The proportion of lung cancers affecting individuals who have never smoked is growing, with these cancers being prone to harbor mutations in the EGFR gene. Little is known about risk factors and prognostic indicators for EGFR-mutant cancers, with current research limited by the scarcity of datasets integrating genomic, clinical, and environmental data. We created the Meyer Cancer Center Molecularly Enhanced Lung Cancer Database (MCC-MELD), including lung cancer cases from a large catchment area in New York City. We identified cases through linkage to our institution's cancer registry and a clinician-initiated, manually curated database. We linked all cases to the electronic health record and in-house tumor genomic testing results. We used natural language processing (NLP) to extract unstructured genomic testing results and detailed smoking history. We linked geocoded addresses to detailed area-level measures. MCC-MELD contains 9,573 patients with lung cancer diagnosed from 1988 to 2024, of whom 20% were non-Hispanic Asian, 14% were non-Hispanic Black, and 8% were Hispanic. We identified 1,092 (11.4%) EGFR-mutant cancers, with NLP identifying 397 cases not identified by structured data. NLP showed high accuracy in ascertaining EGFR status (97%) and quantitative smoking history variables (90%-98%). Never smokers made up 16% of the cases in MCC-MELD. MCC-MELD is an NLP-enhanced database containing clinical information, genomic testing results, and linkages to area-level data for patients with lung cancer from a diverse urban setting. This resource can facilitate studies on lung cancer risk factors, treatment patterns, and outcomes by EGFR and other driver mutation status.

Modelling phylogeny in 16S rRNA gene sequencing datasets using string-based kernels.

Detecting Bicuspid Aortic Valve From Echocardiographic Reports Using Natural Language Processing: A Veterans Affairs Study.

A scalable natural language processing framework for drug repurposing in chemotherapy-induced adverse events from clinical narrative records.

Increasing timely colonoscopy surveillance for patients with high-risk colorectal polyps: Protocol for a cluster randomized trial.

Feelings behind words: A systematic review on how effective IS NLP-based assessment for mental health diagnosis in human studies.

Stabilized neural ordinary differential equation for text classification in natural language processing

WhyMedQA: Enhanced biomedical why question answering using transfer learning approach.

Enhancing healthcare worker mental health via artificial intelligence-driven work process improvements: a scoping review.

Natural language processing and text mining in transportation: Current status, challenges, and future roadmap

There is a growing proliferation of artificial intelligence (AI) in most spheres and sectors of contemporary society including social work. This article reports a survey of South Asian social workers' views on AI and social work including application domains, usefulness, risks and challenges, training needs, and future of the profession. The majority of respondents have suggested that social workers need training on machine learning, reinforcement learning, and natural language processing. A high proportion proposed that AI will redefine the profession's future through multisource data synthesis on client lifeworld contexts, analysis of macro- and organizational-level data for intervention, multiple domains of practical use, and AI-powered decision support systems to recommend interventions. They recommended having digital ethics committees and diverse stakeholder groups to review AI protocols and suggest modifications in case of algorithmic bias. They also highlighted the need for training sessions on the use of AI to ensure its responsible use in social work practice.

Cardiovascular diseases remain the leading cause of morbidity and mortality worldwide, exerting a substantial burden on healthcare systems. Their management requires multidisciplinary approaches, continuity of care, and advanced monitoring tools. Artificial intelligence (AI) has recently emerged as a transformative resource, owing to its ability to analyze large, heterogeneous datasets and generate accurate predictive models. Techniques such as machine learning, deep learning, and natural language processing, combined with multimodal data (electronic health records, imaging, wearable devices, sensors), can enable earlier diagnosis, dynamic risk stratification, and personalized therapies. Furthermore, the integration of AI with telemedicine and digital therapeutics provides new opportunities for remote monitoring, clinical decision support, and patient empowerment, with significant potential to improve clinical outcomes, optimize healthcare resources, and reduce hospitalizations. However, challenges remain, including algorithmic bias, lack of interpretability, ethical and legal concerns, and the need for adequate training of healthcare professionals. The recent adoption of the European AI Act establishes stricter regulatory standards to ensure safety and transparency, though it may slow down large-scale implementation. In conclusion, AI represents a pivotal innovation in cardiovascular medicine, provided it is embedded into validated clinical pathways, supported by scientific evidence, and embraced by clinicians. The future of digital cardiology will rely on the ability to develop predictive, personalized, and patient-centered healthcare models.

Knowledge distillation and teacher-student learning in medical imaging: Comprehensive overview, pivotal role, and future directions.

Toward Integrated Clinical-Computational Nuclear Medicine.

Zero-shot medical image classification via large multimodal models and knowledge graphs-driven processing.

Exploring environmental sustainability of artificial intelligence in radiology: A scoping review.

This case study examines the implementation and impact of the "COIL en Clave ODS" program, focusing on the integration of artificial intelligence (AI) to enhance Collaborative Online International Learning (COIL) initiatives aligned with the Sustainable Development Goals (SDGs) in Ibero-American higher education. Developed by STAR Argentina, the program supported educators from Latin America, the Caribbean, and Spain in co-designing intercultural projects grounded in sustainability. Through AI-driven tools such as natural language processing (NLP) and interactive galleries, the initiative expanded global visibility, improved collaboration, and demonstrated the transformative potential of merging digital innovation with pedagogical reform.

Detecting psychosis via natural language processing of social media posts: potentials and pitfalls.

Assessment of a Zero-Shot Large Language Model in Measuring Documented Goals-of-Care Discussions.

Sustainable electronic trauma registry with NLP/ML-enhanced ICD-10 classification: 13-year insights from violence-heavy KwaZulu-Natal, South Africa.