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PubMed2026

Effectiveness of an AI-based home exercise app for rehabilitation of rotator cuff-related shoulder pain: A randomized controlled trial.

BACKGROUND: Rotator cuff-related shoulder pain contributes to disability and healthcare use. Although therapeutic exercise is first-line treatment, limited supervision and adherence may reduce its effectiveness; digital rehabilitation with real-time feedback may address these limitations. OBJECTIVES: To evaluate the effectiveness of adding a digital rehabilitation program to standard physiotherapy on pain, function, fear-avoidance beliefs, and healthcare utilization. DESIGN: Single-center, assessor-blinded, randomized controlled trial with two parallel groups. METHOD: Forty-six adults (mean age 59 years) with rotator cuff-related shoulder pain were randomized to 12 weeks of conventional physiotherapy or physiotherapy plus an AI-based digital rehabilitation program using computer vision for real-time feedback and performance monitoring. Outcomes were assessed at baseline and at 2, 4, and 12 weeks. Pain intensity (NPRS) was primary outcome; secondary outcomes included upper limb function (QuickDASH), fear-avoidance beliefs (FABQ), and post-intervention healthcare utilization. Analyses followed an intention-to-treat approach. RESULTS: Pain reduction exceeded the MCID (1.3) at 4 and 12 weeks. Between-group differences favoured the intervention at Weeks 2 and 4 (MD -0.7; 95% CI -1.13 to -0.14 and MD -1.01; 95% CI -1.8 to -0.2, respectively). Upper limb function improved more at Week 4 (MD -7.3; 95% CI -12.3 to -2.2). FABQ scores decreased more at Week 12 (MD -7.6; 95% CI -14 to -0.5). Fewer participants in the experimental group required post-intervention healthcare (3 vs 10; p = 0.02). CONCLUSION: Adding AI-based home exercise app to conventional treatment improve pain and may improve function and reduce healthcare utilization in rotator cuff-related shoulder pain.

Humans↗
PubMed2026

Intervention Without Borders - an Automated Self-Guided AI-Enhanced Psychoeducation Intervention for Dementia Caregivers: Parallel-Group Randomized Waitlist-Controlled Trial.

OBJECTIVE: To examine whether a fully automated, self-guided intervention (PDC30) could improve caregiver well-being over a 1-month waitlist control in an international sample. DESIGN: Randomized waitlist-controlled trial. SETTING: Web-based platform accessible globally. PARTICIPANTS: 441 individuals responded to study promotion on the internet, of whom 274 from 43 countries met the study criteria and were randomized. Eligible participants were adults providing ≥10 care hours weekly to community-dwelling relatives with dementia, scoring ≥5 on Patient Health Questionnaire-9 (PHQ-9), and without recent caregiver intervention. INTERVENTION: Available 24/7, PDC30 is a self-guided, automated intervention consisting of a Guidebook, an AI-powered counseling chatbot, and interactive applications for cognitive-behavioral techniques, relaxation, and caregiver-recipient bonding. MEASUREMENTS: At baseline and follow-ups at 1, 2, and 3 months, depression was assessed by PHQ-9. Secondary outcomes were measured with validated brief versions of anxiety, burden, and positive gains. RESULTS: Intent-to-treat analysis using mixed-effects regression showed treatment x time2 effects on all outcomes except anxiety. At 1-month follow-up, coinciding with exclusive access to PDC30, intervention caregivers showed significant improvements in depression (d = -0.37), burden (d = -0.34), and positive gains (d = 0.42). The differences mostly disappeared after control participants received the intervention, while improvements in both groups were sustained thereafter. Participants reported using the website several times weekly, were generally satisfied with it, and found the chatbot most helpful. CONCLUSIONS: The effects on depression and other outcomes were consistent with those observed for in-person programs, suggesting the viability of well-designed automated intervention. The study demonstrates the feasibility, acceptability, and potential global health impact of PDC30.

Humans↗
PubMed2026

Decoding the spatiotemporal patterns of food spoilage microbial communities: Integrating multi-omics and artificial intelligence to enable precision preservation.

In the global food supply chain, food wastage caused by spoilage has resulted in significant economic losses, food shortages, and environmental pressure. This process is fundamentally driven by the spatiotemporal dynamics of microbial communities. However, traditional research methods struggle to elucidate the complex mechanisms of spatial heterogeneity, interspecies interactions, and functional succession. This limits the development of effective preservation strategies. This review systematically reviews the cutting-edge progress of integrating multi-omics technologies and artificial intelligence (AI) to study food spoilage microbial communities, breaking through this bottleneck. We propose an intelligent theoretical framework that could potentially analyze microbial metabolic activities and predict dynamic shelf life if implemented. The conceptual framework integrates multidimensional data, including spatial metabolomics, temporal metatranscriptomics, single-cell transcriptomics, and longitudinal metagenomics. It can also be combined with AI models, such as graph neural networks. The article elaborates on the principles and applications of spatio-temporal monitoring technologies, such as nano secondary ion mass spectrometry, hyperspectral imaging, and the Internet of Things sensing. Through illustrative cases of typical perishable foods, it also explores how such a multi-omics - AI system might be applied to spoilage warning and precise intervention. Additionally, the article addresses the current challenges in data coverage, model generalization, and federated learning implementation. Then the research further explores emerging areas such as engineered probiotics, edge AI, and microfluidic sensing. These areas are targeted at transforming food preservation from an empirical control approach to a data-driven, precise regulatory framework. This transformation provides theoretical support and technical approaches for developing a smart, sustainable food preservation system.

Multiomics↗
PubMed2026

Privacy, security, and reliability risks of artificial intelligence in healthcare: a systematic review of empirical evidence.

BACKGROUND: Artificial intelligence (AI) is increasingly integrated into healthcare information systems, supporting clinical decision-making, imaging analysis, and predictive modeling. While these applications offer operational and clinical benefits, they also introduce emerging risks to patient privacy, data security, and system reliability. OBJECTIVE: To systematically review empirical evidence on privacy breaches, security vulnerabilities, and misuse associated with AI applications in healthcare settings. METHODS: PubMed, Embase, Web of Science, Scopus, IEEE Xplore, and ACM Digital Library were searched for empirical studies published between January 2015 and November 2025 that evaluated AI use or misuse in clinical diagnosis, treatment, or decision-making. Two reviewers independently screened studies and extracted data using a standardized form. Findings were synthesized narratively due to heterogeneity in study designs, AI methods, and reported outcomes. RESULTS: Of 7,285 records identified through database searches and 205 through citation screening, 22 empirical studies met the inclusion criteria, spanning multiple clinical domains and data modalities, predominantly medical imaging applications. Five recurring threat categories were identified: patient re-identification, membership inference, unauthorized access and adversarial exploitation, input manipulation, and misuse or overinterpretation of AI outputs. Across studies, AI models were shown to encode latent biometric signals across diverse data types, limiting the effectiveness of traditional anonymization and synthetic data approaches. Adversarial attacks and input manipulation were also shown to compromise diagnostic performance and system integrity. CONCLUSION: This systematic review provides empirical evidence suggesting that contemporary AI systems in healthcare introduce privacy and security risks that may challenge traditional assumptions about data protection. These findings underscore the need for privacy- and security-by-design approaches and governance frameworks that address risks across the AI lifecycle.

Humans↗
PubMed2025

Improving Community-Based Care for Adolescents with ADHD: a Randomized Controlled Trial of Artificial Intelligence-Assisted Fidelity Supports.

Cognitive-behavioral treatments (CBTs) for adolescents with ADHD demonstrate promise of long-term effects on outcome. However, their implementation in routine care community clinics faces barriers that impact quantity, efficiency, and quality of delivery, as well as client outcomes. This study is a randomized controlled trial designed to evaluate the impact of an AI-assisted service delivery model on therapist implementation of Supporting Teens' Autonomy Daily (STAND), a CBT blended with Motivational Interviewing (MI) for adolescents with ADHD. Adolescents with ADHD (N = 51), who were clients at three community mental health agencies, received treatment from 23 therapists. There was randomization of adolescents and therapists to AI-assisted or standard implementation supports. In addition to standard supports (i.e., training, standard facilitation resources, technical assistance, case supervision), AI-assisted support package included digitized facilitation resources housed in a clinical dashboard (Care4), feedback on content fidelity, and AI-generated feedback on MI implementation quality. The AI-assisted group was associated with more efficient treatment delivery and lower number of appointments attended by the adolescent. There was also a significant decrement in MI quality over time in the AI-assisted group compared to the standard support group. Feedback in focus groups indicated that therapists perceived a task-oriented mindset to be associated with receipt of the AI-assisted support package, leading therapists to prioritize efficiency over relational aspects of therapy. Following the results of this trial, a future, larger RCT should examine the impact of the AI-assisted implementation model on mental health outcomes and cost savings to organizations, third party payers, and clients. Trial registration number: NCT05135065; https://www.clinicaltrials.gov ; Registered September 2021.

Humans↗
PubMed2026

Artificial intelligence in genitourinary oncology: publication trends and systematic review.

OBJECTIVE: To conduct an analysis of publication trends and a systematic review of randomized controlled trials (RCTs) to characterize the current state of artificial intelligence (AI) use in genitourinary (GU) oncology, as AI has emerged as a transformative tool in healthcare with potential applications in diagnostics, treatment planning, and prognostication. METHODS: We searched the Medical Literature Analysis and Retrieval System Online (MEDLINE), Excerpta Medica dataBASE (EMBASE; Ovid), and Cumulative Index to Nursing and Allied Health Literature (CINAHL) Ultimate for studies related to AI and GU oncology, excluding non-English papers, non-human studies, review articles, and articles using AI solely for manuscript writing. Publication trends were analysed from 2013 to 2023 and categorized by study design and cancer type. RCTs were evaluated through systematic review using Covidence (Veritas Health Innovation Ltd, Melbourne, Victoria, Australia) for screening and data extraction. Two reviewers independently assessed all studies, with risk of bias (RoB) evaluated using the Cochrane RoB 2.0 tool. RESULTS: Of 2409 articles identified, 1220 met inclusion criteria. These included 962 retrospective articles, 175 prospective studies, 79 studies with combined retrospective/prospective methods, and four RCTs. Studies most commonly addressed prostate (n = 923), renal (n = 274), and urothelial (n = 194) cancers. Publications grew from 14 in 2013 to 362 in 2023, with substantial acceleration in 2019. Four RCTs were identified - one in urothelial cancer and three in prostate cancer. Two RCTs evaluated AI-based diagnostics, demonstrating improved performance over conventional methods; the remaining two RCTs evaluated AI in prognostication and treatment planning, showing improved gains in imaging interpretation and operational efficiency. RoB varied across studies, primarily related to randomisation and deviations from intended interventions. CONCLUSIONS: Artificial intelligence research in GU oncology has grown, although high-level evidence from RCTs remains limited. Existing trials underscore AI's promise in diagnostics, prognostication, and treatment planning, and the rapidly evolving nature of this field warrants continued prospective investigation.

Humans↗
PubMed2026

First insights into the role of evolutionary history in shaping venom composition of Vipera ammodytes.

Understanding intraspecific venom variation requires distinguishing the contributions of neutral population history from natural selection. This study aims to determine whether venom variation in Vipera ammodytes species complex is structured across eight phylogenetic groups. Despite a complex evolutionary history, venom composition did not differ among phylogenetic groups within the analytical framework used, suggesting that shared ancestry alone does not explain venom variation. Whether local adaptation to environmental conditions explains the observed variation remains an open question for future studies.

Animals↗
PubMed2026

Artificial intelligence-supported double reading in European population breast cancer screening: A systematic review and meta-analysis of prospective programs.

BACKGROUND: Most European population mammography screening programs rely on double reading with arbitration, a model that delivers mortality benefit but is increasingly challenged by radiologist workload, variable specificity, and interval cancers. Artificial intelligence (AI) is being evaluated to support or optimize these established European screening pathways. PURPOSE: To synthesize prospective or program-embedded evaluations of AI conducted within European-style population screening programs and to estimate exploratory program-level absolute risk differences (RDs) per 1000 examinations for cancer detection rate (CDR) and recall. MATERIALS AND METHODS: We performed a prespecified, focused evidence synthesis of three large studies embedded within routine population screening programs operating under European-relevant workflows: MASAI (randomized AI-supported risk triage within a national program), ScreenTrustCAD (prospective paired-reader evaluation with AI as an independent reader in a double-reading framework), and PRAIM (nationwide decision-referral implementation). Outcomes were harmonized as AI-control RDs per 1000 examinations. Random-effects pooling used Hartung-Knapp-Sidik-Jonkman models. For the paired-reader design, sensitivity analyses applied a Kish effective sample-size approach across plausible within-examination correlations (ρ = 0.3-0.8). Positive predictive value (PPV) and workflow/time outcomes were summarized descriptively. RESULTS: Across 597,419 examinations, the pooled CDR RD was +0.9 per 1000 (95% CI -0.0 to +1.8; I2 ≈ 12%), consistent with a modest directional increase with borderline statistical uncertainty. The pooled recall RD was -0.6 per 1000 (95% CI -3.1 to +2.1; I2 ≈ 41-43%), indicating no consistent recall increase across screening programs. Where reported, PPV was higher with AI-supported screening. Efficiency signals included 44.3% fewer total readings in MASAI and shorter reading times for AI-normal examinations in PRAIM; in PRAIM, a program-level safety-net mechanism recovered 204 cancers that would otherwise have been missed. CONCLUSION: In European population screening programs characterized by double reading and arbitration, prospective program-embedded evidence suggests that AI integration may yield a small absolute increase in cancer detection (≈1/1000) without a consistent increase in recall, alongside improved PPV and efficiency signals. These findings suggestAI primarily as a complementary reader within European screening workflows, with implementation requiring explicit quality assurance and monitoring of interval cancers and stage distribution.

Humans↗
PubMed2026

Effectiveness and usability of artificial intelligence-powered assistive technologies in Supporting daily activities of children with cerebral palsy: a systematic review.

BACKGROUND: Cerebral Palsy (CP) is the main cause of motor disabilities in childhood, necessitating innovative approaches to rehabilitation and assistive technology (AT). Simultaneously, artificial intelligence (AI) is increasingly being integrated into devices to create more adaptive, personalized, and effective AT. This systematic review aimed to evaluate the effectiveness and usability of AI-powered assistive technologies designed to support daily activities and rehabilitation in children with CP. MATERIALS AND METHODS: Five databases, including Scopus, Web of Science, PubMed, Embase, and IEEE Xplore, were systematically searched, and 23 articles were included in the final analysis. Articles were identified, selected, and categorized into emerging thematic areas based on the primary function and application of the technology. RESULTS: Five key thematic topics were identified: 1) AI-driven motor rehabilitation and gait training for functional mobility; 2) intelligent assessment and monitoring systems for clinical decision support; 3) AI-supported communication, social interaction, and intention recognition tools; 4) gamified and virtual reality-based interventions to enhance engagement and usability; and 5) smart assistive systems supporting daily living and independent mobility. The findings demonstrate a strong trend toward the application of AI technologies in personalized, engaging, and data-driven interventions for children with CP. However, the field is predominantly in the proof-of-concept stage, with limitations including small sample sizes, lack of long-term clinical validation, challenges in user-centered design, and usability for children with CP. CONCLUSION: AI-powered assistive technologies hold significant potential for transforming the care of children with CP by enabling highly personalized and engaging interventions. To actualize this potential, future work must realize that practical application remains challenging owing to limited clinical validation, technological integration, and usability barriers for children with CP. Future research must prioritize user-centered design and multidisciplinary collaboration to ensure that AI and robotic advancements improve the usability and quality of life for children with CP.

Humans↗
PubMed2026

Artificial intelligence-derived myocardial fibrosis on cardiac magnetic resonance for prognosis in cardiomyopathy: A systematic review of a sparse evidence base.

BACKGROUND: Myocardial fibrosis on cardiovascular magnetic resonance (CMR), assessed by late gadolinium enhancement (LGE) and parametric mapping, is an established predictor of adverse events in cardiomyopathy. We assessed whether artificial intelligence (AI) quantification of fibrosis adds independent prognostic value. METHODS: We searched six databases, a clinical-trials register, and a preprint server from inception to 13 June 2026. Eligible studies used AI to generate a fibrosis marker in adults with ischemic or nonischemic cardiomyopathy, with covariate-adjusted outcomes over ≥12 months. Risk of bias was assessed using PROBAST, PROBAST+AI, and QUIPS. Fewer than three comparable studies precluded meta-analysis; certainty was rated using GRADE. RESULTS: Of 448 records (381 after de-duplication), 18 full texts were reviewed and two included, one peer-reviewed and one preprint. In an ischemic-cardiomyopathy registry (Ghanbari et al.; n = 216 analytic, 26 events), AI-derived dense LGE scar predicted arrhythmic events (univariable hazard ratio [HR] 2.35, 95% CI 1.33-4.15), and AI-derived but not manual scar improved discrimination beyond guideline criteria (area under the curve 0.63 to 0.68; p = 0.02). In a nonischemic dilated-cardiomyopathy preprint (Kim et al.; n = 347, 119 events), automated extracellular volume ≥30% predicted cardiovascular death or heart-failure hospitalization (adjusted HR 2.00, 95% CI 1.32-3.03). Both were at high risk of bias, with data-derived thresholds and no external validation. CONCLUSIONS: Across only two studies, AI-derived fibrosis was independently associated with adverse cardiovascular events, but its added value over manual quantification remains unproven. Certainty was very low. The evidence base is sparse and not yet ready for clinical use.

Humans↗
PubMed2026

Effectiveness of artificial intelligence in nursing simulation education: A systematic review, meta-analysis and bibliometric visualization analysis.

OBJECTIVES: To synthesize the roles and core functions of AI in nursing simulation education for nursing students via systematic review, quantitatively evaluate its effects on students' knowledge and skill outcomes through meta-analysis, and map the research landscape and development trends of this field through bibliometric visualization analysis. DESIGN: Systematic review, meta-analysis and bibliometric visualization analysis. DATA SOURCES: Eight electronic databases: PubMed, Web of Science, MEDLINE, ERIC, Academic Search Complete, China National Knowledge Infrastructure (CNKI), Wanfang Database, VIP Chinese Science and Technology Journal Database (VIP) were employed to search studies from the time of construction to 16 December 2025. REVIEW METHODS: Studies meeting the inclusion criteria were screened. The revised Cochrane Risk of Bias tool (ROB 2) and Joanna Briggs Institute (JBI) critical appraisal checklists were used for quality assessment. Meta-analysis was performed with Review Manager 5.4, and bibliometric visualization analysis was conducted using VOSviewer 1.6.20 and Bibliometrix (based on R4.4.3). RESULTS: A total of 61 studies were included. AI primarily played two roles in nursing simulation education: peer-type new subject (n = 24) and direct mediator (n = 22). Meta-analysis showed that AI interventions significantly improved nursing students' knowledge (SMD = 1.49, 95% CI [0.55,2.43], p = 0.002) and skills (SMD = 0.66, 95% CI [0.02,1.31], p = 0.04). Bibliometric analysis identified that the United States of America and China were the two main contributing countries in this field, and the key motor themes included generative artificial intelligence, virtual patients, and geriatric care. CONCLUSIONS: AI exerts positive effects on nursing students' knowledge acquisition and skill enhancement in simulation education, with peer-type new subject and direct mediator as the dominant roles. Future research should focus on expanding AI applications in multi-specialty simulation scenarios, activating the data-driven value of machine learning, and strengthening international collaboration and standardization construction, so as to promote the sustainable development of AI-integrated nursing simulation education.

Humans↗
PubMed2026

Diagnostic performance of machine learning models for malignant and non-malignant pleural effusion: Systematic review and meta-analysis.

BACKGROUND: Accurately distinguishing malignant pleural effusion (MPE) from non-malignant pleural effusion is clinically important, but the generalisability and methodological quality of machine-learning (ML) models remain uncertain. METHODS: We searched eight databases to 23 April 2026. Diagnostic performance was pooled using random-effects and Reitsma bivariate models, and study quality was assessed using PROBAST+AI. RESULTS: Forty-two studies were included; 17 contributed to the AUC meta-analysis and 14 to the bivariate analysis. The pooled AUC was 0.90 (95 % CI 0.85-0.94; 95 % prediction interval 0.62-0.98), with sensitivity of 0.80 (95 % CI 0.77-0.83) and specificity of 0.87 (95 % CI 0.79-0.92). Only nine studies reported external, temporal or independent validation. Externally validated studies had a lower pooled AUC than studies without external validation (0.83 vs 0.92), with lower specificity observed in the two externally validated studies contributing sensitivity and specificity data. All 42 development assessments had high overall quality concerns, and all 42 model evaluations were judged at high risk of bias. CONCLUSIONS: ML models showed good apparent accuracy for distinguishing MPE from non-MPE, but the evidence was limited by substantial heterogeneity, high risk of bias and scarce external validation. The pooled estimates reflect the average performance of different selected models rather than the expected accuracy of a single clinical test. ML models should be regarded as adjuncts to existing diagnostic pathways until they are confirmed by rigorous multicentre prospective external validation and clinical-impact studies.

Humans↗
PubMed2026

The impact of artificial intelligence on critical thinking and clinical reasoning in health professions education: A systematic review and meta-analysis.

BACKGROUND: Critical thinking and clinical reasoning underpin healthcare professionals' ability to navigate uncertainties and deliver safe and effective care. With artificial intelligence (AI) advancement and growing adoption, AI-based educational tools are increasingly used to support these cognitive competencies' development. OBJECTIVE: To synthesize randomised and controlled clinical trials on AI-based educational tools in health professions education and examine their effects on critical thinking and clinical reasoning among health professions students. METHODS: Six electronic databases were searched from January 1, 2014 to July 28, 2025 was reviewed: PubMed, Cochrane Central Register of Controlled Trials, CINAHL, Scopus, Embase and Web of Science. Two independent reviewers performed data extraction and quality assessment using standardized JBI checklists. The GRADE approach was used to assess the certainty of evidence. Studies were pooled via random-effects meta-analyses or narrative syntheses. RESULTS: Fourteen randomised controlled trials and seven controlled clinical trials were included (n = 21). Meta-analyses revealed small to medium effect sizes for the surrogate clinical reasoning outcomes of performance-based assessment scores (SMD 0.68; 95% CI [0.38, 0.98], p-value = 0.00; I2 = 38%) and knowledge test scores (SMD 0.39; 95% CI [0.09, 0.69], p-value = 0.01; I2 = 79%). Critical thinking and clinical reasoning skills and dispositions were narratively synthesized, with majority of included studies favouring AI-based interventions but the evidence had low to very low certainty. CONCLUSION: AI-based educational interventions may improve critical thinking and clinical reasoning among health profession students, but the evidence is very uncertain. This review offers preliminary insights but does not allow identification of optimal interventions or discipline-specific recommendations due to small sample sizes and substantial intervention heterogeneity. Further research is required to draw definitive conclusions. PROTOCOL REGISTRATION: CRD42025634074.

Humans↗
PubMed2026

Phase 1 Study Evaluating Gefurulimab Pharmacokinetics and Safety Following Delivery Via Autoinjector or Prefilled Syringe With Needle Safety Device in Healthy Adults.

PURPOSE: Gefurulimab, a novel dual-binding nanobody targeting complement component 5 (C5), is in clinical development for anti-acetylcholine receptor antibody-positive generalized myasthenia gravis. Gefurulimab has a low molecular weight, enabling subcutaneous (SC) self-administration by autoinjector (AI) or prefilled syringe with needle safety device (PFS-SD). We compared gefurulimab pharmacokinetic (PK) exposure and safety in healthy adults following a single SC dose administered by AI versus PFS-SD. METHODS: In this phase 1, open-label, randomized, parallel-group study (NCT06208488), healthy participants aged 18 to 65 years were stratified by weight and randomized equally to 1 of 6 combination groups of device and injection site (abdomen/thigh/upper arm). Participants received a single SC dose of gefurulimab on day 1 and were assessed throughout the 92-day evaluation period. Primary endpoints were PK parameters for each device: maximum observed concentration (Cmax) and area under the serum concentration-time curve (AUCinf, AUClast). PK across injection sites, pharmacodynamics, safety, immunogenicity, and device performance were also assessed. FINDINGS: Overall, 175 participants were randomized: AI (n = 87), PFS-SD (n = 88). Geometric least squares mean ratios (90% CI) comparing AI/PFS-SD for Cmax, AUCinf, and AUClast were 97.6% (94.5-100.8), 99.6% (96.1-103.3), and 98.8% (95.2‒102.6), respectively. Secondary analyses found no meaningful differences in PK parameters across injection sites. Serum-free C5 concentrations over time, treatment-emergent adverse event (TEAE) profiles, and antidrug antibody responses were similar between cohorts. Most TEAEs were mild; none led to study discontinuation. IMPLICATIONS: SC administration of gefurulimab by AI and PFS-SD was well tolerated with comparable exposure, meeting bioequivalence criteria.

Humans↗
PubMed2026

Pharmacogenomic and drug interactions risk in cardio-oncology: A precision medicine perspective for India.

Cardio-oncology patients may face complex treatment regimens due to the concurrent existence of cancer and cardiovascular disease, leading to a considerable polypharmacy burden. This significantly increases the prospect of drug-drug interactions (DDIs) and gene-drug interactions. The majority of these interactions arise from comparable pharmacokinetic and pharmacological pathways associated with drug transporters and cytochrome P450 enzymes. The significance of pharmacogenomics in tailored treatment strategies are emphasised by the fact that genetic variability enhances individual differences in drug response, safety, and efficacy. This narrative review focus on the effects of key genetic polymorphisms (e.g., DPYD, CYP2C19, and CYP2C9) on the metabolism and efficacy of commonly prescribed anticancer and cardiovascular medications such as fluoropyrimidines, clopidogrel, and warfarin. In addition it explore the role of pharmacogenomic variants on drug-drug interactions within the field of cardio-oncology. The study ultimately emphasizes the necessity of precision medicine in India to address the genetic diversity and underrepresentation in global genomic databases. The absence of pharmacogenomic testing, infrastructural deficiencies, financial constraints, and insufficient clinical integration hinder the widespread use of this technology in India. The Genome India Project and other national initiatives establish the foundation for pharmacogenomic-guided therapy. Utilizing genetic data, together with artificial intelligence-based predictive tools, for clinical decision-making may enhance medication safety and yield optimal outcomes in Indian cardio-oncology patients.

Humans↗
PubMed2026

The future of precision oncology and artificial intelligence in Belgium: scenarios and policy responses.

PURPOSE: Precision medicine, also known as personalized medicine, enables the provision of tailored health services to patients. In the prevention, early detection, and treatment of cancers, precision medicine is highly promising, given the increasing use of genomic profiling for diagnosis and adapting therapies in several tumor types. Artificial Intelligence (AI) can support this process by analyzing vast amounts of relevant data. However, high-quality data and financial investments in the health system are essential for the implementation of precision medicine and AI solutions in routine cancer care. DESIGN/METHODOLOGY/APPROACH: Building on the quantitative outcomes of a foresight exercise published in another study, this article collects qualitative data to gain more detailed insights into the future of precision oncology in Belgium and discusses the role of AI in this field. It reports the results of a series of expert workshops, focusing on four hypothetical future scenarios that are centered around technological and economic issues that must be overcome for the widespread use of precision oncology in Belgium. FINDINGS: The study concludes that all four scenarios discussed in the workshops would require supportive policy measures in Belgium, which should go beyond mere technological and economic considerations, such as involving patient associations and the public in policy design or creating multi-disciplinary expert groups for precision medicine. ORIGINALITY/VALUE: To the best of our knowledge, this is the first study to employ foresight methodology to illustrate possible future scenarios, scrutinize feasible approaches for implementing precision oncology in Belgium, and discuss the use of AI in this context.

Belgium↗
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

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↗