to lung cancer risk. After briefly reviewing animal data and biological mechanisms, it surveys the relevant epidemiological literature and examines possible explanations for the discrepancies. These explanations emphasize the distinction between statistical associations, which have been found in many studies, and causal associations, which appear not to have been established. Methodological threats to valid causal inference are identified and new approaches for controlling them are proposed using recent techniques from artificial intelligence (AI) and computational statistics. These threats have not been adequately controlled for in previous epidemiological studies. They provide plausible noncausal explanations for the reported increases in relative risks, making it impossible to infer causality between DE exposure and lung cancer risk from these studies. A key contribution is to show how recent techniques developed in the AI-and-statistics literature can help clarify the causal interpretation of complex multivariate data sets used in epidemiological risk assessments. Applied to the key study of Garshick et al. (1988), these methods show that DE concentration has no positive causal association with occupational lung cancer mortality risk.

Animals↗

PubMed1994

International Federation of Clinical Chemistry. Use of artificial intelligence in analytical systems for the clinical laboratory. IFCC Committee on Analytical Systems.

The incorporation of information-processing technology into analytical systems in the form of standard computing software has recently been advanced by the introduction of artificial intelligence (AI) both as expert systems and as neural networks. This paper considers the role of software in system operation, control and automation and attempts to define intelligence. AI is characterized by its ability to deal with incomplete and imprecise information and to accumulate knowledge. Expert systems, building on standard computing techniques, depend heavily on the domain experts and knowledge engineers that have programmed them to represent the real world. Neural networks are intended to emulate the pattern-recognition and parallel-processing capabilities of the human brain and are taught rather than programmed. The future may lie in a combination of the recognition ability of the neural network and the rationalization capability of the expert system. In the second part of this paper, examples are given of applications of AI in stand-alone systems for knowledge engineering and medical diagnosis and in embedded systems for failure detection, image analysis, user interfacing, natural language processing, robotics and machine learning, as related to clinical laboratories. It is concluded that AI constitutes a collective form of intellectual property and that there is a need for better documentation, evaluation and regulation of the systems already being used widely in clinical laboratories.

Artificial Intelligence↗

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↗

PubMed2025

Worldwide Innovative Network (WIN) Consortium in Personalized Cancer Medicine: Bringing next-generation precision oncology to patients.

The human genome project ushered in a genomic medicine era that was largely unimaginable three decades ago. Discoveries of druggable cancer drivers enabled biomarker-driven gene- and immune-targeted therapy and transformed cancer treatment. Minimizing treatment not expected to benefit, and toxicity-including financial and time-are important goals of modern oncology. The Worldwide Innovative Network (WIN) Consortium in Personalized Cancer Medicine founded by Drs. John Mendelsohn and Thomas Tursz provided a vision for innovation, collaboration and global impact in precision oncology. Through pursuit of transcriptomic signatures, artificial intelligence (AI) algorithms, global precision cancer medicine clinical trials and input from an international Molecular Tumor Board (MTB), WIN has led the way in demonstrating patient benefit from precision-therapeutics through N-of-1 molecularly-driven studies. WIN Next-Generation Precision Oncology (WINGPO) trials are being developed in the neoadjuvant, adjuvant or metastatic settings, incorporate real-world data, digital pathology, and advanced algorithms to guide MTB prioritization of therapy combinations for a diverse global population. WIN has pursued combinations that target multiple drivers/hallmarks of cancer in individual patients. WIN continues to be impactful through collaboration with industry, government, sponsors, funders, academic and community centers, patient advocates, and other stakeholders to tackle challenges including drug access, costs, regulatory barriers, and patient support. WIN's collaborative next generation of precision oncology trials will guide treatment selection for patients with advanced cancers through MTB and AI algorithms based on serial liquid and tissue biopsies and exploratory omics including transcriptomics, proteomics, metabolomics and functional precision medicine. Our vision is to accelerate the future of precision oncology care.

Humans↗

PubMedDate not supplied

Medical informatics and clinical decision making: the science and the pragmatics.

There are important scientific and pragmatic synergies between the medical decision making field and the emerging discipline of medical informatics. In the 1970s, the field of medicine forced clinically oriented artificial intelligence (AI) researchers to develop ways to manage explicit statements of uncertainty in expert systems. Classic probability theory was considered and discussed, but it tended to be abandoned because of complexities that limited its use. In medical AI systems, uncertainty was handled by a variety of ad hoc models that simulated probabilistic considerations. To illustrate the scientific interactions between the fields, the author describes recent work in his laboratory that has attempted to show that formal normative models based on probability and decision theory can be practically melded with AI methods to deliver effective advisory tools. In addition, the practical needs of decision makers and health policy planners are increasingly necessitating collaborative efforts to develop a computing and communications infrastructure for the decision making and informatics communities. This point is illustrated with an example drawn from outcomes management research.

Artificial Intelligence↗

PubMed2025

Triage and workflow optimization with artificial intelligence in pediatric imaging.

Artificial intelligence (AI) is being increasingly utilized in various aspects by the radiology department. With an ever-increasing burden on the healthcare system, particularly in emergency units, the need to incorporate AI in patient triage and workflow optimization cannot be overstated. Machine learning (ML)-based algorithms form the core of AI-based software, aiding healthcare professionals at nearly every step in delivering appropriate patient care. Regarding the radiology section of the hospital, AI-based algorithms have proven exceptionally useful in assisting radiologists and technicians with image acquisition. From accurate clinical referrals to scheduling computed tomography/magnetic resonance imaging scan appointments, from ensuring the lowest radiation exposure to offering timely follow-up reminders, ML-based software has indeed revolutionized the concept of modern image acquisition, especially in the pediatric radiology section. Although the implementation of these algorithms is swift, several technical challenges and the limited availability of pediatric datasets preclude their widespread use. The utility of multimodal pediatric datasets, which combine imaging, genomics, and clinical data, for comprehensive AI triage models can help AI systems evolve toward greater adaptability and integration, resulting in enhanced efficiency, reduced turnaround times, and improved patient outcomes in pediatric radiology departments in the future. In this article, we highlight and review the utility of AI and machine learning-based algorithms in efficiently aiding triage and streamlining the workflow in the pediatric radiology section, thereby ensuring an overall improvement in the departmental workflow.

Triage↗

PubMed2026

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

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

Artificial intelligence↗

PubMedDate not supplied

RGSS-ID: an approach to new radiologic reporting system.

RGSS-ID is a developmental computer system that applies artificial intelligence (AI) methods to a reporting system. The representation scheme called Generalized Finding Representation (GFR) is proposed to bridge the gap between natural language expressions in the radiology report and AI methods. The entry process of RGSS-ID is made mainly by selecting items; our system allows a radiologist to compose a sentence which can be completely parsed by the computer. Further RGSS-ID encodes findings into the expression corresponding to GFR, and stores this expression into the knowledge data base. The final printed report is made in the natural language.

Artificial Intelligence↗

PubMed1989

Predicate calculus, artificial intelligence, and workers' compensation.

Application of principles of predicate calculus (PC) and artificial intelligence (AI) search methods to occupational medicine can meet several goals. First, they can improve understanding of the diagnostic process and recognition of the sources of uncertainty in knowledge and in case specific information. Second, PC provides a rational means of resolving differences in conclusion based upon the same premises. Third, understanding of these principles allows separation of knowledge (facts) from the process by which they are used and therefore facilitates development of AI-based expert systems. Application of PC to recognizing causation of pulmonary fibrosis is demonstrated in this paper, providing a method that can be generalized to other problems in occupational medicine. Application of PC and understanding of AI search routines may be particularly applicable to workers' compensation where explicit statement of rational and inferential process is necessary. This approach is useful in the diagnosis of occupational lung disease and may be particularly valuable in workers' compensation considerations, wherein explicit statement of rationale is needed.

Artificial Intelligence↗

PubMed2026

Artificial intelligence in molecular diagnostics for pandemic preparedness.

INTRODUCTION: Molecular diagnostics focusing on the detection and analysis of nucleic acids are indispensable tools for early pathogen identification, transmission monitoring, and genomic surveillance during pandemics. Recent technological advances have broadened the diagnostic landscape, incorporating PCR-based methods, isothermal amplification, high-CRISPR-based amplification detection, and sequencing. Despite their diagnostic potential, widespread implementation remains limited by high validation costs, time and logistical constraints, the need for specialized professional knowledge, and a lack of adaptability in resource-limited settings. Artificial intelligence (AI) is increasingly recognized as a promising but challenging approach, offering tools that streamline assay development, automate data interpretation, and optimize real-time diagnostic performance. AREAS COVERED: This review introduces recently published AI tools with potential to enhance the in-silico design validation process of oligonucleotides for molecular assays. These cover tools for initial assay design and optimization to validation and continuous assay updates. The limitations, including concerns regarding data accuracy, the lack of transparency in data processing ('black box' models), and unresolved licensing and regulatory issues, are highlighted for each tool and as expert opinion. EXPERT OPINION: Collectively, these challenges currently confine most AI-based approaches to research settings and prevent their routine implementation in clinical molecular diagnostics. Their widespread adoption depends on addressing remaining technical, regulatory, and practical challenges.

Humans↗

PubMed2026

The future of pediatric vesicoureteral reflux management.

BACKGROUND AND OBJECTIVE: Vesicoureteral reflux (VUR) is a common condition in pediatric urology, yet important uncertainties persist regarding risk stratification, imaging strategies, and prevention of long-term renal damage. Emerging technologies may help address these challenges. This review provides a forward-looking overview of recent advances in artificial intelligence (AI) and immunomodulation that may influence future management of pediatric VUR. METHODS: A forward-looking literature review was performed using the PubMed database (January 2000-March 2025), focusing on studies addressing AI, immunomodulation, or vaccination in the context of VUR and urinary tract infections. Criteria of inclusion were the relevance to pediatric VUR, the novelty of the proposed concept, the potential clinical implications and, for the AI literature, the existence of a clinical evaluation of the algorithm on a dataset from patients. KEY FINDINGS AND LIMITATIONS: AI-based models show promising performance in supporting clinical decision-making, including prediction of the need for voiding cystourethrography, automated grading of VUR, estimation of recurrent urinary tract infection risk and prediction of chemoprophylaxis. These tools may facilitate more individualized diagnostic and therapeutic strategies, although current evidence is largely retrospective and requires prospective validation. Immunization and immunomodulatory approaches aim to reduce infection burden and modulate inflammatory pathways associated with renal scarring. While early experimental and adult clinical data are encouraging, pediatric-specific evidence remains limited, and clinical applicability in children with VUR is not yet established. CONCLUSION: Artificial intelligence and immunologically targeted strategies represent complementary, emerging approaches that may contribute to more personalized management of pediatric VUR. At present, both should be regarded as exploratory tools whose clinical impact will depend on further validation and appropriately designed pediatric studies.

Humans↗

PubMed2026

Emulated trial of artificial intelligence use and subsequent depressive outcomes in a survey of US adults.

BACKGROUND: Generative artificial intelligence (AI) use has been suggested to have adverse mental health consequences but a causal relationship has not been examined. OBJECTIVE: To simulate a randomised controlled trial of AI use in a work, school or personal context by applying target trial emulation to multiple waves of data from a nationally representative survey. METHODS: We conducted a target trial emulation using non-probability survey data from three waves of a nationally representative survey conducted between 18 June 2024 and 8 January 2025. Participants aged ≥18 years reported generative AI use frequency at baseline. High-frequency use was defined as multiple times per week or more. The primary outcome was depressive symptom severity measured using the Patient Health Questionnaire 9-item (PHQ-9) at follow-up. Generalised causal forests assessed heterogeneity of treatment effects. FINDINGS: Among 19 099 participants assessed at baseline, 2862 (15.0%) reported AI use at least multiple times per week. A subset of 3109 (16.3%) returned for follow-up. In the primary weighted analysis, high-frequency use was not significantly associated with change in PHQ-9 score at follow-up (mean difference -0.18, 95% CI -0.94 to 0.59; p=0.65). Multiple sensitivity analyses using alternate outcome definitions also did not identify significant causal effects. Generalised causal forests yielded no significant evidence of heterogeneity of effect (p=0.81). CONCLUSIONS: In an emulated randomised trial among US adults, generative AI use was not associated with subsequent depressive symptoms. This result does not support the premise that AI use causes greater depressive symptoms, although adverse outcomes among vulnerable individuals cannot be excluded. CLINICAL IMPLICATIONS: AI use is unlikely to cause increased depressive symptoms among most US adults. Continued monitoring should clarify potential risks among vulnerable populations.

Humans↗

PubMed2005

Artificial intelligence and robotics in high throughput post-genomics.

The shift of post-genomics towards a systems approach has offered an ever-increasing role for artificial intelligence (AI) and robotics. Many disciplines (e.g. engineering, robotics, computer science) bear on the problem of automating the different stages involved in post-genomic research with a view to developing quality assured high-dimensional data. We review some of the latest contributions of AI and robotics to this end and note the limitations arising from the current independent, exploratory way in which specific solutions are being presented for specific problems without regard to how these could be eventually integrated into one comprehensible integrated intelligent system.

Artificial Intelligence↗

PubMed2003

An advanced artificial intelligence tool for menu design.

The computer-assisted menu design still remains a difficult task. Usually knowledge that aids in menu design by a computer is hard-coded and because of that a computerised menu planner cannot handle the menu design problem for an unanticipated client. To address this problem we developed a menu design tool, MIKAS (menu construction using incremental knowledge acquisition system), an artificial intelligence system that allows the incremental development of a knowledge-base for menu design. We allow an incremental knowledge acquisition process in which the expert is only required to provide hints to the system in the context of actual problem instances during menu design using menus stored in a so-called Case Base. Our system incorporates Case-Based Reasoning (CBR), an Artificial Intelligence (AI) technique developed to mimic human problem solving behaviour. Ripple Down Rules (RDR) are a proven technique for the acquisition of classification knowledge from expert directly while they are using the system, which complement CBR in a very fruitful way. This combination allows the incremental improvement of the menu design system while it is already in routine use. We believe MIKAS allows better dietary practice by leveraging a dietitian's skills and expertise. As such MIKAS has the potential to be helpful for any institution where dietary advice is practised.

Artificial Intelligence↗

PubMed2025

Cardiovascular Complications Are Increased in Inflammatory Bowel Disease: A Path Toward Achievement of a Personalized Risk Estimation.

Background/Objectives: The global burden of inflammatory bowel diseases (IBDs) continues to rise, with up to 50% of patients experiencing extraintestinal manifestations. Cardiovascular diseases (CVDs) are of particular concern, ranking as the second leading cause of mortality in this population. Despite a comparatively lower prevalence of traditional cardiovascular (CV) risk factors, the persistent inflammatory milieu and immune dysregulation inherent to IBD may contribute to heightened CVD risk. In this study, following a review of the current literature, an ongoing prospective trial designed to clarify CV risk profiles in IBD patients is detailed. Methods: A cohort of patients with IBD is being enrolled for comprehensive baseline evaluation of CV risk factors, lifestyle metrics, and disease characteristics. The incidence of major adverse cardiovascular events (MACEs) will be tracked and contrasted with a gender- and age-matched non-IBD cohort over a 2-year follow-up period. In cases of MACE occurrence, a multi-omics analysis-including genomic, proteomic, transcriptomic, and microbiome profiling-will be performed, along with a parallel evaluation in matched IBD controls without MACE. An artificial intelligence (AI) framework will support the analysis of this complex dataset. Results: To date, over 150 patients with IBD have been enrolled, and detailed phenotypic data and biological samples have been collected. Conclusions: We aim to introduce an IBD-specific correction factor for existing CV risk scores upon study completion. This is particularly relevant for individuals under 40 years of age, who are often inadequately assessed by current risk stratification models.

Crohn’s disease↗